WO2025245166A1 - Biomarkers for xenograft rejection - Google Patents

Biomarkers for xenograft rejection

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Publication number
WO2025245166A1
WO2025245166A1 PCT/US2025/030276 US2025030276W WO2025245166A1 WO 2025245166 A1 WO2025245166 A1 WO 2025245166A1 US 2025030276 W US2025030276 W US 2025030276W WO 2025245166 A1 WO2025245166 A1 WO 2025245166A1
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WIPO (PCT)
Prior art keywords
genes
subject
xenograft
hundred
expression levels
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PCT/US2025/030276
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French (fr)
Inventor
Brendan KEATING
Robert A. Montgomery
Jef Boeke
Eloi SCHMAUCH
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New York University NYU
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New York University NYU
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Publication of WO2025245166A1 publication Critical patent/WO2025245166A1/en
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P37/00—Drugs for immunological or allergic disorders
    • A61P37/02—Immunomodulators
    • A61P37/06—Immunosuppressants, e.g. drugs for graft rejection
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6881—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/24—Immunology or allergic disorders
    • G01N2800/245—Transplantation related diseases, e.g. graft versus host disease

Definitions

  • the present invention relates to methods of detecting a xenograft rejection in a subject, monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said methods comprising determination of expression levels of certain human or porcine genes and comparing expression levels of said genes.
  • Organ transplantation is a life-saving procedure for end-stage organ failure patients.
  • Xenotransplantation transplantation of organs across species
  • genetically engineered organs or tissues from different species has emerged as a promising solution to address this challenge (Wolbrom, et al., 2023).
  • Genetically modified pig organs offer several significant advantages for transplantation into humans and may help alleviate the current shortage of suitable organs (Cooper, et al., 2023; Wolbrom, et al., 2023).
  • the domesticated pig (Sus scrofa domesticus) has been identified as a suitable species due to its size/physiology similarities to human organs, quick maturation to near adult size in 6 months, and positive public perception (Cooper, et al., 2002; Elisseeff, et al., 2021).
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, 2 314164997v1 Attorney Docket No: 243735.000437 PRDM1, NUGGC, and SDC1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs); and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1, wherein the sample is a xenograft tissue biopsy; and 3 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1, wherein the sample is a xenograft tissue biopsy; and 4 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD3G, THEMIS, CD5, and CD6, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of CD8B and/or CD8A, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the gene(s) determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRG1, GZMK, CST7, and GZMH, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft 5 314164997v1 Attorney Docket No: 243735.000437 rejection in a subject, wherein the subject has received a xenograft transplantation said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD4, CD40LG, CCR2, CCR6, and DPP4, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: 6 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GZMA, PRF1, and FASLG, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes TOP2A, TYMS, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1, wherein the sample is a xenograft tissue biopsy; and 7 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MY
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, ST
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft 9 314164997v1 Attorney Docket No: 243735.000437 rejection in a subject, wherein the subject has received a xenograft transplantation said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSA
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A, wherein the sample is a xenograft tissue biopsy; and 10 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, 11 314164997v1 Attorney Docket No: 243735.000437 CD163, C3, V
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, M
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3,
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft 13 314164997v1 Attorney Docket No: 243735.000437 rejection in a subject, wherein the subject has received a xenograft transplantation said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10,
  • the method further comprises step c) (i) determining that the subject has a xenograft rejection or is at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are increased as compared to the control by 1.5-fold or more; or (ii) determining that the subject does not have a xenograft rejection or is not at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are decreased, not increased or increased by less than 1.5-fold as compared to the control.
  • the control is a predetermined value or a value determined from a sample taken from the subject before the xenograft transplantation.
  • the control may be a value determined from a sample taken from the subject at a quiescent timepoint post xenograft transplant where there are nonrejection tractors. 14 314164997v1 Attorney Docket No: 243735.000437 [0039]
  • the control is a predetermined value or a value determined from a sample taken from the xenograft before the xenograft transplantation.
  • the control may be a value determined from a sample taken from the xenograft at a quiescent timepoint post xenograft transplant where there are nonrejection tractors.
  • the method further comprises administering to the subject a treatment that targets plasma cells and/or a complement inhibitory agent, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection.
  • the method further comprises administering to the subject a treatment that targets T cells when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection.
  • the method further comprises administering to the subject a treatment that targets plasma cells, a treatment that targets T cells, a complement inhibitory agent, or a combination thereof, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection.
  • the treatment that targets plasma cells is plasmapheresis.
  • the treatment that targets T cells is rabbit anti-thymocyte globulin (rATG).
  • rATG rabbit anti-thymocyte globulin
  • the complement inhibitory agent is a C3 inhibitor.
  • the method further comprises administering an immunosuppressive agent to the subject when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection.
  • the subject has received a kidney, heart, lung, liver, bone marrow, pancreas, or islet cell transplantation from a porcine donor.
  • the expression levels are determined based on RNA expression, protein expression, epigenetic regulation, or a combination thereof.
  • the expression levels are determined using RNA sequencing, targeted RNA panel, a quantitative PCR assay, an antibody-based method, an epigenetic assay, a single-cell technology, a spatial transcriptomics technology, or a multiplexed approach combining RNA and protein measurements.
  • the antibody-based method is flow cytometry, immunohistochemistry, or enzyme-linked immunosorbent assay (ELISA). 15 314164997v1 Attorney Docket No: 243735.000437
  • the single-cell technology is single-cell RNA sequencing (scRNA-seq) or cellular indexing of transcriptomes and epitopes (CITE-seq).
  • the sample is collected from the subject 3 days after the xenograft transplantation.
  • the method comprises obtaining two or more samples from the subject at different time points after the xenograft transplantation and repeating the method for each sample.
  • a kit comprising: 1) one or more sets of probe nucleic acids useful for detecting two or more genes selected from: i. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD; ii.
  • porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A,
  • Figure 1 shows study design and sampling timeline.
  • left and right ventricle tissue samples were obtained 66 h post-transplantation for snRNA-seq and spatial transcriptomic analyses.
  • Peripheral blood samples were collected at 6-h intervals for comprehensive analysis, including metabolomic, proteomic, lipidomic, cytokine profiling, bulk 18 314164997v1 Attorney Docket No: 243735.000437 RNA-seq, scRNA-seq and flow cytometry.
  • tissue samples at 66 h were analyzed via scRNA-seq and bulk RNA-seq.
  • FIG. 1 For D1, blood samples at 64 h, 65 h and 65.5 h were exclusively collected.
  • the figure’s lower section illustrates the timeline of blood assay execution post-transplantation. The timing at which each assay from blood was carried out is indicated with a circle for sampling from both decedents and diamonds or triangles for sampling from only D1 or D2, respectively. Routine clinical measurements including INR, ALT and AST, in addition to arterial blood gases, comprehensive metabolic panels, complete blood counts, lactate dehydrogenase and troponin levels were measured via blood. This figure was created with BioRender. [0056] Figures 2A-H show transcriptomic signature of PBMCs following transplantation.
  • Figures 2A-C show single-cell RNA-seq derived proportion (percentages) of the indicated cell types at the indicated time points for D1 (left) and D2 (right) in all PBMC cell types (Figure 2A), T/NK cells subtypes (Figure 2B) and B cells (Figure 2C). Percentages are calculated based on the total number of cells in each sample.
  • Figures 2D-F show bulk RNA-seq expression of cell-type specific markers, to validate scRNA-seq based cell-type proportions (shown on the left for T/NK and B cells, in Figure 2D and Figure 2F, respectively).
  • RNA-seq min– max normalized expression (transcript per million (TPM), length normalized) from B and NK/T cell marker genes (Figures 18I-J) were averaged for each sample (shown on the right in Figure 2D and Figure 2F, respectively) and for marker genes of CD8 + and CD4 + T cells in Figure 2E.
  • Figure 2G shows validation of the proportions of different cell types using flow cytometry.
  • PBMCs were subdivided based on protein expression into NK cells (CD56 + ), CD4 + T cells (CD56 ⁇ CD3 + CD4 + CD8 ⁇ ), CD8 + T cells (CD56 ⁇ CD3 + CD8 + CD4 ⁇ ), CD19 + B cells (CD56 ⁇ CD3 ⁇ CD19 + ) and plasmablast B cells (CD56 ⁇ CD3 ⁇ CD19 + CD27 + CD38 + ).
  • Figure 2H shows the top 20 enriched pathways (GSEA) in D1 from the bulk RNA-seq DEA.
  • the gray scale depicts the normalized enrichment score (NES) indicating negative and positive scores.
  • FIGS. 3A-D show integration of transcriptomic, proteomic, metabolomic, lipidomic and cytokine analyses.
  • Figures 3A-B show integrative clustering of bulk omics and cytokines using fuzzy c-means clustering across all time points. Normalized multi-omics analyte levels revealed trends of upregulation from 42 h onward post-xenotransplant, particularly evident in D1 19 314164997v1 Attorney Docket No: 243735.000437 and correlated with clinical variables INR, AST and ALT in the time course.
  • FIG. 3A Shown are data for D1 ( Figure 3A) and D2 (Figure 3B).
  • the different omics are in greyscale according to the legend.
  • Figure 3C shows pathways that are significantly associated with the cluster of interest. The highlighted cluster was one of an optimal number of clusters based on identifying the minimum distance between cluster centroids during soft clustering. Hits pertains to the number of analytes from the cluster of interest that are involved in the significant pathway.
  • Figure 3D shows temporal distribution of cytokine levels (IL-6, IL-8, IL-10 and IL-13) (indicated by the line) and analyte upregulation in the cluster of interest, for both decedents.
  • Figures 4A-L show xenograft snRNA-seq analysis.
  • Figure 4A shows a cell-type 2D map of pig nuclei snRNA-seq data, showing all xenograft samples (both decedents) integrated with the publicly available (Andrijevic, et al., 2022) in vivo pig heart snRNA-seq data.
  • LEC lymphatic endothelial cells
  • MP macrophages/monocytes
  • DIV dividing cells
  • SC Schwann cells.
  • Figure 4B shows cell- type percentage distribution.
  • D1 LV/RV and D2 LV/RV denote the left and right ventricles in D1 and D2, respectively.
  • PMI times of 0 h, 1 h and 7 h indicate ischemia durations, as per the publicly available in vivo pig heart snRNA-seq data (Andrijevic, et al., 2022).
  • ‘ECMO’ and ‘OrganEx’ are reperfusion conditions within this dataset.
  • Figure 4C shows transcriptomic 2D map of human nuclei from the xenograft (snRNA-seq) by cell type.
  • Figure 4D shows human NK/T nuclei count distribution.
  • Figure 4E shows the number of significantly (Padj ⁇ 0.05) overexpressed (log fold change (FC) > 0) and underexpressed (logFC ⁇ 0) DEGs, comparing each condition to 0 h of ischemia.
  • DEA was performed using DESeq2 on Pseudobulk raw counts, calculated independently for each cell type per sample.
  • the logFC and adjusted P value (Wald test P value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output.
  • Figures 4F-G show enrichment of biological pathways in CMs ( Figure 4F) and FBs (Figure 4G) from DE results. The top pathways enriched in D1 xenograft versus control are shown, with enrichments calculated using GSEA of DESeq2 results.
  • Greyscale depicts the NES indicating negative and positive scores.
  • a white star denotes an FDR below 0.05 and the dot size denotes the negative logarithm of the FDR.
  • Figures 4H-J shows subclustering analysis of FBs (all samples integrated).
  • Figure 4H shows a two-dimension map of FBs, sorted by their clusters.
  • Figure 4I shows cell cycle score distribution by subclusters.
  • Figure 4J shows proportion distribution for subclusters FB-4 and FB-6. Percentages based on total 20 314164997v1 Attorney Docket No: 243735.000437 number of FBs in each sample.
  • Figure 4K shows GSEA of cluster FB-4 marker genes.
  • Figure 4L shows differential strength interaction analysis using CellChat cell–cell communication analysis.
  • Figures 5A-M show spatial transcriptomics analysis that highlights vascular remodeling and ischemia.
  • Figure 5A shows spatial transcriptomic slide, mapped with spots corresponding to arterial signal.
  • Clustering analysis was performed on the spatial transcriptomic data, based solely on expression information. Spatial clusters expressing endothelial and vascular markers (clusters 5, 7, 8 and 9) are indicated in the left ventricle of D1 (left) and the right ventricle of D2 (right). Each slide has two technical replicates (A and B). D1-LV-A, D1, left ventricle, replicate A; D2-RV-A, D2, right ventricle, replicate A. Scale bar, 1 mm. Figure 5B shows a close up of the histology of the left ventricle of D1 (corresponding to the area bounded by the black square in Figure 5A, left). Scale bar, 200 ⁇ m.
  • Figure 5C shows a close up of the histology of the right ventricle of D2 (corresponding to the area bounded by the black square in Figure 5A, right). Scale bar, 200 ⁇ m.
  • Figures 5D-E show spatial transcriptomics data after deconvolution, based on the snRNA-seq signal for SMCs (Figure 5D) and FBs (Figure 5E). Scale bar, 1 mm.
  • Figure 5F shows expression of selected marker genes of cluster 7, across all spatial clusters.
  • Figure 5G shows expression of selected marker genes of cluster 7, across all spatial transcriptomics samples.
  • Figure 5H shows cell–cell interaction analysis of the IL-1 signaling network across cell types (based on snRNA-seq data), in D1-LV.
  • Figure 5I shows analysis of IL33 signaling between cell types in D1-LV (based on snRNA-seq) data.
  • Figure 5J shows H&E staining of pig xenograft endomyocardial biopsies from D1 showing ‘lifting’ of endothelium (white arrow), with few inflammatory cells present (black and grey arrow). Scale bar, 1 mm.
  • Figure 5K shows EM micrograph from a biopsy of the xenograft from D1, showing endothelial cell swelling (white arrow) and sarcolemma disruption (grey arrow). Scale bar, 5 ⁇ m.
  • Figure 5L shows DE of hypoxia- and damage-associated genes based on snRNA-seq (pseudobulk) data in FBs, CMs and VECs.
  • Figure 5M shows expression of the genes shown in Figure 5L in SMCs, across conditions. For dot-plots, dot size reflects the percentage of nuclei in each group with gene expression above zero. Dot saturation displays each group’s mean gene expression, min–max scaled.
  • Figures 6A-6J show PCXD and vascular remodeling in the xenograft.
  • Figure 6A shows DE of PCXD-related genes in snRNA-seq data for VECs, CMs, FBs and MPs.
  • Dot size 21 314164997v1 Attorney Docket No: 243735.000437 reflects the ⁇ log10(FDR) from pseudobulk DESeq results and the saturation reflects log2FC. Significance (Padj ⁇ 0.05) is denoted by a black star. The logFC and adjusted P value (Wald test P value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output. Genes labeled as PCXDplus were previously described (Byrne, et al., 2011) as being overexpressed in PCXD, whereas those labeled as PCXDminus were underexpressed.
  • Figure 6B shows top enrichments in D1 for VECs from the pseudobulk-based DEA.
  • Figures 6C-F show subclustering analysis of VECs (all samples integrated).
  • Figure 6C shows low-dimensional embedding representation of VECs, sorted by their subclusters.
  • Figure 6D shows vascular subtype markers.
  • Figure 6E shows proportion distribution of subclusters VEC-4 and VEC-9 across samples.
  • Figure 6F shows GSEA of cluster VEC-4 marker genes.
  • Figures 6G-J show subclustering analysis of PERs and SMCs (all samples integrated).
  • Figure 6G shows low-dimensional embedding representation of SMC–PER subclusters.
  • Figure 6H shows cell cycle score distribution across clusters.
  • Figure 6I shows proportion distribution of subclusters SMC–PER-4 and SMC–PER-6 across samples.
  • Figure 6J shows GSEA of cluster SMC–PER-4 marker genes.
  • Figures 7A-7L show detailed PBMC scRNA-seq data analysis. For dot-plots, dot size reflects the percentage of cells in each group with gene expression above zero. Dot saturation displays each group's mean gene expression, min-max scaled.
  • Figure 7A shows low dimension embedding representation of the PBMC scRNA-seq data, sorted by cell types. HSC-CD34+: Hematopoietic stem cells.
  • Figure 7B shows expression distribution of selected marker genes of the main cell types, basis for their identification.
  • Figure 7C shows low dimension embedding representation of the T/NK cells, sorted by subtype.
  • NK Natural Killer cells
  • CD8 T CD8 + T cells
  • CD4 T CD4 + T cells
  • T reg T-regulatory cells.
  • Figure 7D shows expression distribution of selected marker genes of the subtype, basis for their identification.
  • Figures 7E-F show further dissection of CD8 T ( Figure 7E) and CD4 T ( Figure 7F) cells.
  • Figure 7G shows low dimension embedding representation of the B cells, sorted by subtype.
  • Figure 7H shows expression distribution of selected marker genes of the subtype, basis for their identification.
  • Figure 7I shows proportion differences across time points, for both decedents, in selected cell types and subtypes (labeled on each plot).
  • Figures 7J-K show genes selected as highly specific for the B cell (Figure 7J) and T/NK ( Figure 7K) population, used for bulk RNA-seq proportion validation in 22 314164997v1 Attorney Docket No: 243735.000437 Figure 1.
  • Figure 7L shows a summary of DEG analysis from PBMC scRNA-seq. Number of overexpressed genes (left, LFC > 0 and padj ⁇ 0.05) and underexpressed genes (right, LFC ⁇ 0 and padj ⁇ 0.05).
  • FIGS. 8A-D show multi-omics DE integration. Comparative temporal DEA of individual omics analyses. Bulk transcriptomics, proteomics, lipidomics and metabolomics for blood samples of two pig heart to human xenotransplantations spanning 26 time points are illustrated.
  • the early phase encompasses the 6 hr and 12 hr post-transplant time points, the mid- phase includes 18 hr to 36 hr post-transplant, while the late phase comprises all time points beyond 36 hr.
  • Figure 8A shows transcriptomics. DEA analysis using DESeq2 is limited to genes whose average expression exceeds 4 counts across samples. Genes with significant expression changes (FDR ⁇ 0.05) are shown in dark grey. Only the top 10 most significant genes were labeled due to space limitation. The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini– Hochberg method) were obtained directly from the DESeq2 DEA output.
  • Figure 8B shows proteomics.
  • Proteins were present in more than half of the samples, yielding a total of 895 proteins. Proteins with significant changes (FDR ⁇ 0.05) are shown in dark grey.
  • Figure 8C shows metabolomics. DEA was performed for 459 metabolites. Metabolites with significant changes (FDR ⁇ 0.05) are shown in dark grey.
  • Figure 8D shows lipidomics. The analysis included 720 lipids. Lipids showing significant changes (FDR ⁇ 0.05) are shown in dark grey. In the metabolomics and lipidomics panels, only the top features are highlighted with the molecule names. In all panels, molecules without significant changes are represented in light gray.
  • Figures 9A-B show intersection between clinical measurements and multi-omics integration.
  • Figure 9A shows overlap of clinical blood markers (Lactate, Troponin, Fibrinogen, Ferritin, SBP, BNP, DBP, IL-2R) and multi-omics integration-based cluster of analytes upregulated in D1. The blood marker level is shown in bold line. For each marker, decedent 1 measures are shown on the top graph, decedent 2 on the bottom.
  • Figure 9B shows cardiac 23 314164997v1 Attorney Docket No: 243735.000437 output, cardiac index measurements and vasopressor dose (bottom graphs) put in perspective with the multi-omics integration-based cluster of analytes upregulated in D1 (top graph). Decedent 1 is shown on the left and decedent 2 on the right.
  • Figures 10A-I show xenograft snRNA-seq based gene expression dissection.
  • Figures 10A-B show top marker genes (Wilcoxon rank-sum) for human nuclei ( Figure 10A) and pig nuclei ( Figure 10B) in the xenograft snRNA-seq data.
  • Dot size reflects the percentage of nuclei in each group with gene expression above zero. Dot saturation displays each group's mean gene expression, min-max scaled.
  • Figure 10C shows cell-type proportions for pig nuclei.
  • Figure 10D shows cell-type proportion of human nuclei.
  • Figure 10E shows human T / NK subtypes markers expression within xenograft T/NK human population, across samples.
  • Figure 10F shows NK/T cell activity markers expression across samples.
  • Figures 10G-I show expression distribution of lead genes in D1 which contributed to the Reactome Interleukin Signaling enrichment in CM ( Figure 10G), NIK to Non-canonical NF-kB signaling in CM ( Figure 10H), and Bioplanet 2019 DNA replication in FB ( Figure 10I).
  • FIGS. 11A-H show spatial transcriptomics composition.
  • Figure 11A shows spatial transcriptomic slide mapped with each spatial cluster, in all samples. Scale bar: 1mm.
  • Figures 11B-C show spatial transcriptomics cell type deconvolution (Cell2location) signal, min–max scaled, across Spatial clusters ( Figure 11B), Visium samples (Figure 11C), and their combination (Figure 11D).
  • Figure 11E shows cluster proportion distribution across samples.
  • Figure 11F shows spatial distribution of IL1RL1 expression, represented by the grey scale (log norm expression). Scale bar: 1mm.
  • Figure 11G shows spatial distribution of IL33 expression, represented by the grey scale (log norm expression). Scale bar: 1mm.
  • Figure 11H shows snRNA-seq based cell–cell communication inference results for IL33 signaling.
  • Figures 12A-J show xenograft tissue imaging.
  • Figures 12A-D show H&E staining of Pig Xenograft Endomyocardial Biopsies from decedent 1 ( Figures 12A-B) and decedent 2 ( Figures 12C-D). Scale bar: 1mm.
  • Figure 12A shows contraction band necrosis, with interstitial edema and endothelial swelling of small interstitial capillary.
  • Figure 12B shows endothelial swelling of intramyocardial muscular artery and detached endothelial cells with perivascular 24 314164997v1 Attorney Docket No: 243735.000437 edema.
  • Figure 12C shows endothelial swelling in the intramyocardial muscular artery.
  • Figure 12D shows endothelial swelling and lifting of small interstitial capillary.
  • Figures 12E-K show electron microscopy (EM) images of the xenograft samples.
  • Figure 12E shows electron microscopy image of D2 transplanted xenograft. Scale bar: 5 ⁇ m.
  • Figure 12F shows EM image of D1 transplanted xenograft.
  • Ultrastructural changes in the capillary endothelial cells are characterized by endothelial swelling with narrowing of the capillary lumen and enlarged vacuoles in the cytoplasm. There is perivascular edema resulting in separation of the collagen fibrils.
  • the myocytes have an intact sarcolemma; however, the cytoplasm shows decrease to loss of myofibrils under the sarcolemma.
  • Scale bar 5 ⁇ m.
  • Figure 12G shows D1: accumulation of mitochondria (grey arrow) as a result of degenerated myofibrils, widened disorganized Z-lines (black arrow) in degenerating myofibrils, and swollen interstitial capillary endothelial cell (white arrow).
  • Scale bar 10 ⁇ m.
  • Figure 12H shows D1: degenerating myofibrils (black arrow), swollen endothelial cells in intramyocardial capillary (grey arrow), and sarcolemma disruption (white arrow). Scale bar: 5 ⁇ m.
  • Figure 12I shows D1: endothelial cell swelling with near collapse in an intramyocardial capillary lumen. Scale bar: 5 ⁇ m.
  • Figure 12J shows D2: endothelial swelling (white arrow). Scale bar: 5 ⁇ m.
  • Figures 13A-J show graft PCXD and hypoxia signal.
  • Figure 13A shows expression of genes of interest and hypoxia related genes across Visium samples (within spots from clusters 7 and 8).
  • Figure 13B shows top enrichments for Visium cluster 7 markers (from GSEA on Wilcoxon rank-sum based marker discovery). Saturation depicts the NES, indicating negative and positive scores. When the false discovery rate (FDR) is below 0.05, numbers are displayed, denoting the negative logarithm of the FDR.
  • Figure 13C shows top enrichments for D1-LV-A sample, from Visium data (from GSEA on Wilcoxon rank-sum based DE, at sample level, each sample vs. the others together).
  • Figures 13D-E show hypoxia and damage-associated genes expression changes in snRNA-seq.
  • Figures 13F-J show expression of genes associated with PCXD in the spatial transcriptomics and snRNA-seq data. Dot size reflects the percentage of spots in each group with gene expression above zero. Dot saturation displays each group’s mean gene expression, min–max scaled. Genes labeled as PCXDplus were previously described (Byrne, et al., 2011) as being overexpressed in PCXD, while PCXDminus as underexpressed. Figure 13F shows expression distribution across spatial 25 314164997v1 Attorney Docket No: 243735.000437 clusters.
  • Figure 13G shows expression distribution within all spots, between samples.
  • Figure 13H shows expression distribution within vascular spatial clusters (only spots of cluster 8 and 7 are included), between samples.
  • Figure 13I shows expression distribution across cell types (snRNA-seq).
  • Figure 13J shows expression distribution across conditions (snRNA-seq).
  • Figures 14A-C show further dissection of vascular remodeling in xenografts.
  • Figure 14A shows top enrichments in D1 for PER from pseudobulk based DE analysis.
  • Figure 14B shows top enrichments for SMC in D1 left ventricle.
  • GSEA was performed from Wilcoxon rank-sum test DE analysis at the nuclei level (each condition vs.
  • Non-ischemic in vivo pig cardiac tissue within snRNA-seq, selecting for SMC.
  • Saturation depicts the NES, indicating negative and positive scores.
  • a white star denotes a false discovery rate (FDR) is below 0.05, while dot size denotes the negative logarithm of the FDR.
  • Figure 14C shows expression distribution of lead genes in D1 which contributed to the MSigDB Hallmark 2020 EMT enrichment (top), MSigDB Myc Targets V1 enrichment (middle) and MSigDB TNF-alpha Signaling via NF-kB (bottom), all in VEC.
  • Dot size reflects the -log10(FDR) from DESEQ result, saturation reflects log2FC.
  • Figures 15A-G show lymphatic endothelial cells (LECs) dissection.
  • LECs lymphatic endothelial cells
  • Figure 15A shows top enrichments in D1 for LECs from pseudo-bulk based DE analysis.
  • Figures 15B-G show LEC sub-clustering analysis.
  • Figure 15B shows UMAP of LECs sorted by spatial subclusters, and dotplot of cell cycle score.
  • Figure 15C shows correlation of subcluster expression signal.
  • Figure 15D shows cell cycle score distribution.
  • Figure 15E shows proportion distribution of subclusters LEC-3 and LEC-6 across samples.
  • Figure 15F shows gene set enrichment analysis of subcluster LEC-3 marker genes.
  • Figure 15G shows GSEA analysis of LEC-3 DEA results.
  • Figures 16A-I show dividing cells dissection.
  • Figure 16A shows subtyping and re- embedding of the dividing cells population, low dimensional embedding representation of the dividing cells sorted by their associated cell types. Cells belonging to the dividing group in Figure 3A were isolated and re-clustered. Marker gene expression was used to label them.
  • Figure 16B shows top marker based GSEA results in DIV-PER and
  • Figure 16C shows top marker based GSEA results in DIV-VEC2.
  • Figures 16F-I show expression of ICAM1, P-selectin (SELP), E-selectin (SELE), across all cell types (Figure 16F) Pseudobulk level DE results in VEC population ( Figure 16G), dividing cells subtypes (Figure 16H), across conditions in DIV-VEC1 and DIV-VEC2 ( Figure 16I).
  • Figures 17A-C show quality control of decedent PBMC scRNA-seq.
  • Figure 17A shows distribution of the main CellRanger sample-level QC metrics, across all PBMC scRNA- seq samples. Each sample makes a data point (black dots).
  • Figures 18A-B show additional flow cytometry data.
  • Figure 18A shows flow cytometry gating scheme for one representative sample. Light grey boxes indicate analyzed populations, and dark grey boxes indicate subsets that were further subdivided.
  • Figure 18B shows monocytes identified as CD56-CD3-CD19-HLA-DR + cells and subdivided into classical (CD14 + CD16-), non-intermediate (CD14 + CD16 + ), and non-classical subsets (CD14-CD16 + ).
  • Figures 19A-B show pig and human nuclei categorization. Number of unique molecular indices (UMI) counts mapping to the human (x-axis) or pig (y-axis) genome from the xenograft snRNA-seq, resulting from the CellRanger ‘Barnyard’ experiment multiple genome aligner. Nuclei are then assigned a genome based on that distribution, and cross-species multiplets are called.
  • UMI unique molecular indices
  • Figure 19A shows linear scale.
  • Figure 19B shows logarithmic scale. This partition was used to separate pig and human nuclei in the Xenograft snRNA-seq, for downstream analysis.
  • Figures 20A-F show quality control of heart xenograft sn-RNAseq data.
  • Figure 20A shows distribution of the main CellRanger sample-level QC metrics, across all heart xenograft sn-RNAseq samples (pig genome alignment). Each sample makes a data point (black dots).
  • FIG. 20E shows human transgene expression levels in the xenotransplant Visium Spatial Transcriptomics data, spots grouped by Spatial Clusters ( Figure 20E), and by sample ( Figure 20F).
  • Figures 21A-F show top enrichments from cell type marker gene based GSEA for pig nuclei. Saturation depicts the normalized enrichment score (NES), indicating negative and positive scores.
  • Figure 21A shows CM
  • Figure 21B shows SMC
  • Figure 21C shows VEC
  • Figure 21D shows LEC
  • Figure 21E shows MP
  • Figure 21F shows FB.
  • Figures 22A-22G show differential expression analysis of heart xenograft pig nuclei.
  • Figures 22A-F show top enriched pathways (from GSEA analysis of DE results), in CM ( Figures 22A-C) and in FB ( Figures 22D-F), for Ecmo, Ischemia 7 hr and decedent 2 conditions.
  • Saturation depicts the normalized enrichment score (NES), indicating negative and positive scores.
  • FIG. 22G shows expression distribution of lead genes in decedent 1 which contributed to specific pathways in GSEA. Dot size reflects the - log10FDR from DESEQ result, saturation reflects log2FC. Significance (padj ⁇ 0.05) is denoted by a black star. The LFC and adjusted p-value (Wald test p-value, twotailed, adjusted with the Benjamini Hochberg method) were obtained directly from the DESeq2 DEA output. [0077] Figures 23A-23F shows fibroblast sub-clustering analysis.
  • Figure 23A shows top 5 marker genes (Wilcoxon rank sum test) for FB subclusters.
  • Figure 23B shows top 50 marker genes (Wilcoxon rank sum test) for subcluster FB4.
  • Figure 23C shows top 50 marker genes (Wilcoxon rank sum test) for subcluster FB2.
  • Figure 23D shows correlation of subcluster 28 314164997v1 Attorney Docket No: 243735.000437 expression signal.
  • Figure 23E shows subclusters proportion distribution
  • Figure 23F shows gene set enrichment analysis of subcluster FB2 marker genes.
  • Figures 24A-G show cardiomyocytes sub-clustering analysis.
  • Figure 24A shows UMAP of CMs sorted by subclusters.
  • Figure 24B shows correlation of subcluster expression signal.
  • Figure 24C shows gene set enrichment analysis of subcluster CM4 marker genes.
  • Figure 24D shows top 5 marker genes (Wilcoxon rank sum test) for CM subclusters.
  • Figure 24E shows top 50 marker genes (Wilcoxon rank sum test) for subcluster CM2.
  • Figure 24F shows top 50 marker genes (Wilcoxon rank sum test) for subcluster CM4.
  • Figure 24G shows subclusters proportion distribution.
  • Figures 25A-B show cell-cell communication analysis from heart xenograft. Number of interactions (Figure 25A) and strength of interactions (Figure 25B) are shown between cell types, across conditions, predicted by CellChat.
  • Figures 26A-C show spatial transcriptomics deconvolution with cell2location.
  • Figures 27A-C show spatial transcriptomics deconvolution with DestVI. For each cell type, the DestVI-predicted cell-type proportions are plotted on the spatial transcriptomics slide. Scale bar: 1 mm.
  • Figure 27A shows D1-LV-B, Figure 27B shows D1-RV-B, and Figure 27C shows D2-RV-B.
  • Figures 28A-C show spatial transcriptomics deconvolution signal.
  • FIG. 29A-D show marker genes for spatial transcriptomics clusters of interest. Top 60 marker genes (Wilcoxon rank sum test) for vascular Spatial transcriptomics clusters are shown. Dot size reflects the percentage of spots in each group with gene expression above zero. Dot saturation displays each group's mean gene expression, min-max scaled.
  • Figure 29A shows Cluster 7.
  • Figure 29B shows Cluster 8.
  • Figure 29C shows Cluster 9.
  • Figure 29D shows Cluster 10.
  • Figures 30A-H show vascular endothelial cells sub-clustering analysis.
  • Figure 30A shows top 5 marker genes (Wilcoxon rank sum test) for VEC subclusters.
  • Figures 30B-D show top 50 marker genes (Wilcoxon rank sum test) for subclusters VEC5 ( Figure 30B), VEC4 ( Figure 30C), and VEC9 ( Figure 30D).
  • Figures 30E-F show gene set enrichment analysis of subclusters VEC5 ( Figure 30E) and VEC9 (Figure 30F) marker genes.
  • Figure 30G shows correlation of subcluster expression signal.
  • Figure 30H shows subclusters proportion distribution.
  • Figures 31A-G show pericytes and smooth muscle cells sub-clustering analysis.
  • Figure 31A shows top 5 marker genes (Wilcoxon rank sum test) for PER-SMC subclusters.
  • Figure 31B shows top 50 marker genes (Wilcoxon rank sum test) for subcluster PER-SMC4.
  • Figure 31C shows top 50 marker genes (Wilcoxon rank sum test) for subcluster PER-SMC1.
  • Figure 31D shows correlation of subcluster expression signal.
  • Figure 31E shows expression of SMC and PER markers across subclusters.
  • Figure 31F shows gene set enrichment analysis of subcluster PER-SMC1 marker genes.
  • Figure 31G shows subclusters proportion distribution.
  • Figures 32A-D show lymphatic endothelial cells sub-clustering analysis.
  • Figure 32A shows top 5 marker genes (Wilcoxon rank sum test) for LEC subclusters.
  • Figure 32B shows top 50 marker genes (Wilcoxon rank sum test) for subcluster LEC2.
  • Figure 32C shows gene set enrichment analysis of subcluster LEC2 marker genes.
  • Figure 32D shows subclusters proportion distribution.
  • Figures 33A-D show single-cell RNA-seq analyses of pig-to-human kidney xenotransplantation.
  • Figure 33A shows a schematic overview of the transcriptomic analyses of pig-to-human kidney xenotransplantation. Parts of the figure were created with BioRender.
  • Figure 33B shows unsupervised clustering of the merged single cell transcriptomes across all samples (left) and visualization by cell types (right).
  • UMAP Uniform Manifold Approximation and Projection (McInnes, et al., 2018).
  • Figure 33C shows UMAP visualization of single cell distribution from each kidney sample.
  • Figure 33D shows a dot plot of marker genes indicating cell-type identity across all cell populations. The saturation intensity and dot size represent average expression level and the percentage of expressed cells, respectively.
  • FIG. 34A-F show that human NK cells and macrophages infiltrate into porcine kidney xenograft.
  • Figure 34A shows UMAP visualization of human (dark grey) and porcine (light grey) cell distribution. The immune cell cluster is enlarged for detail.
  • Figure 34B shows UMAP visualization of sub-clustered human and porcine immune cell types.
  • Figure 34C shows a heatmap of human-to-porcine raw reads counting ratios (H/P ratio) of macrophage and NK cell marker genes.
  • Figure 34D shows violin plots of interferon-gamma signaling gene expressions in human and porcine immune cell populations in single-cell transcriptome data.
  • Figures 34E-F show xenotransplantation time-resolved gene expression levels (Figure 34E) and human/porcine transcript (H/P) ratio (Figure 34F) of interferon-gamma signaling genes in the longitudinal bulk RNA-seq of xenograft biopsies. Statistical significance is indicated by asterisks above to the violin plots: * for p ⁇ 0.05, ** for p ⁇ 0.01, *** for p ⁇ 0.001, and **** for p ⁇ 0.0001. [0089] Figures 35A-D show identification of rejection signals in porcine endothelial cells and in immune cells.
  • Figures 35A-B show relative gene expression levels of AbMR (Figure 35A) and TCMR (Figure 35B) marker genes over the period of the xenotransplantation. Genes with dramatic expression changes (>2 folds) at the last time-point were shown in a bigger font size relative to other genes.
  • Figures 35C-D show violin plots of AbMR marker gene expression in porcine endothelial cells (Figure 35C) and TCMR marker gene expression (Figure 35D) in immune cells. Gene expression levels were detected in single-cell RNA-seq data.
  • Figures 36A-J show xenotransplantation-associated porcine kidney damage and the activation of a cell proliferation program.
  • Figure 36A shows volcano plots of differentially expressed genes between xenograft and control for the first (left) and second (right) cases of xenotransplantation. Representative genes (biomarkers of kidney-injury and proliferation) were labeled. A few data dots in the plot reached a ceiling value on the y-axis, indicating system’s default lowest p-value.
  • Figure 36B shows violin plots of kidney-injury biomarker expression levels across major cell types in the nephron.
  • Figure 36C shows a UMAP highlight of the proliferating cell population.
  • Figures 36D-E show UMAP visualization of the sub-clustered proliferating cell cluster signified by organismal origin ( Figure 36D) and cell cycle phase 31 314164997v1 Attorney Docket No: 243735.000437 assignment (Figure 36E).
  • Figure 36F shows ridge plots of top marker genes in cells across G1, S, and G2 phases.
  • Figures 36G-I show gene expression levels of proliferation marker (STMN1, Figure 36G), proximal tubule cell marker (SLC34A1, Figure 36H), and T-cell marker (CD3E, Figure 36I) in the proliferating cell population.
  • Figure 36J shows time-resolved gene expression levels of kidney tissue injury marker genes (labeled damage-related) and cell cycle genes (labeled proliferation-related) in longitudinal RNA-seq.
  • Figures 37A-E show recipient’s PBMCs showing two waves of immune response expression signatures.
  • Figure 37A shows Uniform Manifold Approximation and Projection (UMAP) visualization of the second kidney xenograft recipient’s PBMC clusters.
  • UMAP Uniform Manifold Approximation and Projection
  • Figure 37B shows cell type proportions of PBMC populations across the six timepoints.
  • Figure 37C shows temporally variable MHC class II genes expression pattern across the period of xenotransplantation in representative antigen presenting cell types.
  • Figures 37D-E show relative gene expression levels of gene sets enriched at 12 hours pXTx ( Figure 37D) and 48-53 hours pXTx ( Figure 37E) across the period of xenotransplantation in representative cell types.
  • Figures 38A-D show violin plots of quality control features for all cell types across each kidney single-cell RNA-seq dataset.
  • Figure 38A shows xenograft 1
  • Figure 38B shows xenograft 2
  • Figure 38C shows control kidney 1
  • Figure 38D shows control kidney 2 data.
  • Figures 39A-F show feature plots of marker genes for identifying cell types.
  • Figures 39A-D show UMAP visualization of marker genes used for identifying cell types in kidney single-cell RNA sequencing datasets. The genes correspond to the marker genes in Figure 33D.
  • Figures 39E-F show podocyte marker gene expression levels presented as violin plot (left) across all cell clusters and as scatter plot (right) in the VIM-positive proximal tubule (PT-VIM+) 32 314164997v1 Attorney Docket No: 243735.000437 cells.
  • Figures 40A-E show immune cell type characterization and gene function enrichment analyses of the two xenografts.
  • Figures 40A-C show clustering and cell-type identification of immune cells.
  • Feature plots for marker genes for macrophage CD163, TYROBP
  • Figure 40A NK cell
  • Figure 40B NK cell
  • Figure 40C T cell
  • Figure 40C identifies human cells to be macrophages and NK cells, and porcine cells to be NK, NKT, and T cells, respectively.
  • Figure 40D shows gene ontology enrichment analysis for Top 1000 Differentially Expressed Genes (DEG) ranked by average log2 fold change for xenograft 1 (top) and xenograft 2 (bottom).
  • Figure 40E shows KEGG Enrichment Analysis for xenograft 1 (top) and xenograft 2 (bottom).
  • Figures 41A-G show violin plots of PBMC single-cell RNA-seq data and the cell type marker genes.
  • Figures 41A-F show PBMCs collected at 0h, 6h, 12h, 24h, 48h, and 53h post- xenotransplantation.
  • Figure 41G shows a violin plot visualization of marker genes used for cell type identification in PBMCs. The cell type annotations correspond to Figure 37A.
  • Figures 42A-C show gene expression signatures in recipient’s PBMCs across the period of xenotransplantation.
  • Figures 42A-B show gene ontology enrichment of enriched gene sets.
  • Significantly enriched GO term corresponds to the gene sets of cell types at 12 hours pXTx ( Figure 42A) and at 48-53 hours pXTx ( Figure 42B) presented in Figure 37.
  • Figure 42C shows a dot plot of interferon-gamma expression in PBMCs across the period of the study. NK cells and NKT cells were the main cell types of expression interferon-gamma.
  • Figure 43 shows a graphical abstract of the studies performed herein. The figure was made with BioRender.
  • Figure 44 shows a study overview. A schematic of the study is shown, indicating the major modalities (top) as well as the tissue and blood timepoints, assays utilized at each respective timepoint, treatments/medications utilized, and clinical microbiology testing performed (bottom) in the 61-day period following the pig-to-human Gal-KO thymokidney xenotransplant.
  • CTRL samples from the 5.1k ST panel data correspond to contralateral pig kidney (only pig cells) and native human kidney (only human cells), which act as positive 33 314164997v1 Attorney Docket No: 243735.000437 and negative controls for human cell selection in the analysis herein.
  • BAL bronchoalveolar lavage
  • EBV Epstein-Barr virus
  • Gal-KO ⁇ -1,3-galactosyltransferase (GGTA1) knockout
  • HBV hepatitis B virus
  • rATG rabbit anti-thymocyte globulin.
  • Figure generated with BioRender Figure generated with BioRender.
  • Figures 45A-M show that high-resolution spatial-transcriptional profiling reveals infiltrating human cells in porcine kidneys.
  • Figure 45A shows images illustrating the analytical steps performed for spatial high-resolution tissue transcriptomic profiling (Xenium); examples are from POD49.
  • Figure 45B shows separation of pig (dots above the dotted line) and human (dots below the dotted line) nuclei based on the number of identified transcripts originating from both genomes (sample POD33 is shown as an example).
  • Figure 45C shows human / pig gene expression distribution among cells profiled with the 5.1k ST panel, separated by their assigned species.
  • FIG. 45D shows spatial transcriptomics cell-type labeling. Transplanted tissue H&E image (top panel), and corresponding spatial transcriptomics DAPI (4′,6-diamidino-2-phenylindole (DNA stain)) fluorescence image (bottom panel) with segmentation-based cells are notated by cell type.
  • Figure 45E shows human cells infiltrating the renal cortex with their cell type annotation and associated markers (POD49).
  • Figure 45F shows glomerulus and infiltrating human cells with cell-type annotation and associated markers (POD49).
  • Figures 45G-L show dimension reduction maps of cells identified in the xenograft tissue, notated by identified cell type, for human ( Figures 45G, I, K), and pig ( Figures 45H, J, L) cells, in the 478 Xenium panel ( Figures 45G- H), the 5.1k Xenium panel ( Figures 45I-J), and snRNA-seq ( Figures 45K-L) modalities.
  • Figure 45M shows dimension-reduction map of cells identified in recipient PBMC scRNA-seq, notated by cell type. Left: main cell-types, right-up: T and NK cells, right-down: B cells, plasma cells, dendritic cells.
  • FIGS 45A, D, E, F show data from the 478 ST panel.
  • MP Macrophages
  • CD8T CD8 + T cells
  • CD4T CD4 + T cells
  • NK NK cells
  • pDC plasmacytoid dendritic cells
  • FB Fibroblasts
  • EC Endothelial cells
  • SMC Smooth muscle cells
  • vSMC vascular smooth muscle cells
  • fSMC fibroblast-associated smooth muscle cells
  • VEC vascular endothelial cells
  • cDC classical dendritic cells
  • TEM T effector memory cells
  • MK Megakaryocytes
  • HSC Hematopoietic stem cells
  • TCM T central memory cells
  • MAIT Mucosal-Associated Invariant T cells
  • Treg Regulatory T cells.
  • Figures 46A-K show early host immune cell response involving macrophages, NK cells, pDCs, and plasma/B cells.
  • Figure 46A shows percentage of human cells identified by spatial transcriptomics in the xenograft tissue (5.1k ST panel) across all identified cells.
  • Figure 46B shows plasmablast levels measured in the recipient PBMC scRNA-seq (top), and B marker expression levels from PBMC bulk RNA-seq (bottom).
  • Figure 46C shows human B cell levels measured by spatial transcriptomics in the xenograft tissue (5.1k ST panel, top), in the recipient PBMC scRNA-seq (middle), and marker expression levels identified by PBMC bulk RNA-seq (bottom).
  • Figure 46D shows human NK cell levels measured by spatial transcriptomics in the xenograft tissue (5.1k ST panel, top), in recipient PBMC scRNA-seq (middle), and by PBMC bulk RNA-seq marker expression (bottom).
  • Figure 46E shows flow cytometry levels of NK and B cells.
  • Figure 46F shows human pDC levels measured by spatial transcriptomics in the xenograft tissue (5.1k ST panel, top), in recipient PBMC scRNA-seq (middle), and by PBMC bulk RNA-seq marker expression (bottom).
  • Figure 46G shows quantification of unique BCR clones from bulk BCR-Seq divided by clonal expansion level.
  • Figure 46H shows quantification of unique BCR clones from bulk BCR-Seq divided by BCR isotype family.
  • Figure 46I shows percentage of human macrophages/monocytes (top), CXCL9+ macrophages (middle), and interferon+ immune cells (bottom) in the xenograft tissue (5.1k ST panel).
  • Figure 46J shows gene set enrichment analysis (GO Biological process) of cell type markers for CXCL9+ macrophages (top) and interferon+ cells (bottom).
  • Figure 46K shows expression of CXCL9, CXCL10, and CXCL11 from the infiltrating human immune cells in the xenograft (from 5.1k ST data).
  • ST spatial transcriptomics
  • NK Natural Killer cells
  • pDC plasmacytoid dendritic cells.
  • Figures 47A-T show human T-cell response to xenotransplantation.
  • Figures 47A-D show percentage of T cell subpopulations in the PBMC scRNA-seq data.
  • Figure 47A This includes CD8 TEM ( Figure 47A), Naive CD8 T (Figure 47B), CD4 TCM-TEM (Figure 47C) and Tregs (Figure 47D).
  • Figures 47E-H show percentage of T cell subtypes observed in the 5.1k panel ST data, including CD8 TEM ( Figure 47E), naive CD8T (Figure 47F), CD4T ( Figure 47G) and Tregs (Figure 47H).
  • Figure 47I shows percentage of all T cells from blood scRNA-seq data.
  • Figures 47J-L show T-cell subtypes marker expression in blood bulk RNA-seq data, for CD8T markers CD8A and CD8B (Figure 47J), the CD4T marker TSHZ2 (Figure 47K) and Treg markers RTKN2 and CTLA4 (Figure 47L).
  • Figures 47M-P show T-cell levels in flow cytometry data, for all T cells (Figure 47M).
  • Figure 47Q shows T and NK cell subtypes expression of 35 314164997v1 Attorney Docket No: 243735.000437 ITGAE (CD103), CD38 and TNFRSF9 across data modalities. Samples that have less than 5 cells of the population in question are excluded from the graph.
  • Figure 47R shows quantification of unique TCR clones from bulk TCR-seq divided by clonal expansion level.
  • Figure 47S shows V+J gene expression levels of TCR Beta clones from bulk TCR-seq.
  • Figure 47T shows tracking of the top TCR Beta clones in bulk TCR-seq postoperative time course.
  • TEM T effector memory cells
  • TCM T central memory cells
  • Treg Regulatory T cells
  • UMI Unique molecular identifier
  • NK Natural killer cells.
  • Figure 47T discloses SEQ ID NO: 1.
  • Figures 48A-H show that the transplanted porcine tissue response highlights the damage signaling at POD21 and POD33.
  • Figure 48A shows expression of inflammatory related genes (FOS, FOSB, CXCL12) in the tissue xenograft porcine cells (from the 478 ST panel).
  • Figure 48B shows the proportion of COLEC11+ cells across timepoints (5.1k panel).
  • Figure 48C shows the proportion of SPP1+ cells across timepoints (478 panel).
  • Figures 48D-E show expression of markers of interest for SPP1+ cells in the 478 panel ( Figure 48D) and COLEC11+ cells in the 5.1k panel ( Figure 48E).
  • Figure 48F shows POD21 biopsy region corresponding to high levels of SPP1+ cells (478 panel).
  • Figure 48G shows POD33 biopsy (H&E and ST) with high levels of human immune cells (478 panel).
  • Figure 48H shows POD33 biopsy highlighting expression of pig and human genes of interest near human cells and COLEC11+ cells (5.1k panel).
  • FB Fibroblasts
  • EC Endothelial cells
  • MP Macrophages
  • NK Natural Killer cells
  • pDC Plasmacytoid Dendritic Cells
  • CD8T CD8 + T cells
  • CD4T CD4 + T cells
  • fSMC fibroblast associated smooth muscle cells
  • vSMC vascular smooth muscle cells
  • SMC Smooth muscle cells
  • ST spatial transcriptomics.
  • Figures 49A- C, G pertain to the 478 ST panel data and Figures 49D-F, H-I to the 5.1k ST panel data.
  • Figure 49A shows the distribution of total human and pig probe counts for each identified cell (dots). Cells are notated by their assigned groups (pig, dots above the dotted line; human, dots below the dotted line).
  • Figure 49B shows the distribution of the fraction of counts originating from human probes.
  • Figure 49C shows the distribution of mean expression of human and pig genes, with cells represented as dots notated by their assigned group.
  • Figure 49D shows joint dimension reduction plots from all samples integrated, notated by sample ID (top) and highlighting human kidney cells that were subsequently removed (they originate from the human native kidney sample).
  • Figure 49E shows the resulting integration dimension reduction plots, notated by 36 314164997v1 Attorney Docket No: 243735.000437 fraction of normalized gene expression originating from human (top), by sample ID (left) and highlighting the selected human cluster (right).
  • Figure 49F shows dimension reduction of the human cell clusters, highlighting human gene expression (top), pig gene expression (left), and basis for the identification of a doublet population (right), which is subsequently removed from the human cluster.
  • Figure 49G shows the percentage of human cells found in the 478 ST panel across samples.
  • Figure 49H shows the percentage of human cells found in the 5.1k ST panel across samples.
  • Figure 49I shows the percentage of immune cells from the human native kidney sample sorted by their partition.
  • Figures 50A-D show identification of pig and human cells from xenograft ST and snRNA-seq data.
  • Figure 50A shows human / pig gene expression distribution among 5.1k ST panel transcriptomics data cells, separated by their assigned species. The metric is the fraction of normalized expression of human probes, against all other probes (x-axis). The y-axis shows the percentage of cells of that group (pig or human). Plots are shown for each biopsy timepoint.
  • Figure 50B shows the distribution of the fraction of gene expression originating from human genes in the xenograft snRNA-seq data (log scale).
  • Figure 50C shows joint dimension reduction plots from all snRNA-seq samples integrated, highlighting the fraction of gene expression originating from human genes (left), and the cluster subsequently selected as containing human cells (right).
  • Figure 50D shows dimension reduction plot of the selected human cells, notated by their fraction of human gene expression.
  • Figures 51A-M show further dissection of human macrophages, NK cells, B cells and dendritic cells.
  • Figures 51A-B show dimension reduction graph ( Figure 51A) and percentage across all cells by timepoint ( Figure 51B) of human macrophage, monocytes and dendritic subtypes found in the 5.1k panel ST data notated by cell-type.
  • Figures 51C-D show dimension reduction graph (Figure 51C) and percentage across all cells by timepoint (Figure 51D) for B cells, plasma cells, mast cells and neutrophils found in the 5.1k panel ST data, notated by their names.
  • Figures 51E-G show marker gene expression of those populations in the 5.1k panel ST data.
  • Figure 51H shows percentage levels for additional cell-type populations not already shown in this figure.
  • Figures 51I-J show cell-type markers ( Figure 51I) and associated percentage (Figure 51J) of B, plasma, and dendritic cell populations found in the PBMC scRNA-seq data.
  • Figure 51K shows percentage of human macrophages, pDC and NK cells found in the 478 panel ST data.
  • Figures 51L-M show pDC activation marker genes and CXCL9–CXCL11 expression 37 314164997v1 Attorney Docket No: 243735.000437 in pDC-cDC1 populations in 5.1k ST data ( Figure 51L) and PBMC scRNA-seq data ( Figure 51M).
  • Figures 52A-L show early response from human macrophages and B cells as evidenced by ST and snRNA-seq in addition to BCR-seq.
  • Figure 52A shows expression of immunoglobulin genes in PBMC bulk RNA-seq.
  • Figure 52B shows CDR3 length (nt) distribution in bulk BCR-seq.
  • Figure 52C shows somatic hypermutation frequency in bulk BCR- seq divided by isotype.
  • Figure 52D shows shared overlapping clonotypes across bulk BCR-seq postoperative timepoints.
  • Figure 52E shows tracking of the top BCR IgH clones in bulk BCR- seq postoperative time course.
  • Figures 52F-I show 478 panel ST data highlighting human subpopulation of macrophages and NK cells expressing CXCL9, CXLC10, and CXCL11 as shown in a dimension reduction map (Figure 52F), distribution of these markers across those subpopulations (Figure 52G) and across time in the NK-MP populations (Figure 52H) and percentage distribution of these subpopulations across time ( Figure 52I).
  • Figures 52J-K show similar populations of interest in snRNA-seq data, as shown in expression of markers corresponding to the 5.1k panel data subtypes markers (Figure 52J) and their distribution over time (Figure 52K).
  • Figure 52L shows expression of human CXCL9, CXCL10, CXCL11 observed in the tissue bulk RNA-seq.
  • Figures 53A-K show further human T cell response dissection.
  • Figures 53A-C show human T cell subtypes found in 5.1k ST data, as shown via dimension reduction plot (Figure 53A), percentage across all cells in each timepoint (Figure 53B), and marker genes defining these cells (Figure 53C).
  • Figure 53D shows cell type markers of PBMC NK and T cells populations.
  • Figure 53E shows proportion of proliferative human NK found in the 5.1k ST data.
  • Figures 53F-G show proportions (Figure 53F) and marker gene expression (Figure 53G) of human CD8T and CD4T from the 478 panel ST data.
  • Figure 53H shows distribution of T and NK subtypes found in the PBMC scRNA-seq data, as percentage of all cells across timepoints (for subtypes not shown in Figure 47).
  • Figure 53I shows percentage of human dividing and T cells found in snRNA-seq data.
  • Figure 53J shows expression levels of CD8T markers (CD8A, CD8B, top) and Tregs markers (RTKN2, CTLA4, bottom), from PBMC bulk RNA-seq.
  • Figure 53K shows flow cytometry T cells distributions, CD8 + T cells (left) and CD4 + T cells (right), separated by central memory and effector memory cells. 38 314164997v1 Attorney Docket No: 243735.000437
  • Figures 54A-D show distribution of marker genes delineating resident and circulating T and NK cells.
  • Figure 54A shows NK markers from 5.1k panel ST data.
  • Figure 54B shows T cell markers from 5.1k panel ST data.
  • Figure 54C shows NK markers from PBMC scRNA-seq data.
  • Figure 54D shows T cell markers from PBMC scRNA-seq data.
  • Figures 55A-J show porcine transcriptional response from tissue and porcine resident immune cells.
  • Figure 55A shows inflammatory gene clusters identified by bulk RNAseq longitudinal analysis. Expression over time (top) and top pathway enrichment (bottom) are shown.
  • Figure 55B shows gene-set enrichment of genes overexpressed in specific PODs in the 478 ST panel.
  • Figure 55C shows colocalization analysis of SPP1+ cells (top) and pig immune cells (bottom) from the 478 panel ST data.
  • Figure 55D shows colocalization analysis of COLEC11+ cells (top) and pig immune cells (bottom) from the 5.1k panel ST data.
  • Figures 55E-F show macrophage and T-cell marker expression across cell-types in the 5.1k panel ( Figure 55E) and 478 panel ( Figure 55F) ST data.
  • Figure 55G shows T-cell marker expression across timepoints, shown in pig immune cells in the 5.1k panel (left) and 478 panel (right) ST data.
  • Figure 55H shows the top 80 marker genes for POD33 pig immune cells, identified by Wilcoxon rank-sum analysis comparing their transcriptomic profile to all other time points.
  • Figures 55I-J show expression of marker genes of interest across timepoints in specific pig cell populations, in snRNA-seq samples of the xenograft ( Figure 55I) and in 5.1k ST data ( Figure 55J).
  • Figures 56A-G show B-HOT AbMR signature revealed by ST.
  • Figures 56A-D show B-HOT AbMR signature expression in the 478-gene ST panel, using genes identified as differentially expressed (DEG post- vs. pre-transplantation) in xenografts from Loupy, et al. (Loupy, et al., 2023).
  • Figures 56E-G show B-HOT AbMR signature expression in the 5.1k-gene ST panel, using the entire B-HOT panel, regardless of overlap with Loupy, et al. (Loupy, et al., 2023) results.
  • Figure 56A shows spatial distribution of the B-HOT AbMR signature in POD33 tissue.
  • Figure 56B shows B-HOT signature enrichment in UMAP, overlaid with cell-type composition.
  • Figure 56C shows AbMR signature enrichment across time points.
  • Figure 56D shows marker expression of B-HOT AbMR genes.
  • Figure 56E shows expression of genes (human probes) belonging to the B-HOT AbMR signature across timepoints in all human cells.
  • Figure 56F shows expression of pig genes whose human orthologues are part of the B-HOT 39 314164997v1 Attorney Docket No: 243735.000437 panel, across timepoints in all pig cells.
  • Figure 56G shows spatial distribution in POD33 of the AbMR gene set score, and pig SERPINE1 expression.
  • Figure 57 shows dynamics of selected complement pathway elements protein levels in the serum. Serum abundance over time is shown for the indicated complement factors.
  • Figures 58A-D show expression of pig and human probes from Xenium ST data.
  • Figures 58A-C show expression from the 5.1k ST panel data and Figure 58D shows expression from the 478 panel ST data.
  • Figures 58A-B show expression of human and pig probes for orthologous genes across samples in pig ( Figure 58A) and human ( Figure 58B) immune cells.
  • Figure 58C shows expression of human and pig probes targeting orthologous genes, across cell- types in human immune cells (5.1k panel). In this panel, only probes targeting orthologues with a mean log-normalized expression of at least 0.15 in at least one cell type are shown.
  • Figure 58D shows expression of all human and pig probes targeting orthologous genes, across all human and pig cell types.
  • Figures 59A-E show dissection of human cells found in the xenograft 478 panel Xenium ST data.
  • Figure 59A shows a dimension reduction map of the data notated by timepoint.
  • Figure 59B shows a dimension reduction map of the data notated by cell-type.
  • Figure 59C shows cell type proportion across timepoints.
  • Figure 59D shows Wilcoxon-based differential expression results (top genes for each cell type).
  • Figure 59E shows a dimension reduction map of the data for each timepoint.
  • Figures 60A-E show dissection of pig cells found in the xenograft 478 panel Xenium ST data.
  • Figure 60A shows a dimension reduction map of the data notated by timepoint.
  • Figure 60B shows dimension reduction map of the data notated by cell-type.
  • Figure 60C shows cell type percentage across timepoints.
  • Figure 60D shows Wilcoxon based differential expression results (top genes for each cell type).
  • Figure 60E shows dimension reduction map of the data for each timepoint.
  • Figures 61A-G show dissection of human cells found in the xenograft 5.1k panel Xenium ST data.
  • Figure 61A shows dimension reduction map of the data notated by timepoint.
  • Figure 61B shows dimension reduction map of the data notated by cell-type.
  • Figure 61C shows 40 314164997v1 Attorney Docket No: 243735.000437 cell type percentage across timepoints.
  • Figures 61D-G show Wilcoxon based differential expression results (top genes for each cell type), for all cells (Figure 61D), monocytes/macrophages and dendritic cells (Figure 61E), NK and T cells ( Figure 61F), and Mast, B and plasma cells ( Figure 61G).
  • Figure 62 shows dimension reduction map of the data for each timepoint for the 5.1k panel Xenium ST data.
  • Figures 63A-E show dissection of pig cells found in the xenograft 5.1k panel Xenium ST data.
  • Figure 63A shows dimension reduction map of the data notated by timepoint.
  • Figure 63B shows dimension reduction map of the data notated by cell-type.
  • Figure 63C shows cell type proportion across timepoints.
  • Figure 63D shows Wilcoxon based differential expression results (top genes for each cell type).
  • Figure 63E shows dimension reduction map of the data for each timepoint.
  • Figures 64A-E show dissection of human cells found in the xenograft snRNA seq data (all human cells).
  • Figure 64A shows dimension reduction map of the data notated by timepoint.
  • Figure 64B shows dimension reduction map of the data notated by cell-type.
  • Figure 64C shows cell type proportion across timepoints.
  • Figure 64D shows Wilcoxon based differential expression results (top genes for each cell type).
  • Figure 64E shows dimension reduction map of the data for each timepoint.
  • Figures 65A-E show dissection of human cells found in the xenograft snRNA seq data (lymphocytes and NK cells).
  • Figure 65A shows dimension reduction map of the data notated by timepoint.
  • Figure 65B shows dimension reduction map of the data notated by cell-type.
  • Figure 65C shows cell type proportion across timepoints.
  • Figure 65D shows Wilcoxon based differential expression results (top genes for each cell type).
  • Figure 65E shows dimension reduction map of the data for each timepoint.
  • Figures 66A-E show dissection of pig cells found in the xenograft snRNA-seq data.
  • Figure 66A shows dimension reduction map of the data notated by timepoint.
  • Figure 66B shows dimension reduction map of the data notated by cell-type.
  • Figure 66C shows cell type proportion across timepoints.
  • Figure 66D shows Wilcoxon based differential expression results (top genes for each cell type).
  • Figure 66E shows dimension reduction map of the data for each timepoint. 41 314164997v1 Attorney Docket No: 243735.000437
  • Figures 67A-E show dissection of main cell types found in the PBMC scRNA-seq data.
  • Figure 67A shows dimension reduction map of the data notated by timepoint.
  • Figure 67B shows dimension reduction map of the data notated by cell-type.
  • Figure 67C shows cell type proportion across timepoints.
  • Figure 67D shows Wilcoxon based differential expression results (top genes for each cell type).
  • Figure 67E shows dimension reduction map of the data for each timepoint.
  • Figures 68A-E show dissection of T and NK cells found in the PBMC scRNA-seq data.
  • Figure 68A shows dimension reduction map of the data notated by timepoint.
  • Figure 68B shows dimension reduction map of the data notated by cell-type.
  • Figure 68C shows cell type proportion across timepoints.
  • Figure 68D shows Wilcoxon based differential expression results (top genes for each cell type).
  • Figure 68E shows dimension reduction map of the data for each timepoint.
  • Figures 69A-E show dissection of B, plasma and dendritic cells found in the PBMC scRNA-seq data.
  • Figure 69A shows dimension reduction map of the data notated by timepoint.
  • Figure 69B shows dimension reduction map of the data notated by cell-type.
  • Figure 69C shows cell type proportion across timepoints.
  • Figure 69D shows Wilcoxon based differential expression results (top genes for each cell type).
  • Figure 69E shows dimension reduction map of the data for each timepoint.
  • Figures 70A-B show dissection of B-HOT annotation in the Xenograft ST data.
  • Figures 70A-B show levels of scores derived from the B-HOT signature gene set shown across cell types ( Figure 70A) and timepoints (Figure 70B), in the human cells of the 5.1k genes panel ST dataset.
  • Figure 71 shows dynamics of human complement pathway elements protein levels in the serum. Statistical analysis of protein regulation was based on XT2 Proteograph XT measurements of serum samples. The middle line, shaded region, and dashed line bands correspond to the median and 50% and 95% credible intervals of the protein intensity posterior distribution, respectively.
  • Figure 72 shows dynamics of porcine complement pathway elements protein levels in the serum. Statistical analysis of protein regulation was based on XT2 and XT1 Proteograph XT measurements of serum samples.
  • the middle line, shaded region, and dashed line bands 42 314164997v1 Attorney Docket No: 243735.000437 correspond to the median and 50% and 95% credible intervals of the protein intensity posterior distribution, respectively.
  • DETAILED DESCRIPTION [00127] Considerable progress has been made in pig genome editing to reduce the immunological barriers and incompatibilities between pigs and humans (Cooper, et al., 2012; Montgomery, Mehta, et al., 2022).
  • GGTA1 Galactose- ⁇ -1,3-galactose
  • Xenograft transplantation into brain-dead humans offers a unique opportunity to evaluate 43 314164997v1 Attorney Docket No: 243735.000437 xenografts in the intended recipient model, and to obtain extensive longitudinally-collected blood and tissue samples in a ‘fail-safe’ manner (Montgomery, et al., 2024).
  • the successful GalSafeTM thymus-kidney (“thymokidney”) xenograft transplants were performed in two decedents (Montgomery, Stern, et al., 2022; Loupy, et al., 2023).
  • heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection.
  • longitudinal multi-omics profiling was performed to assess the dynamic interactions in these first two pig heart xenografts transplanted into two human decedents.
  • RNA-seq bulk RNA sequencing
  • scRNA-seq single- cell RNA sequencing
  • snRNA-seq single-nucleotide RNA sequencing
  • Figure 1 systems-level analysis was performed through integration of the respective omics datasets to assess global changes.
  • porcine kidney transplants showed promising physiological functioning during the ⁇ 3 day study period, producing urine and not showing evidence of hyperacute rejection (Montgomery, Stern, et al., 2022).
  • Initial histological analyses of the kidney biopsy samples during and after xenotransplantation did not detect obvious evidence of antibody- mediated rejection (AbMR), nor strong indications of porcine kidney injury (Montgomery, Stern, 45 314164997v1 Attorney Docket No: 243735.000437 et al., 2022).
  • scRNA-seq single-cell RNA sequencing analyses were conducted on the first porcine-to-human kidney xenografts to characterize the intricate xenotransplantation-associated cellular and molecular dynamics and xenograft-recipient interactions. Additionally, longitudinal scRNA-seq of the peripheral blood mononuclear cells (PBMCs) was performed to detect recipient immune responses across time.
  • PBMCs peripheral blood mononuclear cells
  • the most abundant human immune population in the graft was CXCL9+ macrophages, aligning with IFN- ⁇ -driven inflammation and Type I immune response.
  • activated pig-resident macrophages colocalized with infiltrating human cells, suggesting cross-species interactions.
  • Xenograft tissue showed pro-fibrotic tubular and interstitial injury, marked by S100A6, SPP1 (secreted phosphoprotein 1, Osteopontin), and COLEC11, on POD21-POD33.
  • Proteomics revealed complement activation through porcine and human pathways, peaking at POD20 and declining after AbMR therapy including complement inhibition.
  • the implementation of the present invention can utilize, unless otherwise specified, standard techniques in immunology, biochemistry, genetics, computational biology, molecular biology, cell biology, genomics, epigenomics, and bioinformatics, which are well-known to those skilled in the field.
  • the term “about” or “approximately” means within a statistically meaningful range of a value. Such a range can be within an order of magnitude, preferably within 50%, more preferably within 20%, still more preferably within 10%, and even more preferably within 5% of a given value or range.
  • the allowable variation encompassed by the term “about” or “approximately” depends on the particular system under study, and can be readily appreciated by one of ordinary skill in the art.
  • the terms “treat” or “treatment” of a state, disorder or condition include: (1) preventing, delaying, or reducing the incidence and/or likelihood of the appearance of at least one clinical or sub-clinical symptom of the state, disorder or condition developing in a subject that may be afflicted with or predisposed to the state, disorder or condition but does not yet experience or display clinical or subclinical symptoms of the state, disorder or condition; or (2) inhibiting the state, disorder or condition, i.e., arresting, reducing or delaying the development of the disease or a relapse thereof (in case of maintenance treatment) or at least one clinical or sub- clinical symptom thereof; or (3) relieving the disease, i.e., causing regression of the state, disorder or condition or at least one of its clinical or sub-clinical symptoms.
  • the benefit to a subject to be treated is either statistically significant or at least perceptible to the patient or to the physician.
  • the terms “diagnose” or “diagnosis” of xenograft status or outcome include predicting or diagnosing the xenograft status or outcome, determining predisposition to a xenograft status or outcome, monitoring treatment of a xenograft subject, diagnosing a therapeutic response of a xenograft subject, and prognosis of xenograft status or outcome, xenograft progression, and response to treatment.
  • subject refers to mammals, including, without limitation, human and veterinary animals (e.g., cats, dogs, cows, horses, sheep, pigs, etc.) and experimental animal models.
  • subject is a human.
  • sample refers to any biological specimen obtained from a subject or patient.
  • Samples that can be used in the methods of the present disclosure include, without limitation, serum, plasma, whole blood, red blood cells, white blood cells (e.g., peripheral blood mononuclear cells (PBMCs)), polymorphonuclear (PMN) cells, pericardial fluid, ductal lavage fluid, nipple aspirate, lymph (e.g., disseminated tumor cells of the lymph node), bone marrow aspirate, buccal swabs, saliva, urine, stool (i.e., feces), sputum, bronchial lavage fluid, tears, fine needle aspirate (e.g., harvested by random periareolar fine needle aspiration), any other bodily fluid, xenograft sample, a tissue sample such as a biopsy of a site of the xenograft (e.g., needle biopsy), and cellular extracts thereof.
  • PBMCs peripheral blood mononuclear cells
  • PMN polymorphonuclear
  • the sample comprises peripheral blood mononuclear cells (PBMCs) or a biopsy of a site of the xenograft.
  • PBMCs peripheral blood mononuclear cells
  • the mention of one or more method steps does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified.
  • Such other materials not described herein can include, but are not limited to, materials that are developed after the time of the development of the invention, for example. Any dimensions listed in the various drawings are for illustrative purposes only and are not intended to be limiting. Other dimensions and proportions are contemplated and intended to be included within the scope of the invention.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, 50 314164997v1 Attorney Docket No: 243735.000437 IGHM, and IGHD, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, eight, or nine genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, or nine genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs); and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, eight, or 51 314164997v1 Attorney Docket No: 243735.000437 nine genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1.
  • the expression levels of the two, three, four, five, six, seven, eight, or nine genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, four or more genes, or five genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY. In some embodiments, the expression levels of the three or more genes, four or more genes, or five genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, or five genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY. In some embodiments, the expression levels of the two, three, four, or five genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more 52 314164997v1 Attorney Docket No: 243735.000437 genes, or nine genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, eight, or nine genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, or nine genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • the expression levels are determined for three or more genes, four or more genes, or five genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1. In some embodiments, the expression levels of the three or more genes, four or more genes, or five genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, or five genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1. In some embodiments, the expression levels of the two, three, four, or five genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3, 53 314164997v1 Attorney Docket No: 243735.000437 wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, or eight genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, or eight genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, or eight genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3.
  • the expression levels of the two, three, four, five, six, seven, or eight genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes are compared to a corresponding 54 314164997v1 Attorney Docket No: 243735.000437 control.
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A.
  • the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes selected from human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1.
  • the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: 55 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD3G, THEMIS, CD5, and CD6, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, or four genes selected from human genes CD3G, THEMIS, CD5, and CD6. In some embodiments, the expression levels of the three or more genes, or four genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, or four genes selected from human genes CD3G, THEMIS, CD5, and CD6. In some embodiments, the expression levels of the two, three, or four genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of CD8B and/or CD8A, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the gene(s) determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRG1, GZMK, CST7, and GZMH, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, or four genes selected from human genes KLRG1, GZMK, CST7, and GZMH.
  • the expression levels of the three or more genes, or four genes are compared to a corresponding control.
  • the expression levels are determined for two, three, or four genes selected from human genes KLRG1, GZMK, CST7, and GZMH.
  • the expression levels of the two, three, or four genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD4, CD40LG, CCR2, CCR6, and DPP4, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels of the two, three, four, five, six, or seven genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: 58 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, or four genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67. In some embodiments, the expression levels of the three or more genes, or four genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, or four genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67. In some embodiments, the expression levels of the two, three, or four genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GZMA, PRF1, and FASLG, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for two or three genes selected from human genes GZMA, PRF1, and FASLG. In some embodiments, the expression levels of the two or three genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes TOP2A, TYMS, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and 59 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or
  • the expression levels are determined for two or three genes selected from human genes TOP2A, TYMS, and MKI67. In some embodiments, the expression levels of the two or three genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • the expression levels are determined for three or more genes, four or more genes, or five genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1. In some embodiments, the expression levels of the three or more genes, four or more genes, or five genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, or five genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1. In some embodiments, the expression levels of the two, three, four, or five genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, or six genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, or six genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, or six genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2.
  • the expression levels of the two, three, four, five, or six genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • PBMCs peripheral blood mononuclear cells
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, or eight genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, or eight genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, or eight genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2.
  • the expression levels of the two, three, four, five, six, seven, or eight genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: 61 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TR
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty- two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty- nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, fifty-nine
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, ST
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-seven or more genes, thirty-
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty- seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty- 68 314164997v1 Attorney Docket No: 243735.000437 five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: 69 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- 71 314164997v1 Attorney Docket No: 243735.000437 seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP.
  • the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, or ten genes selected from porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, or ten genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, or ten genes selected from porcine genes C3, CXCL2, 73 314164997v1 Attorney Docket No: 243735.000437 C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A.
  • the expression levels of the two, three, four, five, six, seven, eight, nine, or ten genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control.
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, or twenty-three genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1.
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, or twenty-three genes are compared to a corresponding control.
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, or twenty-three genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, 74 314164997v1 Attorney Docket No: 243735.000437 CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1.
  • the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, or twenty-three genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, or twenty-five genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1.
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, or twenty-five genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1.
  • the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, or twenty-five genes are compared to a corresponding control.
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, R
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four 76 314164997v1 Attorney Docket No: 243735.000437 or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-seven or more genes, thirty-
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more 79 314164997v1 Attorney Docket No: 243735.000437 genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-seven or more genes, thirty-
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty- seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty- five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, fifty-nine
  • the expression levels of the two, three, four, five, six, seven, eight, nine, ten, 86 314164997v1 Attorney Docket No: 243735.000437 eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty- one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3,
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-seven or more genes, thirty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty- one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty- nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, fifty- nine,
  • a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G
  • the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-e
  • the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or 97 314164997v1 Attorney Docket No: 243735.000437 more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty- two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, twenty-n
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty- three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty- seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty- six, sixty-seven, sixty-eight, sixty-
  • the sample can comprise functionally-related cells or adjacent cells.
  • Samples may comprise complex populations of cells, which can be assayed together as a population or separated into sub-populations.
  • Cellular and acellular samples can be separated by elutriation, centrifugation, centrifugation with Hypaque, apheresis, density gradient separation, affinity selection, panning, FACS, etc.
  • a homogeneous population of cells may be obtained via use of antibodies specific for markers identified with particular cell types. Instead, a heterogeneous cell population can be used.
  • Cells may be separated using filters. For example, whole blood can be applied to filters that contain pore sizes that select for the desired cell class or type.
  • Cells can be filtered out of diluted, whole blood after the lysis of red blood cells through the use of filters with pore sizes (e.g., between 5 to 10 ⁇ m, see U.S. Pat. No.09/790,673).
  • Other devices can separate cells from the bloodstream (see e.g., Demirci, Toner, Direct etch method for microfluidic channel and nanoheight post-fabrication by picoliter droplets, Applied Physics Letters, 2006; 88 (5), 053117; and Irimia, Geba, Toner, Universal microfluidic gradient generator, Analytical Chemistry, 2006; 78: 3472-3477).
  • a sample Once a sample is obtained, it may be used directly, frozen, or maintained in suitable culture medium.
  • a blood sample may be optionally pre- treated or processed before enrichment.
  • pre-treatment steps may include the addition of a reagent (e.g., a fixant, a stabilizer, a preservative, a lysing reagent, a diluent, a magnetic property regulating reagent, an anti-apoptotic reagent, an anti-thrombotic reagent, an 101 314164997v1 Attorney Docket No: 243735.000437 anti-coagulation reagent, a buffering reagent, a cross-linking reagent, an osmolality regulating reagent, and/or a pH regulating reagent).
  • a reagent e.g., a fixant, a stabilizer, a preservative, a lysing reagent, a diluent, a magnetic property regulating reagent, an anti-apoptotic reagent, an anti-thrombotic reagent, an 101
  • a preservative e.g., an anti-coagulation agent and/or a stabilizer
  • Addition of a preservative allows for extended time for analysis and/or detection.
  • a sample e.g., a blood sample
  • a sample may be analyzed using any of the methods and systems herein within over 1 week, 1 week, 6 days, 5 days, 4 days, 3 days, 2 days, 1 day, 18 hours, 12 hours, 6 hours, 3 hours, 2 hours, 1 hour, or less than 1 hour from the time the sample is obtained.
  • a blood sample may be combined with an agent that selectively lyses one or more components (e.g., one or more cells) in a blood sample.
  • an agent that selectively lyses one or more components (e.g., one or more cells) in a blood sample.
  • enucleated red blood cells and/or platelets can be selectively lysed to generate a sample enriched in nucleated cells.
  • cells of interest can be separated from the sample using methods and/or processes known in the art.
  • the amount of sample can vary depending upon the condition being screened and the subject size.
  • up to 50 mL, 40 mL, 30 mL, 20 mL, 15 mL, 10 mL, 9 mL, 8 mL, 7 mL, 6 mL, 5 mL, 4 mL, 3 mL, 2 mL, or 1 mL of a sample is obtained. In some embodiments, about 1-50 mL, about 2-40 mL, about 3-30 mL, or about 4-20 mL of sample is obtained.
  • more than 5 mL, 10 mL, 15 mL, 20 mL, 25 mL, 30 mL, 35 mL, 40 mL, 45 mL, 50 mL, 55 mL, 60 mL, 65 mL, 70 mL, 75 mL, 80 mL, 85 mL, 90 mL, 95 mL, or 100 mL of a sample is obtained.
  • the method further comprises step c) (i) determining that the subject has a xenograft rejection or is at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are increased as compared to the control by 1.5-fold or more; or (ii) determining that the subject does not have a xenograft rejection or is not at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are decreased, not increased or increased by less than 1.5-fold as compared to the control.
  • the expression levels are determined for at least two of the genes, at least three of the genes, at least four of the genes, at least five of the genes, at least six of the genes, at least seven of the genes, at least eight of the genes, at least nine of the genes, at 102 314164997v1 Attorney Docket No: 243735.000437 least ten of the genes, at least eleven of the genes, at least twelve of the genes, at least thirteen of the genes, at least fourteen of the genes, at least fifteen of the genes, at least sixteen of the genes, at least seventeen of the genes, at least eighteen of the genes, at least nineteen of the genes, at least twenty of the genes, at least twenty-one of the genes, at least twenty-two of the genes, at least twenty-three of the genes, at least twenty-four of the genes, at least twenty-five of the genes, at least twenty-six of the genes, at least twenty-seven of the genes, at least twenty-eight of the genes, at least twenty-nine
  • the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, fifty-nine
  • the expression levels are determined for each of the genes in step (a).
  • the control is a predetermined value or a value determined from a sample taken from the subject before the xenograft transplantation.
  • the control is a predetermined value or a value determined from a sample taken from the xenograft before the xenograft transplantation.
  • the corresponding control is a predetermined standard.
  • the corresponding control is a level or proportion of the expression levels of the one or more genes determined in a sample obtained from the subject at an earlier time point.
  • the corresponding control is generated in one or more subjects that did not undergo xenograft transplantation. In some embodiments, the corresponding control is generated 106 314164997v1 Attorney Docket No: 243735.000437 in one or more subjects on the day of xenograft transplantation (POD 0). In some embodiments, the corresponding control is generated in one or more subjects before the day of xenograft transplantation (before POD 0). In some embodiments, the corresponding control is generated is generated in the xenograft (e.g., organ, tissue, or cells) to be transplanted on the day of xenograft transplantation (POD 0).
  • the xenograft e.g., organ, tissue, or cells
  • the corresponding control is generated in the xenograft (e.g., organ, tissue, or cells) to be transplanted before the day of xenograft transplantation (before POD 0).
  • the method further comprises administering to the subject a treatment that targets plasma cells and/or a complement inhibitory agent, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection.
  • the method further comprises administering to the subject a treatment that targets T cells when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection.
  • the method further comprises administering to the subject a treatment that targets plasma cells, a treatment that targets T cells, a complement inhibitory agent, or a combination thereof, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection.
  • Treatments that target plasma cells may include, but are not limited to, proteasome inhibitors (e.g., bortezomib (Velcade) and carfilzomib (Kyprolis)), immunomodulatory drugs (IMiDs) (e.g., lenalidomide (Revlimid) and pomalidomide (Pomalyst)), monoclonal antibodies (e.g., daratumumab (Darzalex) and isatuximab (Sarclisa)), antibody-drug conjugates (e.g., Belantamab mafodotin (Blenrep)), B-cell maturation antigen (BCMA) targeted therapies (e.g., idecabtagene vicleucel (Abecma) and bispecific antibodies like teclistamab (Tecvayli)).
  • proteasome inhibitors e.g., bortezomib (Velcade) and carfil
  • the treatment that targets plasma cells is plasmapheresis.
  • Plasmapheresis i.e., plasma exchange
  • Treatments that target T cells may comprise CAR T-cell therapy, checkpoint inhibitors (e.g., inhibitors that target PD-1, PD-L1, and/or CTLA-4), T-cell transfer therapy (i.e., adoptive cell therapy), immunosuppressive drugs (e.g., cyclosporine and tacrolimus), vaccines, and bi- specific T-cell engagers (BiTEs).
  • checkpoint inhibitors e.g., inhibitors that target PD-1, PD-L1, and/or CTLA-4
  • T-cell transfer therapy i.e., adoptive cell therapy
  • immunosuppressive drugs e.g., cyclosporine and tacrolimus
  • vaccines e.g., cyclosporine and tacrolimus
  • BiTEs bi- specific T-cell engagers
  • Rabbit anti-thymocyte globulin is an immunosuppressive medication that targets T cells.
  • Other treatments to target T cells may 107 314164997v1 Attorney Docket No: 243735.000437 include, but are not limited to, horse anti-thymocyte globulin (hATG), alemtuzumab (Campath), basiliximab (Simulect), daclizumab, and muromonab-CD3 (Orthoclone OKT3).
  • the treatment that targets T cells is rabbit anti-thymocyte globulin (rATG).
  • the complement system consisting of approximately 30 proteins, is a crucial component of the immune system's effector mechanisms. It is primarily activated through two pathways: the classical pathway, which typically depends on antibodies, and the alternative pathway, which generally operates independently of antibodies. Activation through either pathway results in the formation of C3 convertase, the central enzymatic complex of the cascade. This complex cleaves serum C3 into C3a and C3b. C3b binds covalently at the activation site, facilitating further C3 convertase generation in an amplification loop. C3b, along with C4b (produced exclusively via the classical pathway), and their breakdown products serve as significant opsonins.
  • C3 convertases also contribute to the formation of C5 convertase, which cleaves C5 into C5a and C5b.
  • C5a possesses strong pro-inflammatory and chemotactic properties, enabling it to recruit and activate immune effector cells.
  • the formation of C5b triggers the terminal complement pathway, leading to the sequential assembly of complement proteins C6, C7, C8, and (C9)n, forming the membrane attack complex (MAC or C5b-9).
  • MAC MAC-associated kinase
  • CR1 complement receptor 1
  • DAF decay-accelerating factor
  • MCP membrane cofactor protein
  • the rodent protein CR1 is a broadly 108 314164997v1 Attorney Docket No: 243735.000437 distributed inhibitor of complement activation, functioning similarly to both DAF and MCP.
  • complement activation blocker-2 CAB-2
  • CAB-2 complement activation blocker-2
  • the complement inhibitory agent is a C3 inhibitor.
  • the method further comprises administering an immunosuppressive agent to the subject when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection.
  • the treatment may comprise an immunosuppressive agent, including, but not limited to a Janus kinase inhibitor, a corticosteroid, a mTOR inhibitor, a calcineurin inhibitor, an inosine-5′-monophosphate dehydrogenase (IMPDH) inhibitor, a biologic (e.g., infliximab, adalimumab, abatacept, certolizumab, anakinra, etanercept, golimumab, natalizumab, tocilizumab, ustekinumab, rituximab, secukinumab, vedolizumab, ixekizumab), or a monoclonal antibody (e.g., daclizumab, basilivimab).
  • an immunosuppressive agent including, but not limited to a Janus kinase inhibitor, a corticosteroid, a mTOR inhibitor, a calcineur
  • the treatment may comprise an anti-inflammatory agent, including, but not limited to a steroidal anti-inflammatory agent or a non-steroidal anti- inflammatory agent (e.g., naproxen, ketorolac, diclofenac, meloxicam, etodolac, esomeprazole, misoprostol, ibuprofen, famotidine, nabumetone, mefenamic acid, indomethacin, piroxicam, sulindac, ketoprofen, flurbiprofen, diflunisal, oxaprozin, nabumetone, tolmetin).
  • a steroidal anti-inflammatory agent e.g., naproxen, ketorolac, diclofenac, meloxicam, etodolac, esomeprazole, misoprostol, ibuprofen, famotidine, nabumetone, mefenamic acid, indome
  • the treatment may comprise an antibacterial or antiviral therapy.
  • the method further comprises administering to the subject a treatment for elevated immune activity, inflammation, xenograft damage or dysfunction, or rejection response.
  • the method further comprises administering to the subject a treatment for the xenograft damage or dysfunction, or rejection response based on the type of xenograft damage or dysfunction, or rejection response.
  • the method comprises administering a treatment to the subject when elevated immune activity, inflammation, xenograft damage or dysfunction, or rejection response is detected. Detection of elevated immune activity, inflammation, xenograft damage or dysfunction, or rejection response may be in accordance with the methods described herein.
  • the type of xenograft damage or dysfunction, or rejection response described herein is an antibody-mediated rejection, acute cellular rejection (e.g., T-cell mediated rejection), podocyte damage, ischemia damage, and any other cellular damage.
  • the method comprises determining cell type(s) of recipient- derived cells present in a graft after the graft has been transplanted in a subject.
  • Cellular origins are associated with different types of tissue, for example, mature B-cells can be associated with blood or bone marrow; na ⁇ ve B-cells can be associated with blood or bone marrow; biliary epithelial cells can be associated with liver tissue; breast basal cells can be associated with breast tissue; breast luminal cells can be associated with breast tissue; bulk endothelial cells can be associated with blood vessels; bulk epithelial cells can be associated with any epithelia; bulk immune cells can be associated with immune organs; cardiomyocytes can be associated with heart tissue; cardiopulmonary endothelial cells can be associated with heart or lung tissues; colon epithelial cells can be associated with colon tissue; dermal epithelial cells can be associated with skin tissue; granulocytes can be associated with blood or bone marrow; hepatocytes can be associated with liver tissue; keratinocytes can be associated with skin tissue; kidney epithelial cells can be associated with kidney tissue; liver endothelial cells can be associated with liver tissue; liver
  • the cell type(s) identified may be indicative of the type of xenograft damage or dysfunction, or rejection response.
  • the method may involve determining the type of xenograft damage or dysfunction, or rejection response is an antibody- mediated rejection when the cell type is determined to be endothelial cells.
  • the invention herein provides methods for diagnosis of xenograft or graft rejection. Xenograft or graft rejection encompasses both acute and chronic xenograft or graft rejection.
  • Acute xenograft or graft rejection is the rejection by the immune system of a xenograft or graft transplant recipient when the transplanted xenograft or graft is 110 314164997v1 Attorney Docket No: 243735.000437 immunologically foreign.
  • Acute xenograft or graft rejection is characterized by infiltration of the transplanted xenograft or graft by immune cells of the recipient, which carry out their effector function and work to destroy the transplanted xenograft or graft.
  • Acute xenograft or graft rejection onset is rapid and generally occurs in humans within a few weeks after xenograft or graft transplant surgery.
  • acute xenograft or graft rejection can be suppressed or inhibited with immunosuppressive agents such as a Janus kinase inhibitor, a corticosteroid, a mTOR inhibitor, a calcineurin inhibitor, an inosine-5′-monophosphate dehydrogenase (IMPDH) inhibitor, a biologic (e.g., infliximab, adalimumab, abatacept, certolizumab, anakinra, etanercept, golimumab, natalizumab, tocilizumab, ustekinumab, rituximab, secukinumab, vedolizumab, ixekizumab), or a monoclonal antibody (e.g., daclizumab, basilivimab), or the like.
  • immunosuppressive agents such as a Janus kinase inhibitor, a corticosteroid, a
  • Chronic xenograft or graft rejection generally occurs in humans within months to years after transplant, after successful immunosuppression of acute xenograft or graft rejection. Fibrosis is a common factor in chronic xenograft or graft rejection of organ xenograft or graft transplants. Chronic xenograft or graft rejection can typically be described by a range of disorders that are characteristic of the particular organ.
  • disorders may include fibrotic atherosclerosis; in lung transplants, such disorders may include fibroproliferative destruction of the airway (e.g., bronchiolitis obliterans); in liver transplants, such disorders may include disappearing bile duct syndrome; and in kidney transplants, such disorders may include obstructive nephropathy, nephrosclerosis, or tubulointerstitial nephropathy.
  • Chronic xenograft or graft rejection can also be characterized by denervation of the transplanted tissue, ischemic insult, hypertension accompanying immunosuppressive drugs, and hyperlipidemia.
  • the invention herein provides methods for diagnosis of xenograft damage or dysfunction.
  • Xenograft damage or dysfunction may include, but is not limited to, viral infection, ischemic injury, reperfusion injury, peri-operative ischemia, hypertension, injuries due to reactive oxygen species, injuries caused by pharmaceutical agents, and physiological stress.
  • the xenograft transplant may comprise any cell, tissue, or organ from a donor of one species to a recipient of a different species.
  • the donor may be a non- human primate, cat, cow, dog, goat, horse, pig, rodent, sheep, reptile, or another non-human animal.
  • the rodent may be a beaver, guinea pig, hamster, mouse, 111 314164997v1 Attorney Docket No: 243735.000437 porcupine, prairie dog, rat, squirrel, or another rodent.
  • the non-human primate may be an ape or a monkey.
  • the non-human primate may be an African green monkey, baboon, bonobo, capuchin monkey, chimpanzee, cynomolgus monkey, gorilla, marmoset, orangutan, owl monkey, pig-tailed monkey, rhesus monkey, spider monkey, squirrel monkey, vervet monkey, or another non-human primate.
  • the donor is a pig.
  • the donor is a genetically modified animal.
  • the donor is a genetically modified pig.
  • the xenotransplant recipient may be a human, and the xenotransplant donor may be a pig.
  • the pig may be a Sus scrofa, Sus scrofa domesticus, Phacochoerus aethiopicus, Potamochoerus porcus, or Babirousa babyrussa species.
  • the pig may be a Hanford pig, mini- or micro-pig, Yucatan pig, Yucatan micro pig, Sinclair pig, Gottingen pig, Duroc pig, Yorkshire pig, Landrace pig, or a combination, hybrid, or cross-species thereof.
  • the transplant may be any biological material comprising cells that have DNA and that can be transplanted from a donor to a subject.
  • the cells may be organized as a tissue or portion thereof, an organ or portion thereof, or a population of cells not organized as a tissue or organ.
  • the population of cells may be a population of the same cell type or of different cell types.
  • the xenograft transplant may comprise an organ or portion thereof.
  • the xenograft transplant comprises a kidney. In one embodiment, the xenograft transplant comprises a heart. [00248] In some embodiments, the xenograft transplant comprises a tissue or portion thereof.
  • the xenograft transplant comprises a population of cells.
  • the subject has received a kidney, heart, lung, liver, bone marrow, pancreas, or islet cell transplantation from a porcine donor.
  • the cell comprises an adipocyte, adrenal cell, antigen presenting cell, aortic endothelial cell, aortic smooth muscle cell, astrocyte, basophil, B cell, bladder cell, blood cell, blood precursor cell, bone cell, bone precursor cell, cardiac muscle cell, cardiac myocyte, cervical cell, chondrocyte, ciliated cell, cumulus cell, columnar epithelial cell, cone cell, dopaminergic cell, egg cell, embryonic stem cell, endothelial cell, endometrial cell, epidermal cell, epithelial cell, erythrocyte, fibroblast cell, fibroblast and fetal fibroblast, follicle cell, germ cell, glial cell, goblet cell, granulosa cell, hair cell, heart cell, hematopoietic cell, hepatocyte, Islets of Langerhans cell, keratinized epithelial cell, keratinocyte, kidney cell, Kupffer cell,
  • the subject has received a cartilage, bone, adipose, pancreatic islet, muscle, vascular tissue, heart valve, retinal tissue, neural tissue, or corneal tissue transplantation from a porcine donor.
  • the tissue comprises adipose, areolar, blood, bone, bone marrow, brown adipose, cancellous, cartilage, cartilaginous, cavernous, chondroid, chromaffin, connective tissue, dartoic, elastic, epithelial, epithelium, fatty, fibro-hyaline, fibrous, Gamgee, gelatinous, granulation, gut-associated lymphoid, Haller's vascular, hard hemopoietic, indifferent, interstitial, investing, islet, lymphatic, lymphoid, mesenchymal, mesonephric, 113 314164997v1 Attorney Docket No: 243735.000437 mucous connect
  • the subject has received a heart, liver, kidney, pancreas, lung, thyroid or skin organ transplantation from a porcine donor.
  • the organ comprises adrenal glands, anus, bladder, blood, blood vessels, bones, brain, cartilage, ears, esophagus, eye, glands, gums, hair, heart, hypothalamus, intestines, kidneys, large intestine, ligaments, lips, liver, lungs, lymph, lymph nodes, lymph vessels, mammary glands, mouth, nails, nose, ovaries, oviducts, pancreas, penis, pharynx, pituitary, pylorus, rectum, salivary glands, seminal vesicles, skeletal muscles, skin, small intestine, smooth muscles, spinal cord, spleen, stomach, suprarenal capsule, teeth, tendons, testes, thymus gland, thyroid gland, tongue, tonsils, trachea,
  • the expression levels are determined based on RNA expression, protein expression, epigenetic regulation, or a combination thereof.
  • the expression levels are determined using RNA sequencing, targeted RNA panel, a quantitative PCR assay, an antibody-based method, an epigenetic assay, a single-cell technology, a spatial transcriptomics technology, or a multiplexed approach combining RNA and protein measurements.
  • the antibody-based method is flow cytometry, immunohistochemistry, or enzyme-linked immunosorbent assay (ELISA).
  • the single-cell technology is single-cell RNA sequencing (scRNA-seq) or cellular indexing of transcriptomes and epitopes (CITE-seq).
  • the expression level may be determined at the protein, mRNA, and/or gene level using any suitable methodology known in the art.
  • the expression level is determined by measuring protein expression level. Protein expression level may be determined using any conventional methodology known to those of skill in the art.
  • protein level may be determined using an enzyme-linked immunosorbent assay 114 314164997v1 Attorney Docket No: 243735.000437 (ELISA), a proteomic array, flow cytometry, a radioimmunoassay, a Western blot, an activity assay, and/or a multiplex electrochemiluminescence-based assay.
  • ELISA enzyme-linked immunosorbent assay
  • proteomic array e.g., a proteomic array
  • flow cytometry e.g., a proteomic array
  • flow cytometry e.g., a proteomic array
  • flow cytometry e.g., a radioimmunoassay
  • a Western blot e.g., a protein expression level
  • protein expression level may be determined using approaches comprising, e.g., Biuret methods, UV absorption, Colorimetric dye-based methods, Fluorescent dye-based methods, Turbimetric method, BCA
  • the expression level is determined by measuring the expression level of nucleic acids.
  • Nucleic acids from samples that may be analyzed include, but are not limited to, DNA (e.g., double-stranded, single-stranded, or single-stranded hairpins), DNA/RNA hybrids, or RNA (e.g., mRNA, miRNA, or hairpins). Examples of genetic analyses that can be performed on nucleic acids include, but are not limited to, sequencing, STR detection, SNP detection, gene expression, and RNA expression analysis.
  • the expression level is determined by measuring mRNA expression level. mRNA expression level may be determined using any conventional methodology known to those of skill in the art.
  • mRNA expression level may be determined using, e.g., RNA-seq, real-time polymerase chain reaction (RT-PCR) (e.g., quantitative RT-PCR), Northern blot analysis and/or a Ribonuclease protection assay.
  • RT-PCR real-time polymerase chain reaction
  • detection, identification, and/or quantitation of the donor- specific markers or the recipient-specific markers may be performed by mapping one or more nucleic acids (e.g., RNA or DNA) to the species genome to determine whether the nucleic acids come from the transplant donor or the recipient.
  • less than 1 pg, 5 pg, 10 pg, 15 pg, 20 pg, 25 pg, 30 pg, 40 pg, 50 pg, 100 pg, 200 pg, 500 pg, 1 ng, 5 ng, 10 ng, 20 ng, 30 ng, 40 ng, 50 ng, 100 ng, 200 ng, 500 ng, 1 ug, 5 ug, 10 ug, 20 ug, 30 ug, 40 ug, 50 ug, 100 ug, 200 ug, 500 ug, or 1 mg of nucleic acids are obtained from the sample for measuring expression level.
  • nucleic acids are obtained from the sample for measuring expression level.
  • the methods described herein can be used to measure expression level for one or more target nucleic acid molecule(s).
  • the methods described herein can analyze at least 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 50,000, 115 314164997v1 Attorney Docket No: 243735.000437 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 2,000,000, 3,000,000, 4,000,000, or 5,000,000 different target nucleic acids. [00267] In some embodiments, the methods described herein can be used to differentiate between target nucleic acids that diverge from a different nucleic acid by 1 nt.
  • the methods described herein are used to differentiate between target nucleic acids that diverge from a different nucleic acid by 1 nt or at least 1 nt, 2 nt, 3 nt, 4 nt, 5 nt, 6 nt, 7 nt, 8 nt, 9 nt, 10 nt, 15 nt, 20 nt, 25 nt, 30 nt, 35 nt, 40 nt, 45 nt, or 50 nt.
  • additional genes beyond those listed herein may be assayed and may include additional genes whose expression levels can be used to determine elevated immune activity, inflammation, xenograft damage or dysfunction, or rejection response as well as additional genes whose expression levels can be used to evaluate additional xenotransplant characteristics, including, but not limited to: a graft tolerant phenotype in a subject, chronic rejection in blood, immunosuppressive drug toxicity or adverse side effects including drug- induced hypertension, age- or body mass index- associated genes that correlate with renal pathology or differences in recipient age-related graft acceptance, immune tolerance markers in whole blood, and/or genes with immune modulatory roles that may play a role in transplant outcomes.
  • the sample is collected from the subject 3 days after the xenograft transplantation.
  • the response of the subject to the xenograft or graft transplantation is monitored. The monitoring comprises determining expression levels of one or more genes in a sample obtained from the subject at one or more time points after receiving the xenograft or graft transplantation.
  • the method further comprises determining expression levels of one or more genes in a sample obtained from the subject at multiple time points after receiving the xenograft or graft transplantation.
  • the time points may be the same day or one or more days between and including postoperative day (POD) 0 (day of receiving the xenograft transplantation) through POD 150 or greater, such as POD 0, POD 1, POD 2, POD 3, POD 4, POD 5, POD 6, POD 7, POD 8 POD 9, POD 10, POD 11, POD 12, POD 13, POD 14, POD 15, POD 16, POD 17, POD 18, POD 19, POD 20, POD 21, POD 22, POD 23, POD 24, POD 25, POD 26, POD 27, POD 28, POD 29, POD 30, POD 31, POD 32, POD 33, POD 34, POD 35, POD 36, POD 37, POD 38, POD 39, 116 314164997v1 Attorney Docket No: 243735.000437 POD 40, POD 41, POD 42, POD 43, POD 44, POD 45, POD 46, POD 47, POD 48, POD 49, POD 50, POD 51, POD 52, POD
  • the subject may be monitored at time points later than POD 150.
  • the samples are collected from the subject at least one time a week. In some embodiments, the samples are collected from the subject at least two times a week. In some embodiments, the samples are collected from the subject at least three times a week. In some embodiments, the samples are collected from the subject at least four times a week. In some embodiments, the samples are collected from the subject at least five times a week. In some embodiments, the samples are collected from the subject at least six times a week. In some embodiments, the samples are collected from the subject at least seven times a week.
  • the method for treating the xenograft damage or dysfunction, or rejection response based on the type of xenograft damage or dysfunction, or rejection response comprises administering a treatment for xenograft damage or dysfunction, or rejection response.
  • the methods described herein may further comprise monitoring the efficacy of the treatment. The monitoring may comprise determining expression levels of one or more genes in a sample obtained from the subject.
  • the method comprises obtaining two or more samples from the subject at different time points after the xenograft transplantation and repeating the method for each sample.
  • the methods described herein may be used in combination with one or more other assessments for detecting a xenograft rejection in a subject, or monitoring a 117 314164997v1 Attorney Docket No: 243735.000437 xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject.
  • the other assessment may be the Banff Criteria described in Loupy, A. et al. The Banff 2019 Kidney Meeting Report (I): Updates on and clarification of criteria for T cell– and antibody-mediated rejection. Am. J. Transplant.20, 2318–2331 (2020), which is incorporated herein by reference in its entirety. Table 1.
  • Reagents of interest can include reagents designed for use in production of the above-described gene panels.
  • One type of such reagent can be an array of probe nucleic acids in which the genes of interest described herein can be detected.
  • array formats are known in the art, with a wide variety of different probe structures, substrate compositions and attachment technologies (e.g., dot blot arrays, microarrays, etc.).
  • Representative array structures of interest include those described in U.S. Pat.
  • the arrays include probes for at least 1 of the genes listed in Table 1. In certain embodiments, probes for any combination of genes in Table 1 may be used.
  • the number of genes that are from Table 1 that are represented on the array is at least 2, at least 3, at least 4, at least 5, at least 8 or more, including all of the genes listed in 127 314164997v1 Attorney Docket No: 243735.000437 Table 1.
  • the number of genes that are from Table 1 that are represented on the array is at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least
  • the arrays may include only those genes that are listed in Table 1 or additional genes that are not listed in Table 1, such as probes for genes whose expression pattern can be used to evaluate additional transplant characteristics, including but not limited to: chronic allograft injury (chronic rejection) in blood; immunosuppressive drug toxicity or adverse side effects including drug-induced hypertension; age or body mass index associated genes that correlate with renal pathology or account for differences in recipient age-related graft acceptance; immune tolerance markers in whole blood; genes with immune modulatory roles that may play a role in transplant outcomes; as well as other array assay function related genes, e.g., for assessing sample quality (3’- to 5’-bias in probe location), sampling error in biopsy-based studies, cell surface markers, and normalizing genes for calibrating hybridization results; and the like.
  • probes for genes whose expression pattern can be used to evaluate additional transplant characteristics including but not limited to: chronic allograft injury (chronic rejection) in blood; immunosuppressive drug toxicity or adverse side effects including drug-induced hypertension; age or body mass index
  • the percent of additional genes 128 314164997v1 Attorney Docket No: 243735.000437 that are represented and are not directly or indirectly related to transplantation does not exceed about 50%, and may not exceed about 25%.
  • additional genes are included, a great majority of genes in the collection are transplant characterization genes, whereby a great majority is meant to encompass at least about 75%, typically at least about 80%, and sometimes at least about 85%, at least about 90%, at least about 95%, or higher, including embodiments where 100% of the genes in the collection are phenotype-determinative genes.
  • Transplant characterization genes can be genes whose expression can be employed to characterize transplant function in some manner (e.g., presence of rejection, etc.).
  • Another type of reagent that can be included in the kits described herein is a collection of gene-specific primers that is designed to selectively amplify such genes (e.g., using a PCR- based technique, e.g., real-time RT-PCR). Gene specific primers and methods for using the same are described in U.S. Pat. No.5,994,076, the disclosure of which is herein incorporated by reference.
  • genes that have primers for at least 1 of the genes listed in Table 1 are collections of gene specific primers that have primers for at least 1 of the genes listed in Table 1, and in particular a plurality of these genes (e.g., at least 2, at least 4, at least 8 or more).
  • all of genes that are from Table 1 have primers in the collection
  • the gene specific primer collections may include only those genes that are listed in Table 1, or they may include primers for additional genes that are not listed in Table 1, such as probes for genes whose expression pattern can be used to evaluate additional transplant characteristics, including, but not limited to: chronic allograft injury (chronic rejection) in blood; immunosuppressive drug toxicity or adverse side effects including drug-induced hypertension; age or body mass index associated genes that correlate with renal pathology or account for differences in recipient age-related graft acceptance; immune tolerance markers in whole blood; genes with immune modulatory roles that may play a role in transplant outcomes; as well as other array assay function related genes, e.g., for assessing sample quality (3’- to 5’-bia
  • the percent of additional genes that are represented and are not directly or indirectly related to transplantation does not exceed about 50%, and may not exceed about 25%.
  • a great majority of genes in the collection are transplant characterization genes, whereby a great majority is meant to encompass at least about 75%, typically at least about 80%, 129 314164997v1 Attorney Docket No: 243735.000437 and sometimes at least about 85%, at least about 90%, at least about 95% or higher, including embodiments where 100% of the genes in the collection are phenotype-determinative genes.
  • the systems and kits described herein may include the above-described arrays and/or gene specific primer collections.
  • the systems and kits may further include one or more additional reagents employed in the various methods, such as primers for generating target nucleic acids, dNTPs and/or rNTPs, which may be either premixed or separate, one or more uniquely labeled dNTPs and/or rNTPs (e.g., biotinylated or Cy3 or Cy5 tagged dNTPs), gold or silver particles with different scattering spectra, other post synthesis labeling reagents (e.g., chemically active derivatives of fluorescent dyes), enzymes (e.g., reverse transcriptases, DNA polymerases, RNA polymerases, and the like), various buffer mediums (e.g., hybridization and washing buffers), prefabricated probe arrays, labeled probe purification reagents and components (e.g., spin columns, etc.), signal generation and detection reagents (e.g., streptavidin-alkaline phosphatase conjugate, chemiflu
  • kits described herein may also include in some embodiments, a reference or control that can be employed, e.g., by a suitable computing means, to make a xenograft status determination based on an “input” expression profile, e.g., that has been determined with the above described gene signatures.
  • Representative xenograft status determination elements include databases of gene expression profiles, e.g., reference or control profiles, as described herein.
  • the kits may further include instructions for practicing the described methods. These instructions may be present in the kits in a variety of forms, one or more of which may be present in the kit.
  • kits comprising: 1) one or more sets of probe nucleic acids useful for detecting two or more genes selected from: 130 314164997v1 Attorney Docket No: 243735.000437 i.
  • human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD ii. human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1; iii. human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY; iv. human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14; v. human genes IRF7, TLR7, TLR9, MYD88, and STAT1; vi.
  • human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3 vii. human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A; viii. human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1; ix. human genes CD3G, THEMIS, CD5, and CD6; x. human genes CD8B, and CD8A; xi.
  • human genes KLRG1, GZMK, CST7, and GZMH KLRG1, GZMK, CST7, and GZMH; xii. human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7; xiii. human genes CD4, CD40LG, CCR2, CCR6, DPP4; xiv. human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA; xv. human genes ITGAE, CD38, TNFRSF9, and MKI67; xvi. human genes GZMA, PRF1, and FASLG; xvii. human genes TOP2A, TYMS, and MKI67; xviii.
  • porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A,
  • Example 1 PBMC scRNA-seq in two human decedents post-xenotransplant.
  • 26 longitudinal blood scRNA-seq Figure 17
  • time points were integrated (14 for D1 and 12 for D2) to a two-dimensional (2D)-space embedding and Leiden clustering was used to assign peripheral blood mononuclear cell (PBMC) types ( Figure 7A), with the dynamic proportion of these cell types shown ( Figures 2A-C).
  • PBMC peripheral blood mononuclear cell
  • T cells in D1 are central memory T cells, which circulate to nonlymphoid sites (Martin, et al., 2018; Golubovskaya, et al., 2016).
  • B cell subtypes were identified across the two xeno time courses using their marker genes ( Figures 7G-H) and it was found that the B cell increase in the early time points of D1 mostly corresponds to TCL1 + B cells and, to a lesser degree, CD70 + B cells ( Figure 2C). TCL1 is commonly associated with immune tolerance (Brinas, et al., 2021).
  • DEA differential expression analysis
  • RNA-seq DEA Immune activation signals from the bulk RNA-seq DEA are clear in D1 with pathways such as neutrophil extracellular trap formation and tumor necrosis factor (TNF) signaling via NF- ⁇ B observed, which are not present in D2 ( Figure 2H).
  • TNF tumor necrosis factor
  • the findings reveal a pronounced immune response in the blood immune cells of D1 following transplantation, characterized by a rise in T and NK cells and notable transcriptional alterations across various cell types. This response was notably absent in D2.
  • Multi-omics data were processed from time points every 6 h from pre-transplant (0 h) until ⁇ 66 h in xenograft D1 and D2, with no significant batch effects evident using principal-component analysis (PCA) analysis for each of the single -omic procedures.
  • PCA principal-component analysis
  • the multi-omics datasets were integrated and normalized to identify signatures related to post-xenotransplant time and clinical features. Post-xenotransplant analyte changes in RNA-seq, proteins, lipids, cytokines and metabolites increased from 42 h, especially in D1 ( Figure 3).
  • liver function markers alanine transaminase (ALT) and aspartate transferase (AST); and INR (international normalized ratio), a metric of clotting time.
  • This cluster was highly enriched for: metabolic processes, lipid metabolism and immune response (antigen presentation and processing q ⁇ 0.01).
  • the dynamics of this cluster align well with clinical findings including systolic and diastolic blood pressure as well as troponin levels, and in particular with IL-6, IL-8, IL-10 and IL-13 ( Figures 3D, 9A) and cardiac output metrics (Figure 9B).
  • the human nuclei were immune cells from these categories, demonstrating that diverse human immune cells migrated into the pig heart xenograft.
  • IRI ischemia reperfusion injury
  • the pig xenograft snRNA-seq data were integrated with a published pig heart snRNA- seq dataset (Andrijevic, et al., 2022), which includes 15 samples from five different conditions: ischemia at 0, 1, and 7 h; extracorporeal membrane oxygenation (ECMO); and OrganEx reperfusion.
  • FIG. 4A Integration with this dataset ( Figure 4A) enabled use of the non-ischemic samples as a transcriptional baseline and the ability to compare signals arising from ischemic and reperfusion conditions.
  • the main cardiac cell types were observed, including cardiomyocytes (CMs), vascular endothelial cells (VECs), fibroblasts (FBs), immune cells, smooth muscle cells (SMCs), pericytes (PERs), and Schwann cells for the mapped pig cells ( Figures 4A-B, 10B-C, 21).
  • CMs cardiomyocytes
  • VECs vascular endothelial cells
  • FBs fibroblasts
  • immune cells smooth muscle cells
  • SMCs smooth muscle cells
  • PERs pericytes
  • Schwann cells for the mapped pig cells
  • NK T cell human nuclei in D2 are CD69 + /CCR7- cells with strong expression of CX3CR1, STAT4 and ID2, a population of effector memory T cells with known strong cytotoxic activity (Martin, et al., 2018) ( Figure 10F).
  • Pseudobulk level DEA with DESeq2 was used to compare each ischemic/reperfusion condition, and each decedent to the non-ischemic baseline. Pseudobulk level analysis allowed inference of statistical significance based on the number of samples.
  • DEA revealed signals specifically associated with D1, such as Myc targets and NIK to non-canonical NF- ⁇ B signaling pathways ( Figures 4F-G), whereas some of the reperfusion signals (ECMO) were shared between D1 and not D2, including VEGFA and IL signaling ( Figures 22A-C).
  • IL signaling was driven by overexpression of FOS, CD80, IL15, RIPK2, HMOX1, IL1RAP, TIMP1, and IL25 in both decedents, whereas IL17RB, CCL2, IL4R, STAT3, and HIF1A were only significantly overexpressed in D1 ( Figure 10G).
  • Non-canonical NF- ⁇ B enrichment was driven by proteasome-associated genes, specifically overexpressed in D1 (Figure 10H).
  • Other pathways of interest such as MTOR, EMT, VEGFA, NOTCH and TNF are detailed ( Figure 22G).
  • Figure 22G the most enriched pathways in D1 were also enriched in D2 ( Figure 4G) and vice versa ( Figures 22D-F); these mostly constituted replication and cell division-related signals, in line with the observed higher percentage of FBs compared to the reference ( Figure 4B).
  • the genes that were generating this DE signal were identified ( Figure 10I).
  • deconvolution with Cell2location and DestVi were used to further annotate these spatial transcriptomics clusters with their respective cell-type composition ( Figures 5D-E, 11B-D, 26-28).
  • Cluster 7 also concentrates a high level of FBs and macrophages.
  • Cluster 7 is exclusive to D1 (observed mostly in the left ventricle, Figure 11E). Damage-associated proteins (DAMPs), IL-33 and its ligand IL1RL1 (Roh, et al., 2018) were also enriched in clusters 7 and 8 ( Figures 5F, 11F-G), and were more prevalent in D1 ( Figure 5G). In addition, other DAMPs (Roh, et al., 2018) were found to be in the top 60 DEGs for cluster 7 or 8, including BGN, DCN and S100A2 ( Figures 5F, 29).
  • DAMPs Damage-associated proteins
  • IL-33 and its ligand IL1RL1
  • the IL-33–IL1RL1 axis is of high interest here, with observed interaction with SMCs, FBs, and lymphocytes (all enriched in cluster 7, and in the surrounding clusters), and is confirmed in the snRNA-seq data.
  • Cell-cell communication reveals that this axis is activated exclusively in the heart xenograft, especially in D1-LV ( Figures 5H-I, 11E). Specifically, these interactions involve cross talk between lymphocytes, SMCs, and FBs in D1-LV.
  • Hematoxylin and eosin (H&E) stains in D1 show patchy contraction band necrosis with interstitial edema and endothelial cell swelling in the small interstitial capillary ( Figures 5J, 12A). Also in D1, an intramuscular artery shows endothelial swelling and detachment with few inflammatory cells and perivascular edema ( Figure 12B).
  • Electron microscopy (EM) in D1 shows endothelial cell swelling resulting in the narrowing of the capillary lumen and sarcolemmal disruption, a feature of irreversible myocyte injury, in concordance with the cytoplasmic staining of C4d. Furthermore, widened disorganized Z-lines and accumulation of mitochondria are present, confirming degenerative changes in CMs. Swelling of endothelial cells, though to a lesser extent, is also seen in D2, both in the intramyocardial artery and the capillaries ( Figure 5K).
  • Cluster marker enrichment analysis reveals that these vascular clusters are enriched for epithelial–mesenchymal transition and extracellular matrix (ECM) organization ( Figure 13B).
  • ECM extracellular matrix
  • GSEA sample-based DE Gene Set Enrichment Analysis
  • FIG 13C spatial transcriptomics and histology provides evidence for a vascular damage and vascular remodeling process in xenografts, in addition to hypoxic signals. This is observed across both decedents but much more pronounced in D1 and its left ventricle, where they are also associated with mechanoregulation and immune-related signals.
  • Example 5 Validating observed changes in vascular tissue remodeling.
  • the snRNA-seq data were revisited to identify signals associated with vascular cell types ( Figures 5L-M, 13D).
  • the observed hypoxia signal was more prevalent in D1, with HIF1A levels surpassing those of ischemic and reperfusion controls at 0, 1, and 7 h and ECMO, OrganEx in CMs, MPs, and FBs.
  • hypoxia genes display a gradual increase from non- ischemic samples to 1 h, 7 h, and reperfusion
  • IL17B overexpression is specific to the pig heart xenograft samples (CM and L2).
  • IL33 and IL1RL1 levels did not change in the ischemic/reperfusion conditions, but displayed strong expression in the heart xenograft samples, particularly in D1, in CMs, DIVs, FBs, and especially VECs ( Figure 5L).
  • IL1R1 receptor to DAMP IL1A
  • PCXD perioperative cardiac xenograft dysfunction
  • VEC inflammation is prevalent in subclusters VEC-4 and VEC-9, which are enriched in the reperfusion samples and D1 ( Figures 6E-F).
  • a similar pattern is observed in the PER-SMC population, with PER-SMC-3 ( Figures 6G-J, 31).
  • PER-SMC-6 is a dividing cell population based on its cell cycle score and is specifically present in the xenograft, with a higher prevalence in D1.
  • Visium cluster 9 is enriched in lymphatic endothelial cells (Figure 11B) and is more prevalent in D1 ( Figures 11D-E).
  • DEA at the pseudobulk level in that population reveals a transcriptomic signal specific to D1, mostly related to cell division and mitosis ( Figure 15A), whereas subclustering analysis of that population ( Figures 15B-C, 32) revealed a dividing subcluster LEC-8 specific to the xenograft and more prevalent in D1 ( Figures 15D-E).
  • the LEC-3 population displayed an inflammatory response (TNF signaling) that is 140 314164997v1 Attorney Docket No: 243735.000437 present in both xenograft and reperfusion samples and more prevalent in D1 than D2 ( Figures 15D-F).
  • DIV-PER population was enriched for epithelial–mesenchymal transition and the DIV–VEC-2 population with TGF- ⁇ signaling ( Figures 16B-C).
  • DIV–PER and DIV–VEC-2 were almost exclusively observed in the xenograft samples and more prominently in D1 ( Figure 16D).
  • DIV–VEC-2 population displays markers, such as IL1R1, IL33 and HIF1A ( Figure 16E). Additional evidence was identified supporting vascular inflammation with high expression of ICAM1, P-selectin (SELP), and E-selectin (SELE) in VECs and dividing endothelial cells, predominantly in D1 ( Figures 16F-I).
  • the multi-omics profiling shows a markedly different trajectory between the two pig-heart-to-human decedent xenotransplant procedures, which mirrors their differing clinical trajectories.
  • D1 showed a dramatic increase of several pathways in the blood and tissue, enriched for metabolic processes (glycolysis and pyruvate metabolism), lipid metabolism (peroxisome and fatty acid degradation), liver injury as well as immune response (antigen presentation and processing) and necroptosis.
  • D1 Among the responses occurring in D1, a rapid remodeling of both the tissue immune microenvironment (using spatial RNA-seq and snRNA-seq) and in the circulating immune response (using scRNA-seq) was observed, with a rapid and robust induction of B cells that quickly tapered off. This was followed by plasma cell activation and a robust T cell-mediated response. D2 showed a stable, higher level of B cells but a lower level of T cells, with a marked decrease after the second thymoglobulin injection.
  • IL17B which is linked with IRI in the kidney and heart
  • CCDC3, CCN3 and MYL4 which have been linked with heart ischemia
  • PCXD occurs when cardiac xenografts show significant dysfunction in the first 24-48 h after transplantation in the absence of rejection (Byrne, et al., 2012).
  • PCXD includes xenograft deterioration from both poor preservation protection resulting in ischemia reperfusion and immune-mediated injury, most often from non- ⁇ -Gal antibodies, and is observed in 40-60% of pig-to-nonhuman primate cases (Byrne, et al., 2012). Further dissection of the pig xenograft transcriptome data herein supports PCXD occurrence in D1.
  • CD8 + T cells in the xenograft early on after transplant leads to granulocytic infiltration and necrosis.
  • Increased cold ischemic time results in a more pronounced presence of macrophage, neutrophil and memory T cells (Koritzinsky, et al., 2021).
  • the therapeutic T cell depletion predominantly decreases na ⁇ ve T and T reg cells, with essentially no impact on effector memory T cells (Pearl, et al., 2005). This selective depletion of na ⁇ ve T cells can result in homeostatic expansion of memory CD8 + T cells (White, et al., 2017).
  • Single-cell transcriptomic landscape of pig-to-human kidney xenotransplantation was performed using single-cell RNA sequencing (scRNA-seq) was performed on the xenografts and their contralateral (untransplanted) kidneys from two cases of xenotransplantation using ⁇ -Gal-KO kidneys (Montgomery, Stern, et al., 2022).
  • Integrative analyses of scRNA-seq data of the four samples identified 16 unsupervised cell clusters based on their transcriptomic profiles (Figure 33B), with a balanced representation of cells in the samples of both xenotransplantation cases (Figure 33C). These cell clusters represent the major cell types previously identified in mammalian kidneys, including proximal tubule cells (PTCs), thick ascending limb of the Loop of Henle (TAL), distal tubule cells (DTCs), intercalated cells (ICs), endothelial cells (ECs), and immune cells (Figures 33D, 39).
  • PTCs proximal tubule cells
  • TAL thick ascending limb of the Loop of Henle
  • DTCs distal tubule cells
  • ICs intercalated cells
  • ECs endothelial cells
  • immune cells Figures 33D, 39.
  • Podocytes were not detected as a separate cluster of cells due to their sparsity among kidney cells, though a small population of putative podocytes was identified in the data ( Figures 39E-F) (Malone, et al., 2020; Andrijevic, et al., 2022).
  • Example 8 Human immune cells infiltrate porcine xenografts. [00312] To test whether human immune cells had infiltrated into the xenograft, scRNA-seq raw sequencing reads were mapped to both human and porcine reference genomes to compare their cell-specific mapping efficiency. In principle, human cells should detect more mappable reads when mapped to the human reference genome due to sequence specificity, and vice versa.
  • RNA-seq of core biopsy samples from the second xenograft was performed at 145 314164997v1 Attorney Docket No: 243735.000437 times 0, 12, 24, and 48 hours post-xenotransplantation (pXTx) (Tables 3-4).
  • infiltrated human NK cells express interferon-gamma (IFNG, Figure 34D), while the infiltrated human macrophages express interferon-gamma-stimulated chemokines such as CXCL9, CXCL10 and CXCL11 ( Figure 34D).
  • IFN interferon-gamma
  • CXCL9 interferon-gamma-stimulated chemokines
  • Figure 34D interferon-gamma-stimulated chemokines
  • Example 9 Transcriptomic signatures consistent with an antibody-mediated response.
  • AbMR antibody-mediated rejection
  • TCMR T-cell-mediated rejection
  • the top differentially expressed genes in both xenografts are biomarkers of kidney injury and inflammatory response, including profibrotic osteopontin (SPP1) (Khamissi, et al., 2022; Sinha, et al., 2023), phagocytic osteoactivin (GPNMB) (Patel- Chamberlin, et al., 2011; Adler, et al., 2010), and S100A6 (Cheng, et al., 2005; Chen, et al., 2023) ( Figure 36A).
  • SPP1 profibrotic osteopontin
  • GPNMB phagocytic osteoactivin
  • S100A6 Choeng, et al., 2005; Chen, et al., 2023
  • Kidney injury-related genes were globally increased in the nephron cell types in both xenografts including PTCs, DTCs, TAL cells, and ICs (Figure 36B), indicating signs of holistic tissue damage when xenografts were exposed to the human immune environment.
  • Example 11. Xenograft-specific proliferating cells indicate porcine kidney tissue repair. [00320] Concurrent with detecting global tissue damage in the nephron cells, a unique proliferating cell population was identified among all cell types (Figure 36C).
  • MHC major histocompatibility complex
  • monocytes, macrophages, and megakaryocyte progenitor cells two waves of increased MHC gene expression were found: a first wave at 12 hours pXTx, and a second at 48-53 hours pXTx ( Figure 37C). These two waves of increased MHC gene expression are correlated with two phases of immune gene activation at 12 hours pXTx and 48-53 hours pXTx ( Figures 37D-E).
  • PBMCs at 12 hours pXTx have upregulated gene sets in most cell types including monocytes, macrophages, T cells, B cells, and NK cells ( Figure 37D, Table 6).
  • Macrophages, NK cells, T cells, and megakaryocytes have upregulated gene sets at 48-53 hours pXTx (Figure 37E, Table 7). These genes are enriched in functions related to the activation of predominantly innate immune responses at 12 hours pXTx ( Figure 42A) and the early activation of adaptive immune responses at 48 hours pXTx ( Figure 42B). Interestingly, these two waves of gene expression are correlated with the trajectory of interferon-gamma expression in NK and NKT cells in the recipient’s PBMCs ( Figure 42C). Together, these results from time-resolved scRNA-seq of PBMCs revealed the dynamic immune response of the recipient’s immune system to the porcine xenograft kidney. Table 6.
  • the evidence obtained in this study includes 1) the infiltration and the activation of human macrophage and NK cells in the xenografts ( Figures 34–35); 2) the detection of AbMR-associated marker gene expression through time-resolved RNA-seq of xenograft biopsies ( Figure 35); 3) activation of endothelial cells in xenografts ( Figure 35); and 4) the holistic xenograft tissue damage signals ( Figure 36).
  • these expression changes were not observed in the contralateral untransplanted kidneys that also experienced ischemia, indicating that the observed evidence reflect xenotransplantation-induced responses but not ischemic injury-induced effects.
  • Immune cell infiltration into transplanted organs may indicate activation of the recipient immune response and predict rejection of the graft (Spahn, et al., 2014; Puga Yung, et al., 2017; Itescu, et al., 1998; Parkes, et al., 2017).
  • Recipient s macrophage and NK cell infiltration into the transplanted organ is seen in innate immune response but the extent of the response in the kidney xenograft and degree of endothelial injury seems to represent a unique phenotype.
  • RNA-seq monitoring of the xenograft revealed a two-phase activation of interferon-gamma signaling and the expression of multiple pro-inflammatory chemokines including CXCL9 and CXCL10. It was found that, at 12-to-24 hours pXTx, these pro- inflammatory chemokines were mainly expressed by porcine immune cells.
  • Proliferating or cycling cells have been observed in damaged kidney in both humans and rodents (Toback, et al., 1992; Humphreys, et al., 2008; Cochrane, et al., 2005; Wen, et al., 2023), and in some rejection cases of human kidney allotransplantations (Wu, et al., 2018). Further studies revealed that the activation of proliferative cells occurring in damaged kidneys may represent a cellular hallmark of kidney tissue repair to potentially restore nephron structure and function (Humphreys, et al., 2008; Benigni, et al., 2010; Witzgall, et al., 1994).
  • Example 13 Description of the experiments: clinical events, treatments, and multi-omics assays performed in the 61-day period following xenotransplantation.
  • the relevant clinical phenotypes include: post-operative day (POD) 0-28: IgM glomerular deposition, C3/C3b complement activation, mesangialysis; POD 33: rising creatinine, IgG spike, +C5b9, and diagnosis of AbMR; POD 34- 43: treatment of AbMR with PLEX, steroids and C3 inhibition; POD 44-48: donor specific antibody rebound during treatment pause; POD 49: diagnosis of ongoing AbMR and new cell mediated rejection (CMR); POD 49-58: treatment of AbMR and CMR; POD 61: resolution of AMR/CMR.
  • POD post-operative day
  • POD 33 rising creatinine, IgG spike, +C5b9, and diagnosis of AbMR
  • POD 34- 43 treatment of AbMR with PLEX, steroids and C3 inhibition
  • POD 44-48 donor specific antibody rebound during treatment pause
  • POD 49 diagnosis of ongoing AbMR and new cell mediated rejection (CMR)
  • POD 49-58 treatment of
  • PBMCs peripheral blood mononuclear cells
  • BCR B-cell Receptor
  • TCR T Cell Repertoire
  • clustering was used for species partitioning as the data included the non-transplanted contralateral pig kidney in addition to the decedent’s native human kidney, which was taken at nephrectomy prior to the xenotransplant procedure. This enabled the identification of a human cluster where cells originating from the native human kidney and human cells found in the xenograft grouped together ( Figures 49D-E). This cluster was further dissected to identify pig/human doublets ( Figure 49F). Finally, the performance of the method herein was estimated by comparing the results across the two ST modalities, which yielded similar results across timepoints ( Figures 49G-H).
  • the contralateral pig kidney had no cells assigned to the human group in the partitioning (although 1 cell out of 42,503 in the sample was predicted as a pig/human doublet), indicating very high specificity for the human cell identification process. As ⁇ 99% of cells from the decedent’s native kidney clustered as human cells, this also suggested very high sensitivity for human cell detection (Figure 49I). Distribution of human and pig probe expression was consistent with the partitioning results ( Figures 45C, 50A). Xenograft biopsy snRNA-seq data was partitioned between pig and human in a similar clustering-based method, and displayed a clear bimodal distribution in the human gene expression fraction ( Figures 50B-D).
  • ST mRNA probes were assessed for human and pig orthologues, and consistent expression patterns were noted across pig and human cells, particularly for immune markers such as CD4, CD3 and CD68 ( Figure 58).
  • Figure 58 immune markers such as CD4, CD3 and CD68.
  • ST datasets were overlaid onto hematoxylin and eosin (H&E) slide images, generated from the same tissue samples on which the ST was performed ( Figures 45D-F).
  • Clustering and dimensionality reduction were used to assign cell types to detected cells across data modalities, including the 478 ST panel (Figures 45G-H) and 5.1k ST panel (Figures 45I-J), tissue snRNA-seq data ( Figures 45K-L), and recipient PBMCs scRNA-seq data (Figure 45M). Extensive depiction of the identified cell type, marker genes, and quantitative distribution across timepoints are shown in Figures 59-69. In addition, bulk RNA-seq and flow cytometry were performed on recipient PBMCs to validate changes in cell-type composition observed on scRNA-seq data ( Figure 44).
  • Proteomes were generated for all daily timepoints and ‘pig-alone’ and ‘decedent alone’ (pre-transplant) sera 198 314164997v1 Attorney Docket No: 243735.000437 samples using a proteomics profiling approach (Proteograph XT) and analyzed by LC-MS/MS (liquid chromatography, tandem mass spectrometry).
  • Example 14 Recipient plasma, B and NK cell activation in response to the xenotransplantation.
  • Acute antibody-mediated rejection (AbMR) of the xenograft was confirmed by conventional histology after a for-cause biopsy was performed on POD 33 based on a change in kidney function.
  • human immune cells consisting predominantly of macrophages, natural killer (NK) cells and plasmacytoid dendritic cells (pDCs) infiltrated the xenograft ( Figures 46A-F, 61-62). Patterns in the number of infiltrating human immune cells in the xenograft tissue by ST across time were consistent with the AbMR event ( Figures 46A, 44). Less than 2% of human cells were found immediately post-transplantation, but they increased up to more than 5% at POD 21 ( Figure 46A), before peaking at POD 33 (at biopsy-confirmed AbMR) where human cells represented >20% of all cells observed in the xenograft biopsy ( Figures 46A, 49G-H).
  • Macrophages dynamics in the xenograft [00338] Macrophages constituted the largest human immune cell population within the xenograft across both ST panels ( Figures 45G, 45I, 46I, 59C, 61C). Within this compartment, distinct macrophage and monocyte subpopulations were identified, displaying dynamic changes over time.
  • tissue snRNA-seq data shows that T cells were largely absent prior to POD 33 but had an overall lower total percentage than the ST data (Figure 53I).
  • the blood trends were confirmed with expression dynamics of CD8 + T, CD4 + T and Treg markers in PBMC bulk RNA-seq, showing a sharp decrease of expression immediately after POD 49 ( Figures 47J-L, 53J).
  • CD38 a marker associated with transplant rejection (Mancebo, et al., 2016; Doberer, et al., 2021) was highly expressed in CD8 + T cells around the AbMR event (POD 33), both in PBMC and human infiltrating cells in the xenograft, as well as in NK cells ( Figure 47Q). These populations also displayed increased expression of TNFRSF9, which shows elevated expression in T and NK cells in the context of kidney allograft AbMR, and is associated with effector cell function and immune activity (Fröhlich, et al., 2020; Parkes, et al., 2017; Parkes, et al., 2018).
  • an iReceptor public database search of this and other top clones returned a >50% hit rate against the database (Table 8), providing additional context for determining the functionality and specificity of TCR beta receptors (Corrie, et al., 2018).
  • Many of the clones overlapping with the database were previously found to be related to CMV (cytomegalovirus) and EBV (Epstein-Barr virus), while others, including the top clone, were also found in pediatric recipients following TCR ⁇ /CD19-depleted hematopoietic cells transplantation (Zvyagin, et al., 2017).
  • COLEC11 defined a COLEC11+ population in the 5.1k panel ST data. COLEC11 activates the lectin pathway of the complement system and is associated with kidney damage, such as ischemia/reperfusion injury and complement associated injury rejection (Nauser, et al., 2017; Farrar, et al., 2016; Nauser, et al., 2018). These SPP1+ and COLEC11+ populations have very similar proportions, spiking at POD 21, with relatively high levels at POD 33 and decreasing thereafter ( Figures 48B-C).
  • Figures 48D- E Their gene markers include HIF1A, CXCL14+ or ICAM1, which are related to inflammation, immune recruitment and kidney injury (Rosenberger, et al., 2002; De Greef, et al., 2003; Hill, et al., 1995). High levels of THSB1 and STMN1 were also expressed. [00345] Similarity in expression of marker genes in both clusters suggests that they are the same population, detected in the two different ST panels. They colocalized with fibroblasts and endothelial cells across samples, and with human immune cells for POD 33 ( Figures 48F-H, 55C-D).
  • Porcine immune cells found in the graft cells were mostly resident macrophages (high expression of CD163, MRC1, C1QA, TLR2, and CCR5) ( Figures 55E-F), while porcine T cell 205 314164997v1 Attorney Docket No: 243735.000437 presence was mostly limited to POD 0 based on marker gene expression ( Figure 55G).
  • porcine immune cells in the 5.1k ST panel data revealed increased expression at POD 21 and POD 33 for genes of interest such as HIF1A, CD163, MKI67, STAT3, MX1, IFIT1, and S100A6 ( Figure 55H).
  • kidney damage included increased expression from COLEC11, CDLN1, MX1, IL33, and MX2 in podocytes, and endothelial cells, fibroblasts, and Henle loop in the tissue ( Figures 55I-J).
  • B-HOT-derived AbMR signature was predominant in CXCL9+ macrophages, NK cells, and interferon+ cells ( Figure 70A) and showed higher levels at POD 21 and POD 33 ( Figures 56E, 70B).
  • many probes targeting pig orthologues of B-HOT panel genes had increased expression in pig cells (5k ST panel) at POD 21 and POD 33 ( Figure 56F).
  • AbMR signature score at POD 33 colocalized with pig MX1 expression and to a lower extent pig SERPINE1 ( Figure 56G).
  • CXCL9, CXCL10, and CXCL11 Th1 immune response
  • This pattern mostly originated from a population of activated macrophages, which were enriched for interferon response and cytokine signaling, and were the predominant cell population at POD 33.
  • Another human immune cell population (interferon+) expressing high levels of interferon-associated genes was also prevalent at POD 33.
  • CXCL9- and CXCL10-expressing immune cell hotspots may act as chemotactic niches, guiding T cell infiltration and differentiation.
  • tissue pDCs and cDC1 cells exhibited the highest expression of CXCL9, CXCL10, and CXCL11 at POD 21, alongside activation markers (Birmachu, et al., 2007; Guiducci, et al., 2008; Di Domizio, et al., 2009) such as IRF7 and TLR9, indicating both heightened activation and a potential early role in recruiting CXCR3+ T cells to the graft.
  • the results herein underscore the importance of evaluating immune infiltration and host immune response at timepoints extending beyond the initial 3-day period examined in the aforementioned studies.
  • the ST data herein displayed a distinct bimodal distribution, and the alignment methodology was validated through control samples.
  • the tissue remodeling observed at POD 21 and POD 33 was accompanied by specific expression of molecular markers, including SPP1 (Yu, et al., 2023; Lin, et al., 2023; Alchi, et al., 2005; Jin, et al., 2013; Sinha, et al., 2023).
  • STMN1 a marker of cell proliferation, shown to be overexpressed in the early cellular response to pig-to-human kidney xenotransplantation, which could be part of a tissue repair process following transplant- associated injury (Pan, et al., 2024). Also, expression of COLEC11 was observed, which encodes collectin-11, a marker of complement-mediated kidney injury (Nauser, et al., 2017; Nauser, et al., 2018), which has been associated with ischemia-reperfusion injury (Farrar, et al., 2016). Recent evidence has shown associations of collectin-11 with tubulointerstitial fibrosis (Wu, et al., 2018).
  • THSB1 expression has been associated with kidney fibrotic remodeling after injury (Daniel, et al., 2007). Also, increased expression of other stress and injury response genes was identified, such as HIF1A (Rosenberger, et al., 2002) and IL33 (Chen, et al., 2016). The identified population of interest (SPP1+ and COLEC11+) also expressed high levels of S100A6, a known marker of tubular injury (Cheng, et al., 2005). S100A6 is also found to be markedly expressed in POD 21 and POD 33 in many cell-types of the xenograft.
  • MX1 and MX2 as well as spatial colocalization of MX1 with infiltrating immune cells, suggest a response of pig kidney cells to human interferon activation described herein.
  • Resident pig macrophage activation shown by their increased expression of CD163 (Etzerodt, et al., 2013), S100A6 (Xia, et al., 2018), MKI67 (Lee, et al., 2011), STAT3 (Lee, et al., 2024), IFIT1 (McDermott, et al., 2012), and MX1 (Rascio, et al., 2020) (specifically at POD 21 and POD 33), suggests potential signaling from inflamed or damaged tissue, and raises the possibility of interactions between infiltrating human and resident pig immune cells.
  • the dominant TRBV2/TRBJ1 clonal signature identified in this study was queried for similar observances in the literature.
  • the database returned an exact clonal sequence match from a single donor associated with a previous study on tracking T-cell immune reconstitution after hematopoietic transplant (Zvyagin, et al., 2017).
  • TCR beta chain TCR alpha chain clonotype information was simultaneously generated and similarly, a strong TRAV13/TRAJ4 clonal signature was identified.
  • the AbMR event only partially responded to the first cycle of plasmapheresis, steroids, and complement 210 314164997v1 Attorney Docket No: 243735.000437 inhibition with anti-C3 and C5 treatments. Complete resolution of the AbMR episode was achieved only after further plasmapheresis and T-cell depletion with rATG.
  • the isolated intimal arteritis seen on POD 49 histology could have been easily missed (presaged by the multi-omic studies on POD 33) and the cell mediated contribution of the rejection episode unappreciated.
  • Tregs were first observed in the tissue peaking at POD 33-45, synchronous to the increase in peripheral blood, mimicking the TEM CD8 + T cells pattern. Expansion of Tregs is known to be induced by activated CD4 + or CD8 + T cells (Martin-Moreno, et al., 2018), and the presence of Tregs in transplanted organs is associated with graft tolerance and survival (Mohr Gregoriussen, et al., 2017).
  • This study is one of the most densely phenotyped human studies conducted to date across a defined study period. The aim of this project was focused on discovery and the analytes identified in this study could be further developed into targeted assays (e.g., digital PCR, immunohistochemistry, etc.) that could be easily deployable in a clinical laboratory setting.
  • targeted assays e.g., digital PCR, immunohistochemistry, etc.
  • ST assays excel at identifying diverse immune populations and provide a reliable ground truth for cell-type composition when integrated with histological staining (Abedini, et al., 2024; Janesick, et al., 2023; Zhang, et al., 2024). Consistent with this, the xenograft snRNA-seq data showed a lower percentage of human immune cells in the graft compared to the ST data herein. Leveraging the complementary strengths of these advanced techniques enabled the study herein to achieve a high-resolution characterization of cell types in the xenograft model.
  • the study herein provided an in-depth genomic and pathway-level immune cell characterization allowing early immune and tissue cell responses to the xenograft to be assessed in a human decedent within an ICU setting and maps the early immune response and donor xenograft damage before physiological kidney function decline.
  • the work herein confirms the absence of initial xenograft injury in the first days post-transplantation, characterizes the AbMR episode before its clinical and histological presentation, and confirms its resolution.
  • This study provides an extensive data resource for understanding the dynamics and importance of inflammatory and tissue repair archetypes, interactions with resident pig immune cells, and infiltrating human immune cells comprising both the innate and adaptive immune systems of the recipient.
  • Example 19 provides an extensive data resource for understanding the dynamics and importance of inflammatory and tissue repair archetypes, interactions with resident pig immune cells, and infiltrating human immune cells comprising both the innate and adaptive immune systems of the recipient.
  • Prognostic and diagnostic assays based on gene signatures are described herein. These signatures allow evaluation and prediction of biological processes such as tissue injury, inflammation, fibrosis, immune activation, rejection, and overall graft health. By analyzing expression patterns across a set of genes, it becomes possible to detect early signs of dysfunction and to guide clinical decision-making and therapeutic interventions.
  • Samples for analysis will include: blood (e.g., PBMCs, plasma, serum), tissue biopsies (e.g., from the transplanted organ), urine (e.g., for kidney grafts), and/or other proxy fluids or tissues as appropriate (e.g., pericardial fluid for heart transplants).
  • blood e.g., PBMCs, plasma, serum
  • tissue biopsies e.g., from the transplanted organ
  • urine e.g., for kidney grafts
  • proxy fluids or tissues e.g., pericardial fluid for heart transplants.
  • RNA-sequencing RNA-based methods
  • qPCR assays e.g., RNA-based methods
  • epigenetic assays e.g., methylation, chromatin accessibility [ATAC-seq]
  • single-cell technologies e.g., scRNA-seq, CITE-seq
  • spatial transcriptomics e.g., with targeted panels containing genes of interest or with whole transcriptome methods followed by signature enrichment analyses
  • multiplexed approaches combining RNA and protein measurements.
  • the gene set described herein also includes cell-type markers that were identified in experiments described herein. Many of these cell types show major shifts in abundance in response to transplantation and are associated with the processes described herein. Thus, expression of these markers could also serve as a proxy for the abundance or activation state of these cell types in clinical samples, with major prognostics or diagnostic value. This approach in disclosed herein.
  • the signatures include markers of both porcine and human origin, reflecting the dual biology of xenotransplantation (e.g., donor and host). This provides an opportunity to track both graft-intrinsic and host-driven processes in parallel.
  • Human genes include: LYZ, CD163, NKG7, GNLY, KLRD1, TYROBP, IFNG, CXCL9, CXCL10, CXCL11, COL13A1, TM4SF18, ICAM2, CDH13, RAPGEF5, ROBO4, PLA1A, ANKRD22, TNFSF8, SLAMF8, CDH5, PECAM1, and RAMP3. These genes were shown to have increased expression in human cells in the graft following transplantation, aligning with enhanced immune activity, inflammation, and rejection signals.
  • Pig genes include: CXCL11, CXCL10, COL13A1, TM4SF18, ICAM2, CDH13, RAPGEF5, ROBO4, PLA1A, ANKRD22, TNFSF8, SLAMF8, CDH5, PECAM1, RAMP3, SPP1, GPNMB, S100A6, STMN1, SLC34A1, CD3E, PCLAF, HMGB2, HMGN2, SMC4, USP1, CKS2, and NASP. These genes were found to have increased expression in the xenograft tissue, as a response to inflammation and the rejection signals, including many damage markers. [00366] Experiments described herein of pig to human xenotransplant in 1 decedent, studied for 61 days, revealed the following gene signals.
  • Human genes include: CXCL9, TRBV2, TRBJ1, IGG, IGA, IFNG, CXCL10, CXCL11, STAT1, LILRB1, CD163, CD14, MRC1, CXCR1, CXCR2, CD300E, LILRA5, FCN1, LILRB2, MX1, IFIT1, IFIT2, CD3E, IGHG, IGHA, IGHD, IGHM, FOXP3, CD38, TNFRSF9, and ITGAE. These genes were found to be associated with the rejection event in temporality and are major markers of invading immune cell activity in the graft during the experiment.
  • CXCL10 CXCL11, CXCL9, GBP1, GZMB, ICAM1, IDO1, IRF1, KLF4, LILRB2, PECAM1, PRF1, TAP1, and WARS.
  • AbMR-associated genes that were found to be temporally associated with the rejection event.
  • cell type markers of cell populations which were found to display proportion dynamics that were associated with the rejection event and other clinical events displayed by the xenograft.
  • Cell markers of human cells invading the tissue include, for mast cells: IL1RL1, KIT, GATA2, SLC18A2, LIF, IL18R1, MS4A2, SIGLEC6, HDC, HPGD, and MAOB; for interferon+ cells: MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, CCL8, IRF7, and IFIH1; for neutrophils (activated): ANXA3, CEACAM8, DEFA1, MMP8, PADI4, PGD, RNASE3, CAMP, CSF3R; for plasma cells: XBP1, MZB1, TENT5C, DERL3, CLPTM1L, SLAMF7, PIM2, PDIA4, and POU2AF1; for cDC2 cells: FCER1A, CLEC10A, CD1C, CD1E, CD1D, ENHO, CCR2, FCGR2B, CX3CR1,
  • Cell markers of human blood cells include, for T cells: CD3G, CD3E, THEMIS, CD5, and CD6; for na ⁇ ve cells: LEF1, TCF7, SELL, NELL2, CD55, and CCR7; for CD4+ cells: CD4, and TSHZ2; for CD8+ cells: CD8A, and CD8B; for Treg cells: CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA; for CD8 TEM cells: KLRG1, GZMK, CST7, and GZMH; for cytotoxic cells: GZMA, PRF1, and FASLG; for CD4 TEM cells: ADAM19; for NK cells: KLRC1, GZMB, TRDC, and KLRF1; for proliferating cells: TOP2A, TYMS, and MKI67; for BLK+ cells: BLK, DNM3, ZNF462, and MCF2L2; and for MAIT cells: SLC4A
  • Resident/circulating markers for human NK cells include resident markers: ITGA1 (CD49a), CD44, CD160, TNFSF10, CCR2, CD69, TCF7, EMB, KIT, LTB, and BHLHE40; and circulating markers: S1PR5, KLF2, SELL (CD62L), CX3CR1, and KLRG1.
  • markers for human T cells include resident markers: ITGAE (CD103), ITGA1 (CD49a), CXCR6, CXCR3, ZNF683 (HOBIT), PRDM1 (BLIMP-1), CD69, CD101, and CRTAM; and circulating markers: S1PR1, SELL (CD62L), CCR7, KLF2, TBX21, EOMES, and CX3CR1.
  • pig genes overall markers associated with the rejection event, inflammation, pig immune activity, hypoxia, fibrosis and damage in the tissue include: BGN, C1QA, C3, C3b, C4A, C5b9, CCL19, CD163, CDH18, CDLN1, CCR5, CLDN1, COLEC11, COL1A1, COL1A2, CXCL14, CXCL2, DCN, FOS, FOSB, HIF1A, ICAM1, IFIT1, IL1R1, IL33, JUNB, LYZ, MKI67, MRC1, MX1, MX2, NFKBIZ, NREP, S100A11, S100A6, SERPINE1, SOX9, SPP1, STAT3, STMN1, THBS1, and TLR2.
  • Genes associated with the rejection event in pig immune cells HIF1A, LIMA1, EMP1, EFNA1, DTX4, LYVE1, YWHAG, CD163, CCL2, VCAM1, MKI67, CDK1, CD19, IFIT1, CD1D, PSMD1, FUS, CAM1, STAT1, GBP1, TOP2A, S100A6, MCM6, IGFBP2NL, CTNNB1, YWHAZ, SMCA2, SMCA1, SERPINF1, SERPINH1, TOP2A, AP2M1, RBP7, NDUFB9, MX1, CD1E, FTL1, MK2, MSN, WARS, SLURP1, ANXA2, ANXA1, SMP, TAU, PLC, ACSL4, IL4, and RGS5.
  • Human genes include genes found to have patterns of interest in the human PBMC data that are mostly associated with immune activity, such as: CD69, IL7R, CCR7, TCL1A, CD70, FOS, IL6, IL8, IL10, IL13, CX3CR1, STAT4, and ID2.
  • Genes found to have patterns of interest in human cells found to be infiltrating the graft include: CCR7, CD69, IL7R, CX3CR1, STAT4, and ID2.
  • Cell markers of human cells include, for T cells / NK cells: CD247, CD96, ITK, CD7, BCL11B, and APBA2; for B cells: EBF1, BANK1, MS4A1, BLK, CD79A, PAX5, 216 314164997v1 Attorney Docket No: 243735.000437 and PLEKHG1; for Monocyte-MP cells: CD163, VCAN, LYZ, FMN1, MAFB, CD14, and FCN1; for dividing cells: MKI67, CENPF, ASPM, RRM2, and TPX2; for granulocytes: IL18RAP, MGAM, and ADGRG3; for HSC-CD34+ cells: ERG, CHRM3, MED12L, MEIS1, ZNF521, and CD109; for erythroblasts: CA1, SLC4A1, A
  • Pig genes of interest, associated with vascular cells/dividing cells, and overall damage markers/region include: HIF1A, IL17B, IL33, IL1RL1, IL1R1, CXCL14, PHLDA2, ICAM1, SELP, SELE, ADIRF, MYH11, CDC3, TP5311, CKB, MYLK, PIL6, CSPG4, CPE, C4A, GJA5, KCNMB1, OLFML2A, S100A2, THBS1, TIMP1, SERPINE1, ENO1, PIP5, and CCL19.
  • Genes known to be associated with PCXD, and that showed patterns of interest in the analysis include: CPM, PTX3, SLC5A6, CXCL14, PHLDA2, PPP1R14B, TNC, FAF1, IL1R1, HMOX1, F13A1, FERMT1, STOX1, LMAN2L, SLF1, TRIM44, TMEFF2, SLC40A1, GASK1B, CTSS, NTRK2, MS4A7, HBB, RND3, RHOJ, and AKR1B1.
  • CM interleukins
  • TAB2, IL17RB, PELI1, MEF2A, FBXW11, PSMB7, 217 314164997v1 Attorney Docket No: 243735.000437 IL13RA1, FOS, STAT5B, SOCS3, CDC42, MSN, PSMD14, CD80, N4BP1, PSMD5, BCL6, CAPZA1, PTK2B, IL1RAP, IL15, PSMC2, MMP2, RAP1B, PTPN12, USP14, PSMB2, COL1A2, VIM, ANXA1, FN1, CCL2, PSMA6, HMOX1, IL4R, PSMA4, PSMD6, PSMD12, YWHAZ, PSMC1, RIPK2, RPS6KA3, STAT3, CRLF1, TIMP1, CREB1, PSMC6, IL25, HIF1A, LYN, DUSP4, CEBPD, PSMA3, NFKBIA, GSTO1, and ANXA
  • CM
  • Pig markers of population of interest that displayed enrichment in specific pathways believed to be related to the response of the organ to transplantation, and found to be present in high proportion in D1 include, LEC3 marker genes: COL4A2, AGO2, ADAMTS9, SOCS5, 218 314164997v1 Attorney Docket No: 243735.000437 RAI14, PLXNA2, BIRC2, PLAT, CCNL1, CEMIP, SMAD3, PARVB, HIF1A, MARCHF3, SERPINE1, SEMA3A, CEMIP2, PALM2AKAP2, CHSY3, WDR43, MACF1, ABHD17C, VMP1, ARHGAP23, EMP1, NFKBIA, YBX3, TNIP1, PHF11, LONRF3, USP10, NOL11, HK1, LTBP1, EMP2, RIN3, ACKR2, TXNRD1, REL, NAV2, and TEAD4; and DIV VEC2 markers: LDB2, PLAT,
  • D1 developed an increased need for vasopressors, which led to evidence of visceral hypo-perfusion, including rising lactate and liver enzymes, indicating ischemic injury to end organs.
  • D1 explant tissue demonstrated progressive myocyte injury and cell death, which were not present in day 1 and 2 endomyocardial biopsies. Standard histology and immunohistology did not show features of hyperacute rejection in either daily biopsies or explant (Moazami, et al., 2023).
  • the two human decedent recipients of the pig heart xenografts were one male and one female, both of European ancestry.
  • the first xenotransplant was performed in an 82-kg, 72-year- old male recipient with a body mass index of 25.9 and a history of coronary artery bypass graft surgery and heart failure.
  • a heart from a 70-kg male pig that was 309 days old at procurement was used.
  • the total ischemia time was 4 hours and 21 minutes, of which 3 hours and 11 minutes was in cold static storage.
  • the second xenotransplantation recipient was a 57-kg, 64-year-old female with a body mass index of 22. She had two previous kidney and pancreas allotransplants and was maintained on oral mycophenolate and prednisone.
  • the immunosuppression regimen for both decedents did not include the co-stimulatory blockade drugs considered essential to support long-term survival of a xenograft (Cooper, et al., 2007; Schroder, et al., 2019).
  • CO/CI measurements commence approximately 24 h post-reperfusion. Cardiac function data was enhanced by Swan Ganz catheter data, offering quantitative insights into cardiac performance beyond the basic CO/CI metrics. Blood samples were collected at pre-transplant (0 h) and then at 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 65, 66 and 66.5 hours (terminal sample) for D1 and 0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60 and 66 hours (terminal sample) for D2. Table 9.
  • RNA-seq 7 across the two pig-to-human decedent cardiac xenotransplant procedures, spanning PBMCs and fresh-frozen tissue samples by bulk RNA-seq, targeted RNA profiling, scRNA-seq and snRNA- seq, and spatial transcriptomics as described herein.
  • Bulk RNA-seq and scRNA-seq were performed on blood samples collected at the time points above, except for the final three bulk RNA-seq time points from D1, which occurred at 60, 64 and 65 hours.
  • RNA-seq was performed to assess the concordance with scRNA- seq, as the latter is more prone to batch effects, with fewer numbers of starting input cells, leading to wider variance in cell-type proportions. Analyses were performed using venous blood collected into PaxGene tubes across all time points and processed as a single batch. For data analysis, FASTQ files were aligned to GRCh38 GENCODE Human release v.43 using STAR v.2.7-10a. STAR run parameters were adapted from the ENCODE RNA-seq pipeline for gene count quantification (ENCODE Project Consortium, 2012).
  • Counts were preprocessed by removing genes with fewer than an average of four counts across all samples.
  • gene DEA was performed on each contrast using DESeq2 (Love, et al., 2014).
  • Standalone analysis was conducted at the decedent level using the time after transplantation (in hours, with pre-transplantation set as 0) as a continuous variable, with DESeq2 (Love, et al., 2014). This was used to track expression shifts over the post-transplantation period.
  • PBMC scRNAseq Fresh PBMCs at each time point were collected by gradient centrifugation, followed by lysing any remaining red blood cells and cryopreservation according to the 10x Genomics protocol (protocol no. CG00039). Upon collection, the cryopreserved cells were thawed in four batches, then loaded for standard 10x Genomics scRNA-seq (v.3.1 Chemistry,10x Genomics). Initial scRNA-seq data processing was performed using the 223 314164997v1 Attorney Docket No: 243735.000437 CellRanger v.7.0.1. pipeline using the vendor-recommended workflow.
  • count matrices were processed using scanpy (Wolf, et al., 2018) v.1.9.3, with Python v.3.10.10.
  • quality control (QC) filters were applied as follows: maximum 50,000 counts; maximum 7,000 genes; maximum 20% mitochondrial gene expression; and minimum 500 genes.
  • UMIs unique molecular identifiers
  • Harmony Kersunsky, et al., 2019
  • harmonypy v.0.0.6 was used for batch correction, at the individual level (to integrate the two decedents) for main cell-type embedding and at the time-point level to correct for technical batch-effect and subtype analysis.
  • Tissue snRNA-seq/spatial transcriptomics For snRNA-seq, 4 ⁇ 50 ⁇ m OCT curls were used as starting material, from which nuclei were isolated using a Chromium Nuclei Isolation kit (10x Genomics). The isolated nuclei were resuspended in 50 ⁇ l wash and resuspension buffer, counted using an automated cell counter, immediately loaded into gel bead-in-emulsion as a single replicate and run according to the Chromium Single Cell 3’ kit (10x Genomics). For spatial transcriptomics, 10- ⁇ m OCT sections in duplicate were used (serial sections processed to single-nuclei curls).
  • GSE183448 which was acquired by 10x chromium snRNA-seq as above.
  • the raw fastq files were retrieved through the Sequence Read Archive and were processed with CellRanger using the same pig genome and velocyto (La Manno, et al., 2018).
  • DESeq2 (Love, et al., 2014) was run for each of the eight conditions (D1, D2, 1 h PMI, 7 h PMI, ECMO, and OrganEx), independently and each were compared to controls (0 h PMI, no ischemia).
  • D1, D2, 1 h PMI, 7 h PMI, ECMO, and OrganEx were selected and the downstream processing was performed again, starting from the raw counts matrix.
  • a resolution of 0.5 was selected for Leiden subclustering.
  • Subcluster-specific marker genes were obtained with a Wilcoxon rank-sum DEA, which output was used in GSEA (using the GSEApy (Fang, et al., 2023) v.1.0.5 prerank function) obtained pathways enriched in each subcluster.
  • the whole integrated snRNA-seq data were used, with the count matrix for all samples and the nuclei main cell-type annotation was passed to CellChat (Jin, et al., 2021) (v.2.0.0).
  • the CellChat tutorial was followed to infer the cellular communication network for each condition and to compare the strength and number of interactions between the conditions.
  • the six samples (2 ⁇ D1-LV, 2 ⁇ D2-RV, and 2 ⁇ D1-RV) were processed using CellRanger and the mixed genome. They were further processed by scanpy (Wolf, et al., 2018). Reads aligned to the human genome were discarded.
  • Barcoded spots were filtered based on a maximum of 30,000 counts and 5,000 genes, a minimum of 100 counts and genes and a mitochondrial filter set at 40%. Data were processed as with the single-cell protocols, encompassing normalization, logarithmization, selection of highly variable genes, standard regression based on total counts per spot, scaling, PCA, UMAP dimension reduction, and Leiden clustering. For deconvolution, two methods were used to infer the cell-type composition of the spatial transcriptomics spots, using the annotated snRNA-seq data (from the matched samples) as the expression references.
  • Cell2location (Kleshchevnikov, et al., 2022) v.0.1.3 was run according to the documentation’s tutorial, using the sample origin as the batch key, a detection_alpha value of 150 and a number of expected cells per location of five (N_cells_per_location).
  • DestVI (Lopez, et al., 2022) (DestVI-utils v.0.1.0 and scvi-tools v.1.0.4) was run following the documentation’s tutorial, to obtain predicted cell-type proportions. [00390] All enrichments were run using GSEApy (Fang, et al., 2023), the prerank module was used for GSEA, and the enrichr module was used for over representation analysis.
  • Proteomics Proteomic profiling of plasma samples was performed by 16-Plex tandem mass tag (TMT)-based mass spectrometry (MS). For a high-throughput TMT-based MS study, a pool of all samples was used as a reference across different runs. Protein quantification and QC were performed as previously described (Andrijevic, et al., 2022). In brief, in each MS run, pep- tide spectrum matches (PSMs) without quantification were removed before sum normalization with the reference channel. Only PSMs with unique protein groups were selected that had an SPS match of >65 and a co-isolation interference of ⁇ 50.
  • TMT 16-Plex tandem mass tag
  • MS mass spectrometry
  • Metabolomics and lipidomics Sample preparation plasma: Metabolites and complex lipids were extracted using a biphasic separation with cold methyl tert-butyl ether (MTBE), methanol, and water in a deep-well plate format. In brief, 1 ml ice-cold MTBE and 260 ⁇ l methanol was added to 40 ⁇ l plasma spiked with 40 ⁇ l deuterated lipid internal standards (cat. no.5040156, Sciex). Samples were agitated at 4 °C for 30 minutes.
  • MTBE cold methyl tert-butyl ether
  • HILIC experiments were performed using a ZIC- HILIC column 2.1 ⁇ 100 mm, 3.5 ⁇ m, 200 ⁇ (Millipore) and mobile-phase solvents consisting of 10 mM ammonium acetate in 50:50 acetonitrile:water (A) and 10 mM ammonium acetate in 95:5 acetonitrile:water (B).
  • RPLC experiments were performed using a Zorbax SBaq column 2.1 ⁇ 50 mm, 1.7 ⁇ m, 100 ⁇ (Agilent) and mobile-phase solvents consisting of 0.06% acetic acid in water (A) and 0.06% acetic acid in methanol (B).
  • Lipidomics data were reported by Shotgun Lipidomic Assistant (v.1.21) (Su, et al., 2021) software, which calculates concentrations for each detected lipid as the average intensity of the analyte MRM/average intensity of the most structurally similar internal standard (IS) MRM multiplied by its concentration.
  • Cytokine panels C-reactive protein (CRP), D-dimer, ferritin, lactate, lactate dehydrogenase, interferon- ⁇ , IL-1 ⁇ , IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-13 and IL-17 and soluble IL-2R and TNF, were measured in both xenograft transplant decedents as previously described (Moazami, et al., 2023) and using the same time points as above, for the Multiplex Human Cytokine/Chemokine/Growth Factor Panel A 48 Plex kit (Millipore Sigma) run on the Luminex 200 System (Millipore Sigma).
  • DEA DEA was performed for D1 and D2 separately. Then DEA was performed using a linear model ( ⁇ individual + stage) followed by empirical Bayes moderation with the limma package.
  • Integrative analysis methodology Processed bulk RNA-seq (PBMCs) and cytokine, metabolomic, lipidomic and proteomic data from plasma were normalized and scaled to z-scores. Integrative analysis was performed using fuzzy c-means clustering with the mfuzz (v.2.62) package to identify multi-omics analytes that had similar temporal expression patterns across the heart transplant time course.
  • the xenograft tissues were processed in a Leica ASP300 (Leica Biosystems) following a 1 h, 13-step program, and paraffin-embedded using standard procedures.
  • the 5- ⁇ m tissue sections were collected onto Plus slides (Fisher Scientific), air dried and stored at room temperature before use. Each sample was histochemically stained with H&E and Masson’s trichrome (Carson). H&E stains and EM were assessed by an expert clinical transplant pathologist.
  • Transmission EM Explanted heart tissues were fixed and stored in 2.5% glutaraldehyde and 2% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4) at 4 °C.
  • the samples were washed three times with 0.1 M phosphate buffer (10 minutes each time), post-fixed with 1% osmium tetroxide for 1.5 h at room temperature and dehydrated in a serial of ethanol solutions (30%, 50%, 70%, 85%, 95%, and 100% for 15 min each).
  • the heart tissue was then washed in propylene oxide twice for 5 min each, infiltrated with propylene oxide and embedded in EMbed 812 (Electron Microscopy Sciences). Semi-thin sections were cut at 500 nm and stained with 1% toluidine blue to find the best orientation of the heart tissue.
  • Ultrathin sections 230 314164997v1 Attorney Docket No: 243735.000437 of 70 nm were cut, mounted on 200-mesh copper grids, stained with uranyl acetate and lead citrate by standard methods, and imaged with a JEOL 1400 Flash transmission electron microscope (Japan) using a Rio16 camera (Gatan).
  • Flow cytometry PBMC samples were thawed and rested for 1 h at 37 °C in RPMI supplemented with 10% FBS. Then, cells were treated with Human TruStain FcX (BioLegend) and NovaBlock (Thermo Fisher Scientific) for 10 min at room temperature.
  • CD4 (1:300 dilution, clone SK3, BioLegend, 344851); CD8 (1:1,000 dilu- tion, clone SK1, BioLegend, 344762); CD14 (1:1,000 dilution, clone M5E2, BD, 612902); CD16 (1:1,000 dilution, clone 3G8, BD, 566171); CD19 (1:100 dilution, clone SJ25C1, BD, 612939); CD27 (1:100 dilution, clone O323, BioLegend, 302833); CD38 (1:300 dilution, clone HIT2, BioLegend, 303549); CD56 (1:50 dilution, clone 5.1H11, BioLegend, 362539); HLA-DR (1:1,000 dilution, clone G46
  • PBMCs were then permeabilized with the eBioscience Foxp3/ Transcription Factor Staining Buffer Set (Thermo Fisher) for 20 minutes at room temperature in the dark, followed by 1 h intracellular staining at room temperature in the dark for the following antibodies: CD3 (1:500 dilution, clone SK7, BioLegend, 344851). Cells were resuspended in 1% paraformaldehyde (Electron Microscopy Sciences). All samples were acquired on a five-laser Aurora cytometer (Cytek Biosciences) and data were analyzed using FlowJo (v.10.10.0, BD Biosciences).
  • PBMC samples were collected from the recipient in case 2 at the following timepoints: pre-transplant (0h) and then at 6h, 12h, 24h, 48h, and 53h (right before termination) post-xenotransplantation.
  • Sample preparation for bulk and single-cell RNA-seq Freshly collected kidney tissues were minced rapidly in a cell culture dish followed by washing with DPBS buffer.
  • Minced tissue was resuspended in 5mL of 37°C pre-warmed 0.25% Trypsin plus TURBO DNase (10U/mL final concentration) for 20 min with mechanical dissociation using serological pipette every 5 min. Following digestion, dissociation mix was filtered through a 100 ⁇ m strainer. The remaining large tissues on the strainer were further ground and washed to collect additional cells. Cell debris and dead cells were removed using Miltenyi Biotech Dead Cell Removal Kit. Cells were counted using Countess II Automated Cell Counter before loading for single-cell RNA-seq using 10X Genomics Chromium Single Cell 3' Kit (v3.1 Chemistry).
  • RNA-seq For bulk RNA-seq, kidney core biopsy samples were homogenized using a pestle homogenizer and total RNA was extracted from lysate using a QIAGEN RNeasy Plus kit. Total RNA quality and quantity were examined using the RNA 6000 Nano Kit and Agilent 2100 Bioanalyzer. Bulk RNA-seq library was constructed following the Illumina Ribo-Zero Plus rRNA Depletion Kit protocol (protocol number: 20037135). 232 314164997v1 Attorney Docket No: 243735.000437 [00412] Longitudinal single-cell RNA-seq of the recipient’s PBMCs were prepared at the same time after collecting all samples across timepoints.
  • Fresh PBMCs of each timepoint were first collected through gradient centrifugation and any remaining RBCs were removed using the ACK lysing buffer followed by washing by the DPBS buffer.
  • the prepared PBMCs for each time point were cryopreserved according to the 10X Genomics protocol (protocol number: CG00039). Upon collecting all PBMC samples, the frozen cells were thawed at the same time, then loaded for single-cell RNA-seq using 10X Genomics Chromium Single Cell 3' Kit (v3.1 Chemistry).
  • 10X Genomics protocol protocol number: CG00039
  • Kidney single-cell sequencing data were separately mapped to both human genome (GRCh38) and porcine genome (Sscrofa11) to distinguish species origin of cells, whereas recipient PBMC single cell data were mapped to human genome (GRCh38) only.
  • Single-cell raw expression matrices were then processed and analyzed using Seurat v4.3.0 (Butler, et al., 2018). Cells with less than 500 expressed genes, 1000 UMI, or more than 8% of transcripts from mitochondrial genes were removed from the kidney single cells, resulting in a total of 23,868 cells.
  • human-porcine one-to-one homologous genes single cells with greater proportion of transcripts mapped to the porcine genome than the human genome are identified to be porcine cells, and vice versa to be human cells.
  • PBMC single-cell RNA-seq datasets were filtered by removing extra-low-quality cells with less than 200 expressed genes or higher than 20% of mitochondrial genes, resulting in a total of 31,321 cells across 6 time points.
  • Differential expression analysis Differentially expressed genes between the control and xenograft samples were assessed using the Seurat FindMarkers function, whose transcripts were detected in at least 20% of cells, with a log-fold-change threshold of 0.25, and each identified DEG were tested for statistical significance using the Wilcoxon rank sum test. Differentially expressed genes with log2 fold change > 0.5, and p-value ⁇ 0.01 were filtered as genes of interest for further analysis.
  • Cell cycle analyses Cell cycle scoring was applied to assess the cycling status of single cells.
  • CellCycleScoring function of Seurat computes a score for each cell cycle phase (G1, G2/M and S) and clustering cells into the three phases of cell cycles by summing the expression levels of the phase-specific marker genes. The cell cycle scores and phase assignments were annotated for each cell, and followed by secondary validation of cell cycle marker gene expressions.
  • Temporal gene set coexpression clustering analysis To minimize the effect of noise on temporal gene expression values in PBMC data, the corrected UMI counts matrix adjusted for sequencing depth from the SCT assay was used, normalized by gene using centered log ratio transformation by applying the Seurat NormalizeData function, then scaled using Seurat ScaleData function for each cell type independently.
  • a set of temporally variable genes was identified by using Seurat FindVariableFeatures and mitochondrial genes (MT), ribosomal genes (RP), or genes whose normalized expression was detected in ⁇ 20% of all cells within the given cell type were filtered.
  • Temporally enriched gene set identification In each cell type, co-expressed gene sets with strong evidence of temporal enrichment were identified.
  • GSEA Gene set enrichment analysis for functional annotations: All Gene Ontology (GO) term (Ashburner, et al., 2000) and Kyoto Encyclopedia of Genes and Genomes (KEGG) term (Kanehisa, et al., 2000) enrichment analyses were performed using clusterProfiler (v4.8.2) (Wu, et al., 2021). GO terms generated from the least number of overlapping terms were visualized for GO term analyses of PBMC data. All p-values in the GSEA were adjusted for multiple tests using the Benjamin-Hochberg method (Benjamini, et al., 1995).
  • RNA-seq of kidney biopsy samples The bulk RNA-seq data across 0, 12, 24, and 48 hours pXTx was analyzed to understand the temporal changes in gene expression between the control and xenograft samples. Two technical replicates were conducted at 0 hours post-transplantation from the control kidneys. Gene expression at time 0 was calculated by the average value between the two replicates. The log2 fold change in gene expression was calculated for each gene across the different time points relative to the expression value at time 0. To calculate the human to pig raw count reads ratio (H/P Ratio), the absolute raw reads mapped to the human genome for every gene at a specific time point was divided by the absolute raw reads mapped to the porcine genome.
  • H/P Ratio human to pig raw count reads ratio
  • Clinical subject The decedent was a 57-year-old male who died from complications related to a biopsy performed on grade 4 glioblastoma. He was declared dead based on neurologic criteria, and was ruled out as an organ donor due to the glioblastoma.
  • Clinical samples and data Figure 44 outlines the different omics assays performed at each timepoint, along with immunosuppressive medications, clinical microbiological testing, and significant clinical events.
  • Pig xenograft organ selection and procurement The genetically modified pig GalSafeTM thymokidney was supplied by Revivicor.
  • Spatial RNA expression profiling For the 478 probes panel: Spatial RNA expression profiling within transplanted pig kidney tissue was performed using a custom 478-gene panel on a Xenium In Situ Analyzer (10x Genomics). A total of seven biopsies acquired over the full time-course were assessed ( Figure 44).
  • FFPE paraffin-embedded
  • Xenium probes for 478 mRNAs encompassing 129 human and 349 pig cell-specific markers and additional genes of interest (outlined in Tables 10-11), were designed according to the manufacturer’s instructions (10x Genomics). The custom probe set was then used to label and detect mRNAs in kidney tissues. Seven xenograft samples were processed and analyzed across two runs, according to the protocols described above. Regions of interest were selected on the Xenium Analyzer to include the totality of kidney tissue within each section. A second serial tissue section was stained with hematoxylin and eosin (H&E) and imaged using bright-field microscopy.
  • H&E hematoxylin and eosin
  • nuclei identification and cell segmentation were performed using Xenium Analyzer software to generate a cell/gene count matrix. This dataset was subsequently analyzed in python using Squidpy (Palla, et al., 2022) and Scanpy (Wolf, et al., 2018). All samples were loaded using the scanpy.read_10x_h5 function on the cell_feature_matrix.h5. Transcripts with a nucleus distance parameter greater than 0.1 were excluded from analysis in order to minimize transcript-cell misassignment and cells with less than 20 assigned transcripts were excluded.
  • a human_expression_fraction metric was subsequently calculated for each cell, defined as the fraction of raw counts originating from human transcripts.
  • a cell was considered to be of human origin when more than half of the transcripts originated from human probes.
  • the count matrix was normalized and logarithmized.
  • human and pig cells were separated into two independent matrices, where pig genes were removed from the pig matrix and human genes from the human matrix.
  • PCA and UMAP Uniform Manifold Approximation and Projection
  • HLA-DRA HLA-DRB1, CXCL9, HLA-DPA1, HLA-DPB1
  • HLA-DRB1 genes from the Loupy, et al. AbMR signature and present in the assay herein were selected. The average expression per cell was calculated, and cells were classified as “AbMR” or “Other” based on a 0.9 log-normalized expression threshold. This binary classification was then spatially mapped to Day 33 tissue and analyzed across time points.
  • Table 10.478 ST Xenium panel design List of the 478 genes constituting the panel, with the genome name (GRCh38 for human, Sscrofa11 for pig), their symbol, and Ensembl IDs.
  • Kidney xenograft core biopsies were collected at multiple time points, including post-reperfusion (POD 0), POD 21, POD 33, POD 45, and a wedge biopsy of the explant (POD 61). Additionally, a biopsy from the pig’s contralateral kidney and decedent’s native kidney were included.
  • APPV targets Two additional APPV targets were included as part of the 100 targets for the positive and negative sense strands using p7 (2 probes), NS3 (4 probes) and NS5 genes (4 probes) for a total of 10 independent probes.5uM sections from each FFPE block were used for H&E staining and processing on the Xenium platform following the 10x Genomics Xenium In Situ for FFPE – Deparaffinization & Decrosslinking protocol and the Xenium Prime In Situ Gene Expression with optional Cell Segmentation Staining protocol (10x Genomics). Data processing was performed the same as described for the 478-panel (described herein) with the modification that cells with fewer than 70 transcripts were excluded for the larger panel.
  • the integrated matrix was scaled, followed by PCA and UMAP embedding.
  • Clusters mapping exclusively to the native human kidney samples were removed, as they represent human kidney parenchymal cells. All other human clusters were included in the final dataset as they contained human cells detected during the time-course. After reprocessing the remaining cells (from log-normalized counts), a human cell cluster was identified (Figure 49D) that was characterized by high human gene expression.
  • GSEA Gene set enrichment analysis
  • RNA was extracted from whole blood samples across 27 timepoints (see Figure 44) collected in PAXgene Blood RNA Tubes (BD Biosciences) using the PAXgene Blood RNA extraction kit (Qiagen). Purity was assessed using the ThermoFisher NanoDrop One. Purified RNA was quantified using the Qubit RNA Broad Range Assay Kit (ThermoFisher Scientific). The RNA Integrity Number (RIN) equivalent was determined using the RNA ScreenTape on the 4150 TapeStation (Agilent).
  • TCR libraries were generated using the Takara SMARTER Human TCR a/b Profiling Kit v2 and BCR libraries were generated using the Takara SMART-Seq Human BCR (with UMIs) kit (Takara). Libraries were pooled and sequenced using the iSeq 100 and a 50 x 8 x 8 x 50 cycle run to allow for index balancing.
  • cryopreserved cells were thawed in four batches and then loaded for standard 10X Genomics single-cell RNA-seq (v3.1 Chemistry, 10x Genomics).22 PBMC scRNA-seq samples were obtained, each for a different timepoint.
  • Initial scRNA-seq data processing was performed using the CellRanger pipeline with vendor-recommended workflows. After CellRanger preprocessing, the count matrices were processed using scanpy. Briefly, QC filters were applied as follows: a maximum of 50,000 counts, a maximum of 7,000 genes, a maximum of 20% mitochondrial gene expression, and a minimum of 500 genes.
  • Cell types were identified based on marker genes and gene set enrichment, and specific cell type subtypes were identified and validated by comparing marker gene expression to Azimuth markers (specifically the PBMC dataset (Hao, et al., 2021)).
  • Azimuth markers specifically the PBMC dataset (Hao, et al., 2021)
  • subclustering analysis was performed. A subset of the count matrix (normalized and logarithmized) containing only cells of the population of interest was created. Subsequently, highly variable gene selection, PCA, UMAP and leiden were once again performed on the subpopulation, resulting in subclusters, which annotated based on their marker genes expression.
  • PBMC bulk RNA-seq Bulk RNA-seq on PBMCs was performed to assess concordance with the scRNA-seq results.
  • WTS libraries were generated using the Illumina Stranded Total RNA Prep, Ligation with Ribo-Zero Plus library preparation kit (Illumina). Libraries underwent quantification using the KAPA Library Quant (Roche) Primers & Universal qPCR Mix and fragment analysis using the Agilent Fragment Analyzer 5200 (Agilent).
  • RNA shield buffer Zymo Research, ZR1100-50
  • RNeasy Plus kit QIAGEN, 74134
  • RNA qualities were evaluated using an RNA quality and quantity nano assay kit on a bioanalyzer.
  • RNA libraries were constructed using the TruSeq stranded total RNA, with Ribo Zero Gold library prep kit. RNA libraries were sequenced with an Illumina NovaSeq6000 SP 100 Cycle Flow Cell v1.5.
  • Tissue snRNA-seq OCT-embedded samples (POD 0, POD 10, POD 14, POD 21, POD 33, POD 49, POD 56, POD 61) were processed using the Miltenyi gentleMACs Octo Dissociator.
  • Isolated nuclei were resuspended in 50 ⁇ L of 1x PBS (Phosphate Buffered Saline) 303 314164997v1 Attorney Docket No: 243735.000437 and fixed using the Scale Biosciences fixation kit (2020001) following the low input fixation protocol. Each fixed sample was distributed across the 96 wells of the first barcoding plate in the Scale Biosciences RNA kit (950884), with a target loading amount of 10,000 cells per well. Samples were taken through reverse transcription and then spread across the second barcoding ligation step. Finally, nuclei were distributed across the last barcoding plate at a count of 1,600 cells per well.
  • 1x PBS Phosphate Buffered Saline
  • CellBender (Fleming, et al., 2019) v3 was applied to the unfiltered matrices to remove empty droplets and ambient RNA contamination.
  • the initial step involved the species-level partition of human and pig nuclei.
  • Cells underwent a preliminary filtering step ( ⁇ 50 detected genes), followed by normalization and log transformation of counts. Highly variable genes were identified using Scanpy, and the integrated matrix was scaled prior to PCA and UMAP embedding.
  • nuclei within this cluster were classified as human-derived, whereas all remaining nuclei were classified as pig-derived. Subsequent analyses were conducted separately for human and pig populations. For each, raw count data (prior to normalization and log transformation) were retrieved, retaining only human genes for human nuclei and pig genes for pig nuclei. QC-based filtering was then applied independently to both datasets as follows. Human nuclei: gene number cutoffs (max: 4,000, min: 100), UMI counts cutoffs (min: 200, max: 10,000), mitochondrial expression cutoff (max: 6%).
  • Pig nuclei gene number cutoffs (max: 6,000, min: 500), UMI counts cutoffs (min: 500, max: 20,000), mitochondrial expression cutoff (max: 6%).
  • clusters were manually assigned based on canonical marker gene expression ( Figures 64 and 66).
  • lymphocyte and NK cell clusters were further subclustered by selecting relevant cells and reprocessing from the normalized, log- transformed counts, allowing for higher-resolution characterization of immune cell subsets (Figure 65).
  • Antibody Clone Fluorochrome Supplier Catalog Lot number 305 314164997v1 Attorney Docket No: 243735.000437 Foxp3 PCH101 PE-Cy5.5 Invitrogen 35-4776-42 2065635 well plates in a randomized manner and processed on a SP100 Automation Instrument using two nanoparticle suspensions (XT1 and XT2) from the Proteograph XT Assay Kit, as per manufacturer’s instructions (Seer, Inc.).
  • MS analysis was performed on an Orbitrap Astral mass spectrometer (Thermo Fisher). Approximately, ⁇ 380-400 ng of material per NP suspension was injected in Data Independent Acquisition (DIA) mode using m/z 3 isolation windows from m/z 380–980. MS1 scans were acquired at 240k resolution and MS2 at 80k resolution. LC-MS raw files were analyzed using the Proteograph Analysis Suite (PAS) with the data processed using DIA-NN 1.8.1 and a PAS library-free search with the cloud scalable Match Between Run (MBR) option applied.
  • PAS Proteograph Analysis Suite
  • MRR cloud scalable Match Between Run
  • TLR7 stimulation in human plasmacytoid dendritic cells leads to the induction of early IFN-inducible genes in the absence of type I IFN.
  • Mancebo, E. et al. High proportion of CD95(+) and CD38(+) in cultured CD8(+) T cells predicts acute rejection and infection, respectively, in kidney recipients.
  • OPN Osteopontin
  • SPP1 central nervous system
  • GSEA Gene Set Enrichment Analysis

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Abstract

The present invention relates to methods of detecting a xenograft rejection in a subject, monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said methods comprising determination of expression levels of certain human or porcine genes and comparing expression levels of said genes.

Description

Attorney Docket No: 243735.000437 BIOMARKERS FOR XENOGRAFT REJECTION CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to U.S. Provisional Patent Application No.63/649,853, filed May 20, 2024, and U.S. Provisional Patent Application No.63/664,311, filed June 26, 2024, the disclosures of which are herein incorporated by reference in their entireties. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH [0002] This invention was made with government support under HG009491, OD033430, P30 CA016087, and AI144522 awarded by the National Institutes of Health. The government has certain rights in the invention. SEQUENCE LISTING [0003] The instant application contains a Sequence Listing which has been submitted electronically in XML format and is hereby incorporated by reference in its entirety. Said XML copy, created on May 19, 2025, is named 243735_000437_SL.xml and is 18,223 bytes in size. FIELD OF THE INVENTION [0004] The present invention relates to methods of detecting a xenograft rejection in a subject, monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said methods comprising determination of expression levels of certain human or porcine genes and comparing expression levels of said genes. BACKGROUND [0005] Organ transplantation is a life-saving procedure for end-stage organ failure patients. Severe shortage of donor organs has been a worldwide challenge (Sykes, et al., 2022; Matas, et al., 2023) and remains a major limitation within human allo-transplantation, with waiting lists greatly outpacing the number of donors available. Over 100,000 people are currently on the 1 314164997v1 Attorney Docket No: 243735.000437 transplant waiting list in the United States, and due to organ shortages, only ~1 in 4 of those will ever receive an allograft (national U.S. data, OPTN). Exploring alternative sources of organs for transplantation to meet the increasing demand is considered to be a health care imperative. Xenotransplantation (transplantation of organs across species) of genetically engineered organs or tissues from different species has emerged as a promising solution to address this challenge (Wolbrom, et al., 2023). Genetically modified pig organs offer several significant advantages for transplantation into humans and may help alleviate the current shortage of suitable organs (Cooper, et al., 2023; Wolbrom, et al., 2023). The domesticated pig (Sus scrofa domesticus) has been identified as a suitable species due to its size/physiology similarities to human organs, quick maturation to near adult size in 6 months, and positive public perception (Cooper, et al., 2002; Elisseeff, et al., 2021). Organs from 6-10-month-old pigs are the most suitable for xenotransplantation (Hryhorowicz, et al., 2017). SUMMARY OF THE INVENTION [0006] In one aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0007] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, 2 314164997v1 Attorney Docket No: 243735.000437 PRDM1, NUGGC, and SDC1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs); and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0008] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0009] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0010] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1, wherein the sample is a xenograft tissue biopsy; and 3 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0011] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0012] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0013] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1, wherein the sample is a xenograft tissue biopsy; and 4 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0014] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD3G, THEMIS, CD5, and CD6, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0015] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of CD8B and/or CD8A, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the gene(s) determined in step (a) to a corresponding control. [0016] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRG1, GZMK, CST7, and GZMH, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0017] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft 5 314164997v1 Attorney Docket No: 243735.000437 rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0018] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD4, CD40LG, CCR2, CCR6, and DPP4, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0019] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0020] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: 6 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0021] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GZMA, PRF1, and FASLG, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0022] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes TOP2A, TYMS, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0023] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1, wherein the sample is a xenograft tissue biopsy; and 7 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0024] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0025] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0026] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, 8 314164997v1 Attorney Docket No: 243735.000437 LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0027] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0028] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft 9 314164997v1 Attorney Docket No: 243735.000437 rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0029] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0030] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A, wherein the sample is a xenograft tissue biopsy; and 10 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0031] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0032] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0033] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, 11 314164997v1 Attorney Docket No: 243735.000437 CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0034] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, 12 314164997v1 Attorney Docket No: 243735.000437 SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0035] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0036] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft 13 314164997v1 Attorney Docket No: 243735.000437 rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [0037] In some embodiments, the method further comprises step c) (i) determining that the subject has a xenograft rejection or is at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are increased as compared to the control by 1.5-fold or more; or (ii) determining that the subject does not have a xenograft rejection or is not at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are decreased, not increased or increased by less than 1.5-fold as compared to the control. [0038] In some embodiments, the control is a predetermined value or a value determined from a sample taken from the subject before the xenograft transplantation. In some embodiments, the control may be a value determined from a sample taken from the subject at a quiescent timepoint post xenograft transplant where there are nonrejection tractors. 14 314164997v1 Attorney Docket No: 243735.000437 [0039] In some embodiments, the control is a predetermined value or a value determined from a sample taken from the xenograft before the xenograft transplantation. In some embodiments, the control may be a value determined from a sample taken from the xenograft at a quiescent timepoint post xenograft transplant where there are nonrejection tractors. [0040] In some embodiments, the method further comprises administering to the subject a treatment that targets plasma cells and/or a complement inhibitory agent, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. [0041] In some embodiments, the method further comprises administering to the subject a treatment that targets T cells when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. [0042] In some embodiments, the method further comprises administering to the subject a treatment that targets plasma cells, a treatment that targets T cells, a complement inhibitory agent, or a combination thereof, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. [0043] In some embodiments, the treatment that targets plasma cells is plasmapheresis. [0044] In some embodiments, the treatment that targets T cells is rabbit anti-thymocyte globulin (rATG). [0045] In some embodiments, wherein the complement inhibitory agent is a C3 inhibitor. [0046] In some embodiments, the method further comprises administering an immunosuppressive agent to the subject when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. [0047] In some embodiments, the subject has received a kidney, heart, lung, liver, bone marrow, pancreas, or islet cell transplantation from a porcine donor. [0048] In some embodiments, the expression levels are determined based on RNA expression, protein expression, epigenetic regulation, or a combination thereof. [0049] In some embodiments, the expression levels are determined using RNA sequencing, targeted RNA panel, a quantitative PCR assay, an antibody-based method, an epigenetic assay, a single-cell technology, a spatial transcriptomics technology, or a multiplexed approach combining RNA and protein measurements. [0050] In some embodiments, the antibody-based method is flow cytometry, immunohistochemistry, or enzyme-linked immunosorbent assay (ELISA). 15 314164997v1 Attorney Docket No: 243735.000437 [0051] In some embodiments, the single-cell technology is single-cell RNA sequencing (scRNA-seq) or cellular indexing of transcriptomes and epitopes (CITE-seq). [0052] In some embodiments, the sample is collected from the subject 3 days after the xenograft transplantation. [0053] In some embodiments, the method comprises obtaining two or more samples from the subject at different time points after the xenograft transplantation and repeating the method for each sample. [0054] In another aspect, provided herein is a kit comprising: 1) one or more sets of probe nucleic acids useful for detecting two or more genes selected from: i. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD; ii. human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1; iii. human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY; iv. human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14; v. human genes IRF7, TLR7, TLR9, MYD88, and STAT1; vi. human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3; vii. human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A; viii. human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1; ix. human genes CD3G, THEMIS, CD5, and CD6; x. human genes CD8B, and CD8A; xi. human genes KLRG1, GZMK, CST7, and GZMH; xii. human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7; xiii. human genes CD4, CD40LG, CCR2, CCR6, DPP4; xiv. human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA; xv. human genes ITGAE, CD38, TNFRSF9, and MKI67; xvi. human genes GZMA, PRF1, and FASLG; 16 314164997v1 Attorney Docket No: 243735.000437 xvii. human genes TOP2A, TYMS, and MKI67; xviii. human genes GBP1, IRF1, TAP1, WARS1, and IDO1; xix. human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2; xx. human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, CXCR2; xxi. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2; xxii. human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2; xxiii. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, 17 314164997v1 Attorney Docket No: 243735.000437 IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67; xxiv. porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP; xxv. porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A; xxvi. porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1; xxvii. porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1; and/or xxviii. porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1; or any combination of the genes listed above, and 2) optionally, packaging and/or instructions for using the same. BRIEF DESCRIPTION OF THE DRAWINGS [0055] Figure 1 shows study design and sampling timeline. For both decedents, left and right ventricle tissue samples were obtained 66 h post-transplantation for snRNA-seq and spatial transcriptomic analyses. Peripheral blood samples were collected at 6-h intervals for comprehensive analysis, including metabolomic, proteomic, lipidomic, cytokine profiling, bulk 18 314164997v1 Attorney Docket No: 243735.000437 RNA-seq, scRNA-seq and flow cytometry. Specifically, for D2, tissue samples at 66 h were analyzed via scRNA-seq and bulk RNA-seq. For D1, blood samples at 64 h, 65 h and 65.5 h were exclusively collected. The figure’s lower section illustrates the timeline of blood assay execution post-transplantation. The timing at which each assay from blood was carried out is indicated with a circle for sampling from both decedents and diamonds or triangles for sampling from only D1 or D2, respectively. Routine clinical measurements including INR, ALT and AST, in addition to arterial blood gases, comprehensive metabolic panels, complete blood counts, lactate dehydrogenase and troponin levels were measured via blood. This figure was created with BioRender. [0056] Figures 2A-H show transcriptomic signature of PBMCs following transplantation. Figures 2A-C show single-cell RNA-seq derived proportion (percentages) of the indicated cell types at the indicated time points for D1 (left) and D2 (right) in all PBMC cell types (Figure 2A), T/NK cells subtypes (Figure 2B) and B cells (Figure 2C). Percentages are calculated based on the total number of cells in each sample. Figures 2D-F show bulk RNA-seq expression of cell-type specific markers, to validate scRNA-seq based cell-type proportions (shown on the left for T/NK and B cells, in Figure 2D and Figure 2F, respectively). For bulk RNA-seq, min– max normalized expression (transcript per million (TPM), length normalized) from B and NK/T cell marker genes (Figures 18I-J) were averaged for each sample (shown on the right in Figure 2D and Figure 2F, respectively) and for marker genes of CD8+ and CD4+ T cells in Figure 2E. Figure 2G shows validation of the proportions of different cell types using flow cytometry. PBMCs were subdivided based on protein expression into NK cells (CD56+), CD4+ T cells (CD56−CD3+CD4+CD8−), CD8+ T cells (CD56−CD3+CD8+CD4−), CD19+ B cells (CD56−CD3−CD19+) and plasmablast B cells (CD56−CD3−CD19+CD27+CD38+). Figure 2H shows the top 20 enriched pathways (GSEA) in D1 from the bulk RNA-seq DEA. The gray scale depicts the normalized enrichment score (NES) indicating negative and positive scores. A white star denotes that the false discovery rate (FDR) is below 0.05 and the dot size denotes the negative logarithm of the FDR. [0057] Figures 3A-D show integration of transcriptomic, proteomic, metabolomic, lipidomic and cytokine analyses. Figures 3A-B show integrative clustering of bulk omics and cytokines using fuzzy c-means clustering across all time points. Normalized multi-omics analyte levels revealed trends of upregulation from 42 h onward post-xenotransplant, particularly evident in D1 19 314164997v1 Attorney Docket No: 243735.000437 and correlated with clinical variables INR, AST and ALT in the time course. Shown are data for D1 (Figure 3A) and D2 (Figure 3B). The different omics are in greyscale according to the legend. Figure 3C shows pathways that are significantly associated with the cluster of interest. The highlighted cluster was one of an optimal number of clusters based on identifying the minimum distance between cluster centroids during soft clustering. Hits pertains to the number of analytes from the cluster of interest that are involved in the significant pathway. Figure 3D shows temporal distribution of cytokine levels (IL-6, IL-8, IL-10 and IL-13) (indicated by the line) and analyte upregulation in the cluster of interest, for both decedents. [0058] Figures 4A-L show xenograft snRNA-seq analysis. Figure 4A shows a cell-type 2D map of pig nuclei snRNA-seq data, showing all xenograft samples (both decedents) integrated with the publicly available (Andrijevic, et al., 2022) in vivo pig heart snRNA-seq data. LEC, lymphatic endothelial cells; L2, lymphoid cells 2; L3, lymphoid cells 3; L4, lymphoid cells 4; MP, macrophages/monocytes; DIV, dividing cells; SC, Schwann cells. Figure 4B shows cell- type percentage distribution. D1 LV/RV and D2 LV/RV denote the left and right ventricles in D1 and D2, respectively. PMI times of 0 h, 1 h and 7 h indicate ischemia durations, as per the publicly available in vivo pig heart snRNA-seq data (Andrijevic, et al., 2022). ‘ECMO’ and ‘OrganEx’ are reperfusion conditions within this dataset. Figure 4C shows transcriptomic 2D map of human nuclei from the xenograft (snRNA-seq) by cell type. Figure 4D shows human NK/T nuclei count distribution. Figure 4E shows the number of significantly (Padj < 0.05) overexpressed (log fold change (FC) > 0) and underexpressed (logFC < 0) DEGs, comparing each condition to 0 h of ischemia. DEA was performed using DESeq2 on Pseudobulk raw counts, calculated independently for each cell type per sample. The logFC and adjusted P value (Wald test P value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output. Figures 4F-G show enrichment of biological pathways in CMs (Figure 4F) and FBs (Figure 4G) from DE results. The top pathways enriched in D1 xenograft versus control are shown, with enrichments calculated using GSEA of DESeq2 results. Greyscale depicts the NES indicating negative and positive scores. A white star denotes an FDR below 0.05 and the dot size denotes the negative logarithm of the FDR. Figures 4H-J shows subclustering analysis of FBs (all samples integrated). Figure 4H shows a two-dimension map of FBs, sorted by their clusters. Figure 4I shows cell cycle score distribution by subclusters. Figure 4J shows proportion distribution for subclusters FB-4 and FB-6. Percentages based on total 20 314164997v1 Attorney Docket No: 243735.000437 number of FBs in each sample. Figure 4K shows GSEA of cluster FB-4 marker genes. Figure 4L shows differential strength interaction analysis using CellChat cell–cell communication analysis. [0059] Figures 5A-M show spatial transcriptomics analysis that highlights vascular remodeling and ischemia. Figure 5A shows spatial transcriptomic slide, mapped with spots corresponding to arterial signal. Clustering analysis was performed on the spatial transcriptomic data, based solely on expression information. Spatial clusters expressing endothelial and vascular markers (clusters 5, 7, 8 and 9) are indicated in the left ventricle of D1 (left) and the right ventricle of D2 (right). Each slide has two technical replicates (A and B). D1-LV-A, D1, left ventricle, replicate A; D2-RV-A, D2, right ventricle, replicate A. Scale bar, 1 mm. Figure 5B shows a close up of the histology of the left ventricle of D1 (corresponding to the area bounded by the black square in Figure 5A, left). Scale bar, 200 µm. Figure 5C shows a close up of the histology of the right ventricle of D2 (corresponding to the area bounded by the black square in Figure 5A, right). Scale bar, 200 µm. Figures 5D-E show spatial transcriptomics data after deconvolution, based on the snRNA-seq signal for SMCs (Figure 5D) and FBs (Figure 5E). Scale bar, 1 mm. Figure 5F shows expression of selected marker genes of cluster 7, across all spatial clusters. Figure 5G shows expression of selected marker genes of cluster 7, across all spatial transcriptomics samples. Figure 5H shows cell–cell interaction analysis of the IL-1 signaling network across cell types (based on snRNA-seq data), in D1-LV. Figure 5I shows analysis of IL33 signaling between cell types in D1-LV (based on snRNA-seq) data. Figure 5J shows H&E staining of pig xenograft endomyocardial biopsies from D1 showing ‘lifting’ of endothelium (white arrow), with few inflammatory cells present (black and grey arrow). Scale bar, 1 mm. Figure 5K shows EM micrograph from a biopsy of the xenograft from D1, showing endothelial cell swelling (white arrow) and sarcolemma disruption (grey arrow). Scale bar, 5 µm. Figure 5L shows DE of hypoxia- and damage-associated genes based on snRNA-seq (pseudobulk) data in FBs, CMs and VECs. Figure 5M shows expression of the genes shown in Figure 5L in SMCs, across conditions. For dot-plots, dot size reflects the percentage of nuclei in each group with gene expression above zero. Dot saturation displays each group’s mean gene expression, min–max scaled. [0060] Figures 6A-6J show PCXD and vascular remodeling in the xenograft. Figure 6A shows DE of PCXD-related genes in snRNA-seq data for VECs, CMs, FBs and MPs. Dot size 21 314164997v1 Attorney Docket No: 243735.000437 reflects the −log10(FDR) from pseudobulk DESeq results and the saturation reflects log2FC. Significance (Padj < 0.05) is denoted by a black star. The logFC and adjusted P value (Wald test P value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output. Genes labeled as PCXDplus were previously described (Byrne, et al., 2011) as being overexpressed in PCXD, whereas those labeled as PCXDminus were underexpressed. Figure 6B shows top enrichments in D1 for VECs from the pseudobulk-based DEA. Figures 6C-F show subclustering analysis of VECs (all samples integrated). Figure 6C shows low-dimensional embedding representation of VECs, sorted by their subclusters. Figure 6D shows vascular subtype markers. Figure 6E shows proportion distribution of subclusters VEC-4 and VEC-9 across samples. Figure 6F shows GSEA of cluster VEC-4 marker genes. Figures 6G-J show subclustering analysis of PERs and SMCs (all samples integrated). Figure 6G shows low-dimensional embedding representation of SMC–PER subclusters. Figure 6H shows cell cycle score distribution across clusters. Figure 6I shows proportion distribution of subclusters SMC–PER-4 and SMC–PER-6 across samples. Figure 6J shows GSEA of cluster SMC–PER-4 marker genes. [0061] Figures 7A-7L show detailed PBMC scRNA-seq data analysis. For dot-plots, dot size reflects the percentage of cells in each group with gene expression above zero. Dot saturation displays each group's mean gene expression, min-max scaled. Figure 7A shows low dimension embedding representation of the PBMC scRNA-seq data, sorted by cell types. HSC-CD34+: Hematopoietic stem cells. Figure 7B shows expression distribution of selected marker genes of the main cell types, basis for their identification. Figure 7C shows low dimension embedding representation of the T/NK cells, sorted by subtype. NK: Natural Killer cells, CD8 T: CD8+ T cells, CD4 T: CD4+ T cells, Treg: T-regulatory cells. Figure 7D shows expression distribution of selected marker genes of the subtype, basis for their identification. Figures 7E-F show further dissection of CD8 T (Figure 7E) and CD4 T (Figure 7F) cells. Figure 7G shows low dimension embedding representation of the B cells, sorted by subtype. Figure 7H shows expression distribution of selected marker genes of the subtype, basis for their identification. Figure 7I shows proportion differences across time points, for both decedents, in selected cell types and subtypes (labeled on each plot). Percentages are calculated based on the total number of cells (all cell types) in each sample. Figures 7J-K show genes selected as highly specific for the B cell (Figure 7J) and T/NK (Figure 7K) population, used for bulk RNA-seq proportion validation in 22 314164997v1 Attorney Docket No: 243735.000437 Figure 1. Figure 7L shows a summary of DEG analysis from PBMC scRNA-seq. Number of overexpressed genes (left, LFC > 0 and padj < 0.05) and underexpressed genes (right, LFC < 0 and padj < 0.05). The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output. [0062] Figures 8A-D show multi-omics DE integration. Comparative temporal DEA of individual omics analyses. Bulk transcriptomics, proteomics, lipidomics and metabolomics for blood samples of two pig heart to human xenotransplantations spanning 26 time points are illustrated. The early phase encompasses the 6 hr and 12 hr post-transplant time points, the mid- phase includes 18 hr to 36 hr post-transplant, while the late phase comprises all time points beyond 36 hr. For each panel, the x-axis represents the fold change in log2 scale, and the y-axis depicts the -log10 transformed unadjusted p value. These analyses were run individually for decedent 1 (top) and decedent 2 (bottom). The individual omics are as follows: Figure 8A shows transcriptomics. DEA analysis using DESeq2 is limited to genes whose average expression exceeds 4 counts across samples. Genes with significant expression changes (FDR<0.05) are shown in dark grey. Only the top 10 most significant genes were labeled due to space limitation. The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini– Hochberg method) were obtained directly from the DESeq2 DEA output. Figure 8B shows proteomics. Proteins were present in more than half of the samples, yielding a total of 895 proteins. Proteins with significant changes (FDR < 0.05) are shown in dark grey. Figure 8C shows metabolomics. DEA was performed for 459 metabolites. Metabolites with significant changes (FDR < 0.05) are shown in dark grey. Figure 8D shows lipidomics. The analysis included 720 lipids. Lipids showing significant changes (FDR < 0.05) are shown in dark grey. In the metabolomics and lipidomics panels, only the top features are highlighted with the molecule names. In all panels, molecules without significant changes are represented in light gray. Corrected p-values (FDR, from two-tailed moderated t-tests, adjusted using the Benjamini– Hochberg method) and log fold changes (LFC) were derived from limma outputs. [0063] Figures 9A-B show intersection between clinical measurements and multi-omics integration. Figure 9A shows overlap of clinical blood markers (Lactate, Troponin, Fibrinogen, Ferritin, SBP, BNP, DBP, IL-2R) and multi-omics integration-based cluster of analytes upregulated in D1. The blood marker level is shown in bold line. For each marker, decedent 1 measures are shown on the top graph, decedent 2 on the bottom. Figure 9B shows cardiac 23 314164997v1 Attorney Docket No: 243735.000437 output, cardiac index measurements and vasopressor dose (bottom graphs) put in perspective with the multi-omics integration-based cluster of analytes upregulated in D1 (top graph). Decedent 1 is shown on the left and decedent 2 on the right. [0064] Figures 10A-I show xenograft snRNA-seq based gene expression dissection. Figures 10A-B show top marker genes (Wilcoxon rank-sum) for human nuclei (Figure 10A) and pig nuclei (Figure 10B) in the xenograft snRNA-seq data. Dot size reflects the percentage of nuclei in each group with gene expression above zero. Dot saturation displays each group's mean gene expression, min-max scaled. Figure 10C shows cell-type proportions for pig nuclei. Figure 10D shows cell-type proportion of human nuclei. Figure 10E shows human T / NK subtypes markers expression within xenograft T/NK human population, across samples. Figure 10F shows NK/T cell activity markers expression across samples. Figures 10G-I show expression distribution of lead genes in D1 which contributed to the Reactome Interleukin Signaling enrichment in CM (Figure 10G), NIK to Non-canonical NF-kB signaling in CM (Figure 10H), and Bioplanet 2019 DNA replication in FB (Figure 10I). Dot size reflects the -log10(FDR) from DESeq result, saturation reflects log2FC. Significance (padj < 0.05) is denoted by a black star. The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output. [0065] Figures 11A-H show spatial transcriptomics composition. Figure 11A shows spatial transcriptomic slide mapped with each spatial cluster, in all samples. Scale bar: 1mm. Figures 11B-C show spatial transcriptomics cell type deconvolution (Cell2location) signal, min–max scaled, across Spatial clusters (Figure 11B), Visium samples (Figure 11C), and their combination (Figure 11D). Figure 11E shows cluster proportion distribution across samples. Figure 11F shows spatial distribution of IL1RL1 expression, represented by the grey scale (log norm expression). Scale bar: 1mm. Figure 11G shows spatial distribution of IL33 expression, represented by the grey scale (log norm expression). Scale bar: 1mm. Figure 11H shows snRNA-seq based cell–cell communication inference results for IL33 signaling. [0066] Figures 12A-J show xenograft tissue imaging. Figures 12A-D show H&E staining of Pig Xenograft Endomyocardial Biopsies from decedent 1 (Figures 12A-B) and decedent 2 (Figures 12C-D). Scale bar: 1mm. Figure 12A shows contraction band necrosis, with interstitial edema and endothelial swelling of small interstitial capillary. Figure 12B shows endothelial swelling of intramyocardial muscular artery and detached endothelial cells with perivascular 24 314164997v1 Attorney Docket No: 243735.000437 edema. Figure 12C shows endothelial swelling in the intramyocardial muscular artery. Figure 12D shows endothelial swelling and lifting of small interstitial capillary. Figures 12E-K show electron microscopy (EM) images of the xenograft samples. Figure 12E shows electron microscopy image of D2 transplanted xenograft. Scale bar: 5 µm. Figure 12F shows EM image of D1 transplanted xenograft. Ultrastructural changes in the capillary endothelial cells are characterized by endothelial swelling with narrowing of the capillary lumen and enlarged vacuoles in the cytoplasm. There is perivascular edema resulting in separation of the collagen fibrils. The myocytes have an intact sarcolemma; however, the cytoplasm shows decrease to loss of myofibrils under the sarcolemma. Scale bar: 5 µm. Figure 12G shows D1: accumulation of mitochondria (grey arrow) as a result of degenerated myofibrils, widened disorganized Z-lines (black arrow) in degenerating myofibrils, and swollen interstitial capillary endothelial cell (white arrow). Scale bar: 10 µm. Figure 12H shows D1: degenerating myofibrils (black arrow), swollen endothelial cells in intramyocardial capillary (grey arrow), and sarcolemma disruption (white arrow). Scale bar: 5 µm. Figure 12I shows D1: endothelial cell swelling with near collapse in an intramyocardial capillary lumen. Scale bar: 5 µm. Figure 12J shows D2: endothelial swelling (white arrow). Scale bar: 5 µm. [0067] Figures 13A-J show graft PCXD and hypoxia signal. Figure 13A shows expression of genes of interest and hypoxia related genes across Visium samples (within spots from clusters 7 and 8). Figure 13B shows top enrichments for Visium cluster 7 markers (from GSEA on Wilcoxon rank-sum based marker discovery). Saturation depicts the NES, indicating negative and positive scores. When the false discovery rate (FDR) is below 0.05, numbers are displayed, denoting the negative logarithm of the FDR. Figure 13C shows top enrichments for D1-LV-A sample, from Visium data (from GSEA on Wilcoxon rank-sum based DE, at sample level, each sample vs. the others together). Figures 13D-E show hypoxia and damage-associated genes expression changes in snRNA-seq. With DEA (pseudobulk) signals in dividing cells, LEC, L2, MP and SMC and VEC (Figure 13D), and across cell types (Figure 13E). Figures 13F-J show expression of genes associated with PCXD in the spatial transcriptomics and snRNA-seq data. Dot size reflects the percentage of spots in each group with gene expression above zero. Dot saturation displays each group’s mean gene expression, min–max scaled. Genes labeled as PCXDplus were previously described (Byrne, et al., 2011) as being overexpressed in PCXD, while PCXDminus as underexpressed. Figure 13F shows expression distribution across spatial 25 314164997v1 Attorney Docket No: 243735.000437 clusters. Figure 13G shows expression distribution within all spots, between samples. Figure 13H shows expression distribution within vascular spatial clusters (only spots of cluster 8 and 7 are included), between samples. Figure 13I shows expression distribution across cell types (snRNA-seq). Figure 13J shows expression distribution across conditions (snRNA-seq). [0068] Figures 14A-C show further dissection of vascular remodeling in xenografts. Figure 14A shows top enrichments in D1 for PER from pseudobulk based DE analysis. Figure 14B shows top enrichments for SMC in D1 left ventricle. Here, GSEA was performed from Wilcoxon rank-sum test DE analysis at the nuclei level (each condition vs. non-ischemic in vivo pig cardiac tissue, within snRNA-seq, selecting for SMC). Saturation depicts the NES, indicating negative and positive scores. A white star denotes a false discovery rate (FDR) is below 0.05, while dot size denotes the negative logarithm of the FDR. Figure 14C shows expression distribution of lead genes in D1 which contributed to the MSigDB Hallmark 2020 EMT enrichment (top), MSigDB Myc Targets V1 enrichment (middle) and MSigDB TNF-alpha Signaling via NF-kB (bottom), all in VEC. Dot size reflects the -log10(FDR) from DESEQ result, saturation reflects log2FC. Significance (padj < 0.05) is denoted by a black star. The LFC and adjusted p value (Wald test p value, two-tailed, adjusted with the Benjamini–Hochberg method) were obtained directly from the DESeq2 DEA output. [0069] Figures 15A-G show lymphatic endothelial cells (LECs) dissection. Figure 15A shows top enrichments in D1 for LECs from pseudo-bulk based DE analysis. Figures 15B-G show LEC sub-clustering analysis. Figure 15B shows UMAP of LECs sorted by spatial subclusters, and dotplot of cell cycle score. Figure 15C shows correlation of subcluster expression signal. Figure 15D shows cell cycle score distribution. Figure 15E shows proportion distribution of subclusters LEC-3 and LEC-6 across samples. Figure 15F shows gene set enrichment analysis of subcluster LEC-3 marker genes. Figure 15G shows GSEA analysis of LEC-3 DEA results. [0070] Figures 16A-I show dividing cells dissection. Figure 16A shows subtyping and re- embedding of the dividing cells population, low dimensional embedding representation of the dividing cells sorted by their associated cell types. Cells belonging to the dividing group in Figure 3A were isolated and re-clustered. Marker gene expression was used to label them. Figure 16B shows top marker based GSEA results in DIV-PER and Figure 16C shows top marker based GSEA results in DIV-VEC2. Saturation depicts the NES, indicating negative and positive scores. A white star denotes a false discovery rate (FDR) is below 0.05, while dot size 26 314164997v1 Attorney Docket No: 243735.000437 denotes the negative logarithm of the FDR Figure 16D shows proportion distribution of dividing cells subtypes between samples and conditions. Percentages are calculated based on the total number of cells, from all cell types. Figure 16E shows top marker genes of the DIV-VEC2 cells population. Figures 16F-I show expression of ICAM1, P-selectin (SELP), E-selectin (SELE), across all cell types (Figure 16F) Pseudobulk level DE results in VEC population (Figure 16G), dividing cells subtypes (Figure 16H), across conditions in DIV-VEC1 and DIV-VEC2 (Figure 16I). [0071] Figures 17A-C show quality control of decedent PBMC scRNA-seq. Figure 17A shows distribution of the main CellRanger sample-level QC metrics, across all PBMC scRNA- seq samples. Each sample makes a data point (black dots). The quartiles of that distribution are represented within the box (median as the center bar), while the distribution's remaining data points are indicated by the whiskers, extending from the box. Points considered "outliers" are those beyond 1.5 times the interquartile range (IQR) and are not included within the whiskers' span. N = 26 scRNA-seq samples (14 for D1, 12 for D2). Figures 17B-C show distribution of cell level QC metrics before (Figure 17B) and after (Figure 17C) filtering. [0072] Figures 18A-B show additional flow cytometry data. Figure 18A shows flow cytometry gating scheme for one representative sample. Light grey boxes indicate analyzed populations, and dark grey boxes indicate subsets that were further subdivided. Figure 18B shows monocytes identified as CD56-CD3-CD19-HLA-DR+ cells and subdivided into classical (CD14+CD16-), non-intermediate (CD14+CD16+), and non-classical subsets (CD14-CD16+). [0073] Figures 19A-B show pig and human nuclei categorization. Number of unique molecular indices (UMI) counts mapping to the human (x-axis) or pig (y-axis) genome from the xenograft snRNA-seq, resulting from the CellRanger ‘Barnyard’ experiment multiple genome aligner. Nuclei are then assigned a genome based on that distribution, and cross-species multiplets are called. Figure 19A shows linear scale. Figure 19B shows logarithmic scale. This partition was used to separate pig and human nuclei in the Xenograft snRNA-seq, for downstream analysis. [0074] Figures 20A-F show quality control of heart xenograft sn-RNAseq data. Figure 20A shows distribution of the main CellRanger sample-level QC metrics, across all heart xenograft sn-RNAseq samples (pig genome alignment). Each sample makes a data point (black dots). The quartiles of their distribution are represented within the box (median as the center bar), while the 27 314164997v1 Attorney Docket No: 243735.000437 distribution's remaining data points are indicated by the whiskers, extending from the box. Points considered "outliers" are those beyond 1.5 times the interquartile range (IQR) and are not included within the whiskers' span. N = 4 snRNA-seq samples (D1-LV, D1-RV, D2-LV, D2- RV). Figure 20B shows cell level QC metrics before and after filtering for pig (top) and human (bottom) nuclei. Figures 20C-D show human transgene expression levels in the pig heart snRNA-seq data. Expression is shown among the heart xenograft samples, and, for negative control, the integrated public pig dataset (Andrijevic, et al., 2022) (nuclei separated by condition), non-normalized (Figure 20C), average expression per cell, min-max scaled (Figure 20D). Figures 20E-F show human transgene expression levels in the xenotransplant Visium Spatial Transcriptomics data, spots grouped by Spatial Clusters (Figure 20E), and by sample (Figure 20F). [0075] Figures 21A-F show top enrichments from cell type marker gene based GSEA for pig nuclei. Saturation depicts the normalized enrichment score (NES), indicating negative and positive scores. When the false discovery rate (FDR) is below 0.05, a white star is plotted, while dot size denotes the negative logarithm of the FDR. Figure 21A shows CM, Figure 21B shows SMC, Figure 21C shows VEC, Figure 21D shows LEC, Figure 21E shows MP, and Figure 21F shows FB. [0076] Figures 22A-22G show differential expression analysis of heart xenograft pig nuclei. Figures 22A-F show top enriched pathways (from GSEA analysis of DE results), in CM (Figures 22A-C) and in FB (Figures 22D-F), for Ecmo, Ischemia 7 hr and decedent 2 conditions. Saturation depicts the normalized enrichment score (NES), indicating negative and positive scores. When the false discovery rate (FDR) is below 0.05, a white star is plotted, while dot size denotes the negative logarithm of the FDR. Figure 22G shows expression distribution of lead genes in decedent 1 which contributed to specific pathways in GSEA. Dot size reflects the - log10FDR from DESEQ result, saturation reflects log2FC. Significance (padj < 0.05) is denoted by a black star. The LFC and adjusted p-value (Wald test p-value, twotailed, adjusted with the Benjamini Hochberg method) were obtained directly from the DESeq2 DEA output. [0077] Figures 23A-23F shows fibroblast sub-clustering analysis. Figure 23A shows top 5 marker genes (Wilcoxon rank sum test) for FB subclusters. Figure 23B shows top 50 marker genes (Wilcoxon rank sum test) for subcluster FB4. Figure 23C shows top 50 marker genes (Wilcoxon rank sum test) for subcluster FB2. Figure 23D shows correlation of subcluster 28 314164997v1 Attorney Docket No: 243735.000437 expression signal. Figure 23E shows subclusters proportion distribution Figure 23F shows gene set enrichment analysis of subcluster FB2 marker genes. [0078] Figures 24A-G show cardiomyocytes sub-clustering analysis. Figure 24A shows UMAP of CMs sorted by subclusters. Figure 24B shows correlation of subcluster expression signal. Figure 24C shows gene set enrichment analysis of subcluster CM4 marker genes. Figure 24D shows top 5 marker genes (Wilcoxon rank sum test) for CM subclusters. Figure 24E shows top 50 marker genes (Wilcoxon rank sum test) for subcluster CM2. Figure 24F shows top 50 marker genes (Wilcoxon rank sum test) for subcluster CM4. Figure 24G shows subclusters proportion distribution. [0079] Figures 25A-B show cell-cell communication analysis from heart xenograft. Number of interactions (Figure 25A) and strength of interactions (Figure 25B) are shown between cell types, across conditions, predicted by CellChat. [0080] Figures 26A-C show spatial transcriptomics deconvolution with cell2location. For each cell-type, the deconvolution posterior distribution is plotted on the spatial transcriptomics slide. Scale bar: 1 mm. Figure 26A shows D1-LV-B, Figure 26B shows D1-RV-B, and Figure 26C shows D2-RV-B. [0081] Figures 27A-C show spatial transcriptomics deconvolution with DestVI. For each cell type, the DestVI-predicted cell-type proportions are plotted on the spatial transcriptomics slide. Scale bar: 1 mm. Figure 27A shows D1-LV-B, Figure 27B shows D1-RV-B, and Figure 27C shows D2-RV-B. [0082] Figures 28A-C show spatial transcriptomics deconvolution signal. Spatial transcriptomics cell type deconvolution (Cell2location) signal, min max-scaled, is shown across Spatial clusters (Figure 28A), Visium samples (Figure 28B), and their combination (Figure 28C). The signal is shown for Cell2location (unscaled), and DestVI (unscaled, left; min-max normalized, right). [0083] Figures 29A-D show marker genes for spatial transcriptomics clusters of interest. Top 60 marker genes (Wilcoxon rank sum test) for vascular Spatial transcriptomics clusters are shown. Dot size reflects the percentage of spots in each group with gene expression above zero. Dot saturation displays each group's mean gene expression, min-max scaled. Figure 29A shows Cluster 7. Figure 29B shows Cluster 8. Figure 29C shows Cluster 9. Figure 29D shows Cluster 10. 29 314164997v1 Attorney Docket No: 243735.000437 [0084] Figures 30A-H show vascular endothelial cells sub-clustering analysis. Figure 30A shows top 5 marker genes (Wilcoxon rank sum test) for VEC subclusters. Figures 30B-D show top 50 marker genes (Wilcoxon rank sum test) for subclusters VEC5 (Figure 30B), VEC4 (Figure 30C), and VEC9 (Figure 30D). Figures 30E-F show gene set enrichment analysis of subclusters VEC5 (Figure 30E) and VEC9 (Figure 30F) marker genes. Figure 30G shows correlation of subcluster expression signal. Figure 30H shows subclusters proportion distribution. [0085] Figures 31A-G show pericytes and smooth muscle cells sub-clustering analysis. Figure 31A shows top 5 marker genes (Wilcoxon rank sum test) for PER-SMC subclusters. Figure 31B shows top 50 marker genes (Wilcoxon rank sum test) for subcluster PER-SMC4. Figure 31C shows top 50 marker genes (Wilcoxon rank sum test) for subcluster PER-SMC1. Figure 31D shows correlation of subcluster expression signal. Figure 31E shows expression of SMC and PER markers across subclusters. Figure 31F shows gene set enrichment analysis of subcluster PER-SMC1 marker genes. Figure 31G shows subclusters proportion distribution. [0086] Figures 32A-D show lymphatic endothelial cells sub-clustering analysis. Figure 32A shows top 5 marker genes (Wilcoxon rank sum test) for LEC subclusters. Figure 32B shows top 50 marker genes (Wilcoxon rank sum test) for subcluster LEC2. Figure 32C shows gene set enrichment analysis of subcluster LEC2 marker genes. Figure 32D shows subclusters proportion distribution. [0087] Figures 33A-D show single-cell RNA-seq analyses of pig-to-human kidney xenotransplantation. Figure 33A shows a schematic overview of the transcriptomic analyses of pig-to-human kidney xenotransplantation. Parts of the figure were created with BioRender. Figure 33B shows unsupervised clustering of the merged single cell transcriptomes across all samples (left) and visualization by cell types (right). UMAP, Uniform Manifold Approximation and Projection (McInnes, et al., 2018). Figure 33C shows UMAP visualization of single cell distribution from each kidney sample. Figure 33D shows a dot plot of marker genes indicating cell-type identity across all cell populations. The saturation intensity and dot size represent average expression level and the percentage of expressed cells, respectively. Endo, endothelial cells; IC_NS, nonspecific intercalated cells; IC_TypeB, Type B intercalated cells, IC_TypeA, Type A intercalated cells; DTC, distal tubule cells; TAL_1, thick ascending limb population 1; 30 314164997v1 Attorney Docket No: 243735.000437 TAL_2, thick ascending limb population 2; PT, proximal tubule cells; PT_VIM+, vimentin- positive proximal tubule cells; PT_Prolif, proliferating proximal tubule cells. [0088] Figures 34A-F show that human NK cells and macrophages infiltrate into porcine kidney xenograft. Figure 34A shows UMAP visualization of human (dark grey) and porcine (light grey) cell distribution. The immune cell cluster is enlarged for detail. Figure 34B shows UMAP visualization of sub-clustered human and porcine immune cell types. Figure 34C shows a heatmap of human-to-porcine raw reads counting ratios (H/P ratio) of macrophage and NK cell marker genes. Figure 34D shows violin plots of interferon-gamma signaling gene expressions in human and porcine immune cell populations in single-cell transcriptome data. Figures 34E-F show xenotransplantation time-resolved gene expression levels (Figure 34E) and human/porcine transcript (H/P) ratio (Figure 34F) of interferon-gamma signaling genes in the longitudinal bulk RNA-seq of xenograft biopsies. Statistical significance is indicated by asterisks above to the violin plots: * for p < 0.05, ** for p < 0.01, *** for p < 0.001, and **** for p < 0.0001. [0089] Figures 35A-D show identification of rejection signals in porcine endothelial cells and in immune cells. Figures 35A-B show relative gene expression levels of AbMR (Figure 35A) and TCMR (Figure 35B) marker genes over the period of the xenotransplantation. Genes with dramatic expression changes (>2 folds) at the last time-point were shown in a bigger font size relative to other genes. Figures 35C-D show violin plots of AbMR marker gene expression in porcine endothelial cells (Figure 35C) and TCMR marker gene expression (Figure 35D) in immune cells. Gene expression levels were detected in single-cell RNA-seq data. Statistical significance is indicated by asterisks above to the violin plots: * for p < 0.05, ** for p < 0.01, *** for p < 0.001, and **** for p < 0.0001. [0090] Figures 36A-J show xenotransplantation-associated porcine kidney damage and the activation of a cell proliferation program. Figure 36A shows volcano plots of differentially expressed genes between xenograft and control for the first (left) and second (right) cases of xenotransplantation. Representative genes (biomarkers of kidney-injury and proliferation) were labeled. A few data dots in the plot reached a ceiling value on the y-axis, indicating system’s default lowest p-value. Figure 36B shows violin plots of kidney-injury biomarker expression levels across major cell types in the nephron. Figure 36C shows a UMAP highlight of the proliferating cell population. Figures 36D-E show UMAP visualization of the sub-clustered proliferating cell cluster signified by organismal origin (Figure 36D) and cell cycle phase 31 314164997v1 Attorney Docket No: 243735.000437 assignment (Figure 36E). Figure 36F shows ridge plots of top marker genes in cells across G1, S, and G2 phases. Figures 36G-I show gene expression levels of proliferation marker (STMN1, Figure 36G), proximal tubule cell marker (SLC34A1, Figure 36H), and T-cell marker (CD3E, Figure 36I) in the proliferating cell population. The smaller cluster of cells expressing T cell markers but not PTC markers (CD3E+; SLC34A1–) is shown. Figure 36J shows time-resolved gene expression levels of kidney tissue injury marker genes (labeled damage-related) and cell cycle genes (labeled proliferation-related) in longitudinal RNA-seq. [0091] Figures 37A-E show recipient’s PBMCs showing two waves of immune response expression signatures. Figure 37A shows Uniform Manifold Approximation and Projection (UMAP) visualization of the second kidney xenograft recipient’s PBMC clusters. mono-CD14, Monocytes CD14; mono-CD16, CD16-positive monocytes; M1, macrophage 1; M2, Macrophage 2; NK, natural killer cells; NKT1, natural killer T cells 1; NKT2, natural killer T cells 2; T-CD8, CD8 T cells; T-CD4, CD4 T cells; T-Reg, T Regulatory cells, N.B, naïve B cells; M.B, memory B cells; P.B, plasma B cells; MGK-P, Megakaryocyte Progenitor cells; MGK, Megakaryocytes; RBC, Red blood cells (Erythrocytes). Figure 37B shows cell type proportions of PBMC populations across the six timepoints. Figure 37C shows temporally variable MHC class II genes expression pattern across the period of xenotransplantation in representative antigen presenting cell types. Figures 37D-E show relative gene expression levels of gene sets enriched at 12 hours pXTx (Figure 37D) and 48-53 hours pXTx (Figure 37E) across the period of xenotransplantation in representative cell types. [0092] Figures 38A-D show violin plots of quality control features for all cell types across each kidney single-cell RNA-seq dataset. Figure 38A shows xenograft 1, Figure 38B shows xenograft 2, Figure 38C shows control kidney 1, and Figure 38D shows control kidney 2 data. Each panel presents the total number of expressed genes (nFeature_RNA, top) and total number of transcripts (nCount_RNA, bottom) of each cell. [0093] Figures 39A-F show feature plots of marker genes for identifying cell types. Figures 39A-D show UMAP visualization of marker genes used for identifying cell types in kidney single-cell RNA sequencing datasets. The genes correspond to the marker genes in Figure 33D. Figures 39E-F show podocyte marker gene expression levels presented as violin plot (left) across all cell clusters and as scatter plot (right) in the VIM-positive proximal tubule (PT-VIM+) 32 314164997v1 Attorney Docket No: 243735.000437 cells. Saturation intensity in the scatter plots indicate marker gene expression levels, with a zoom-in highlight of the podocyte cell group. [0094] Figures 40A-E show immune cell type characterization and gene function enrichment analyses of the two xenografts. Figures 40A-C show clustering and cell-type identification of immune cells. Feature plots for marker genes for macrophage (CD163, TYROBP) (Figure 40A), NK cell (NKG7, GNLY) (Figure 40B), and T cell (CD3E, CD3G) (Figure 40C) identifies human cells to be macrophages and NK cells, and porcine cells to be NK, NKT, and T cells, respectively. Figure 40D shows gene ontology enrichment analysis for Top 1000 Differentially Expressed Genes (DEG) ranked by average log2 fold change for xenograft 1 (top) and xenograft 2 (bottom). Figure 40E shows KEGG Enrichment Analysis for xenograft 1 (top) and xenograft 2 (bottom). [0095] Figures 41A-G show violin plots of PBMC single-cell RNA-seq data and the cell type marker genes. Figures 41A-F show PBMCs collected at 0h, 6h, 12h, 24h, 48h, and 53h post- xenotransplantation. Each panel presents the total number of expressed genes (nFeature_RNA, top) and total number of transcripts (nCount_RNA, bottom) of each cell. Figure 41G shows a violin plot visualization of marker genes used for cell type identification in PBMCs. The cell type annotations correspond to Figure 37A. [0096] Figures 42A-C show gene expression signatures in recipient’s PBMCs across the period of xenotransplantation. Figures 42A-B show gene ontology enrichment of enriched gene sets. Significantly enriched GO term corresponds to the gene sets of cell types at 12 hours pXTx (Figure 42A) and at 48-53 hours pXTx (Figure 42B) presented in Figure 37. Figure 42C shows a dot plot of interferon-gamma expression in PBMCs across the period of the study. NK cells and NKT cells were the main cell types of expression interferon-gamma. [0097] Figure 43 shows a graphical abstract of the studies performed herein. The figure was made with BioRender. [0098] Figure 44 shows a study overview. A schematic of the study is shown, indicating the major modalities (top) as well as the tissue and blood timepoints, assays utilized at each respective timepoint, treatments/medications utilized, and clinical microbiology testing performed (bottom) in the 61-day period following the pig-to-human Gal-KO thymokidney xenotransplant. The “CTRL” samples from the 5.1k ST panel data correspond to contralateral pig kidney (only pig cells) and native human kidney (only human cells), which act as positive 33 314164997v1 Attorney Docket No: 243735.000437 and negative controls for human cell selection in the analysis herein. BAL: bronchoalveolar lavage, EBV: Epstein-Barr virus, Gal-KO: α-1,3-galactosyltransferase (GGTA1) knockout, HBV: hepatitis B virus, rATG: rabbit anti-thymocyte globulin. Figure generated with BioRender. [0099] Figures 45A-M show that high-resolution spatial-transcriptional profiling reveals infiltrating human cells in porcine kidneys. Figure 45A shows images illustrating the analytical steps performed for spatial high-resolution tissue transcriptomic profiling (Xenium); examples are from POD49. Figure 45B shows separation of pig (dots above the dotted line) and human (dots below the dotted line) nuclei based on the number of identified transcripts originating from both genomes (sample POD33 is shown as an example). Figure 45C shows human / pig gene expression distribution among cells profiled with the 5.1k ST panel, separated by their assigned species. The x-axis represents the fraction of normalized expression attributed to human probes and the y-axis represents the percentage of cells in that group (pig or human). Figure 45D shows spatial transcriptomics cell-type labeling. Transplanted tissue H&E image (top panel), and corresponding spatial transcriptomics DAPI (4′,6-diamidino-2-phenylindole (DNA stain)) fluorescence image (bottom panel) with segmentation-based cells are notated by cell type. Figure 45E shows human cells infiltrating the renal cortex with their cell type annotation and associated markers (POD49). Figure 45F shows glomerulus and infiltrating human cells with cell-type annotation and associated markers (POD49). Figures 45G-L show dimension reduction maps of cells identified in the xenograft tissue, notated by identified cell type, for human (Figures 45G, I, K), and pig (Figures 45H, J, L) cells, in the 478 Xenium panel (Figures 45G- H), the 5.1k Xenium panel (Figures 45I-J), and snRNA-seq (Figures 45K-L) modalities. Figure 45M shows dimension-reduction map of cells identified in recipient PBMC scRNA-seq, notated by cell type. Left: main cell-types, right-up: T and NK cells, right-down: B cells, plasma cells, dendritic cells. Figures 45A, D, E, F show data from the 478 ST panel. MP: Macrophages, CD8T: CD8+ T cells, CD4T: CD4+ T cells, NK: NK cells, pDC: plasmacytoid dendritic cells, FB: Fibroblasts, EC: Endothelial cells, SMC: Smooth muscle cells, vSMC: vascular smooth muscle cells, fSMC: fibroblast-associated smooth muscle cells, VEC: vascular endothelial cells, cDC: classical dendritic cells, TEM: T effector memory cells, MK: Megakaryocytes, HSC: Hematopoietic stem cells, TCM: T central memory cells, MAIT: Mucosal-Associated Invariant T cells, Treg: Regulatory T cells. 34 314164997v1 Attorney Docket No: 243735.000437 [00100] Figures 46A-K show early host immune cell response involving macrophages, NK cells, pDCs, and plasma/B cells. Figure 46A shows percentage of human cells identified by spatial transcriptomics in the xenograft tissue (5.1k ST panel) across all identified cells. Figure 46B shows plasmablast levels measured in the recipient PBMC scRNA-seq (top), and B marker expression levels from PBMC bulk RNA-seq (bottom). Figure 46C shows human B cell levels measured by spatial transcriptomics in the xenograft tissue (5.1k ST panel, top), in the recipient PBMC scRNA-seq (middle), and marker expression levels identified by PBMC bulk RNA-seq (bottom). Figure 46D shows human NK cell levels measured by spatial transcriptomics in the xenograft tissue (5.1k ST panel, top), in recipient PBMC scRNA-seq (middle), and by PBMC bulk RNA-seq marker expression (bottom). Figure 46E shows flow cytometry levels of NK and B cells. Figure 46F shows human pDC levels measured by spatial transcriptomics in the xenograft tissue (5.1k ST panel, top), in recipient PBMC scRNA-seq (middle), and by PBMC bulk RNA-seq marker expression (bottom). Figure 46G shows quantification of unique BCR clones from bulk BCR-Seq divided by clonal expansion level. Figure 46H shows quantification of unique BCR clones from bulk BCR-Seq divided by BCR isotype family. Figure 46I shows percentage of human macrophages/monocytes (top), CXCL9⁺ macrophages (middle), and interferon⁺ immune cells (bottom) in the xenograft tissue (5.1k ST panel). Figure 46J shows gene set enrichment analysis (GO Biological process) of cell type markers for CXCL9⁺ macrophages (top) and interferon⁺ cells (bottom). Figure 46K shows expression of CXCL9, CXCL10, and CXCL11 from the infiltrating human immune cells in the xenograft (from 5.1k ST data). ST: spatial transcriptomics, NK: Natural Killer cells, pDC: plasmacytoid dendritic cells. [00101] Figures 47A-T show human T-cell response to xenotransplantation. Figures 47A-D show percentage of T cell subpopulations in the PBMC scRNA-seq data. This includes CD8 TEM (Figure 47A), Naive CD8 T (Figure 47B), CD4 TCM-TEM (Figure 47C) and Tregs (Figure 47D). Figures 47E-H show percentage of T cell subtypes observed in the 5.1k panel ST data, including CD8 TEM (Figure 47E), naive CD8T (Figure 47F), CD4T (Figure 47G) and Tregs (Figure 47H). Figure 47I shows percentage of all T cells from blood scRNA-seq data. Figures 47J-L show T-cell subtypes marker expression in blood bulk RNA-seq data, for CD8T markers CD8A and CD8B (Figure 47J), the CD4T marker TSHZ2 (Figure 47K) and Treg markers RTKN2 and CTLA4 (Figure 47L). Figures 47M-P show T-cell levels in flow cytometry data, for all T cells (Figure 47M). Figure 47Q shows T and NK cell subtypes expression of 35 314164997v1 Attorney Docket No: 243735.000437 ITGAE (CD103), CD38 and TNFRSF9 across data modalities. Samples that have less than 5 cells of the population in question are excluded from the graph. Figure 47R shows quantification of unique TCR clones from bulk TCR-seq divided by clonal expansion level. Figure 47S shows V+J gene expression levels of TCR Beta clones from bulk TCR-seq. Figure 47T shows tracking of the top TCR Beta clones in bulk TCR-seq postoperative time course. TEM: T effector memory cells, TCM: T central memory cells, Treg: Regulatory T cells, UMI: Unique molecular identifier, NK: Natural killer cells. Figure 47T discloses SEQ ID NO: 1. [00102] Figures 48A-H show that the transplanted porcine tissue response highlights the damage signaling at POD21 and POD33. Figure 48A shows expression of inflammatory related genes (FOS, FOSB, CXCL12) in the tissue xenograft porcine cells (from the 478 ST panel). Figure 48B shows the proportion of COLEC11+ cells across timepoints (5.1k panel). Figure 48C shows the proportion of SPP1+ cells across timepoints (478 panel). Figures 48D-E show expression of markers of interest for SPP1+ cells in the 478 panel (Figure 48D) and COLEC11+ cells in the 5.1k panel (Figure 48E). Figure 48F shows POD21 biopsy region corresponding to high levels of SPP1+ cells (478 panel). Figure 48G shows POD33 biopsy (H&E and ST) with high levels of human immune cells (478 panel). Figure 48H shows POD33 biopsy highlighting expression of pig and human genes of interest near human cells and COLEC11+ cells (5.1k panel). FB: Fibroblasts, EC: Endothelial cells, MP: Macrophages, NK: Natural Killer cells, pDC: Plasmacytoid Dendritic Cells, CD8T: CD8+ T cells, CD4T: CD4+ T cells, fSMC: fibroblast associated smooth muscle cells, vSMC: vascular smooth muscle cells, SMC: Smooth muscle cells, ST: spatial transcriptomics. [00103] Figures 49A-I show identification of pig and human cells from ST data. Figures 49A- C, G pertain to the 478 ST panel data and Figures 49D-F, H-I to the 5.1k ST panel data. Figure 49A shows the distribution of total human and pig probe counts for each identified cell (dots). Cells are notated by their assigned groups (pig, dots above the dotted line; human, dots below the dotted line). Figure 49B shows the distribution of the fraction of counts originating from human probes. Figure 49C shows the distribution of mean expression of human and pig genes, with cells represented as dots notated by their assigned group. Figure 49D shows joint dimension reduction plots from all samples integrated, notated by sample ID (top) and highlighting human kidney cells that were subsequently removed (they originate from the human native kidney sample). Figure 49E shows the resulting integration dimension reduction plots, notated by 36 314164997v1 Attorney Docket No: 243735.000437 fraction of normalized gene expression originating from human (top), by sample ID (left) and highlighting the selected human cluster (right). Figure 49F shows dimension reduction of the human cell clusters, highlighting human gene expression (top), pig gene expression (left), and basis for the identification of a doublet population (right), which is subsequently removed from the human cluster. Figure 49G shows the percentage of human cells found in the 478 ST panel across samples. Figure 49H shows the percentage of human cells found in the 5.1k ST panel across samples. Figure 49I shows the percentage of immune cells from the human native kidney sample sorted by their partition. [00104] Figures 50A-D show identification of pig and human cells from xenograft ST and snRNA-seq data. Figure 50A shows human / pig gene expression distribution among 5.1k ST panel transcriptomics data cells, separated by their assigned species. The metric is the fraction of normalized expression of human probes, against all other probes (x-axis). The y-axis shows the percentage of cells of that group (pig or human). Plots are shown for each biopsy timepoint. Figure 50B shows the distribution of the fraction of gene expression originating from human genes in the xenograft snRNA-seq data (log scale). Figure 50C shows joint dimension reduction plots from all snRNA-seq samples integrated, highlighting the fraction of gene expression originating from human genes (left), and the cluster subsequently selected as containing human cells (right). Figure 50D shows dimension reduction plot of the selected human cells, notated by their fraction of human gene expression. [00105] Figures 51A-M show further dissection of human macrophages, NK cells, B cells and dendritic cells. Figures 51A-B show dimension reduction graph (Figure 51A) and percentage across all cells by timepoint (Figure 51B) of human macrophage, monocytes and dendritic subtypes found in the 5.1k panel ST data notated by cell-type. Figures 51C-D show dimension reduction graph (Figure 51C) and percentage across all cells by timepoint (Figure 51D) for B cells, plasma cells, mast cells and neutrophils found in the 5.1k panel ST data, notated by their names. Figures 51E-G show marker gene expression of those populations in the 5.1k panel ST data. Figure 51H shows percentage levels for additional cell-type populations not already shown in this figure. Figures 51I-J show cell-type markers (Figure 51I) and associated percentage (Figure 51J) of B, plasma, and dendritic cell populations found in the PBMC scRNA-seq data. Figure 51K shows percentage of human macrophages, pDC and NK cells found in the 478 panel ST data. Figures 51L-M show pDC activation marker genes and CXCL9–CXCL11 expression 37 314164997v1 Attorney Docket No: 243735.000437 in pDC-cDC1 populations in 5.1k ST data (Figure 51L) and PBMC scRNA-seq data (Figure 51M). [00106] Figures 52A-L show early response from human macrophages and B cells as evidenced by ST and snRNA-seq in addition to BCR-seq. Figure 52A shows expression of immunoglobulin genes in PBMC bulk RNA-seq. Figure 52B shows CDR3 length (nt) distribution in bulk BCR-seq. Figure 52C shows somatic hypermutation frequency in bulk BCR- seq divided by isotype. Figure 52D shows shared overlapping clonotypes across bulk BCR-seq postoperative timepoints. Figure 52E shows tracking of the top BCR IgH clones in bulk BCR- seq postoperative time course. Figures 52F-I show 478 panel ST data highlighting human subpopulation of macrophages and NK cells expressing CXCL9, CXLC10, and CXCL11 as shown in a dimension reduction map (Figure 52F), distribution of these markers across those subpopulations (Figure 52G) and across time in the NK-MP populations (Figure 52H) and percentage distribution of these subpopulations across time (Figure 52I). Figures 52J-K show similar populations of interest in snRNA-seq data, as shown in expression of markers corresponding to the 5.1k panel data subtypes markers (Figure 52J) and their distribution over time (Figure 52K). Figure 52L shows expression of human CXCL9, CXCL10, CXCL11 observed in the tissue bulk RNA-seq. [00107] Figures 53A-K show further human T cell response dissection. Figures 53A-C show human T cell subtypes found in 5.1k ST data, as shown via dimension reduction plot (Figure 53A), percentage across all cells in each timepoint (Figure 53B), and marker genes defining these cells (Figure 53C). Figure 53D shows cell type markers of PBMC NK and T cells populations. Figure 53E shows proportion of proliferative human NK found in the 5.1k ST data. Figures 53F-G show proportions (Figure 53F) and marker gene expression (Figure 53G) of human CD8T and CD4T from the 478 panel ST data. Figure 53H shows distribution of T and NK subtypes found in the PBMC scRNA-seq data, as percentage of all cells across timepoints (for subtypes not shown in Figure 47). Figure 53I shows percentage of human dividing and T cells found in snRNA-seq data. Figure 53J shows expression levels of CD8T markers (CD8A, CD8B, top) and Tregs markers (RTKN2, CTLA4, bottom), from PBMC bulk RNA-seq. Figure 53K shows flow cytometry T cells distributions, CD8+ T cells (left) and CD4+ T cells (right), separated by central memory and effector memory cells. 38 314164997v1 Attorney Docket No: 243735.000437 [00108] Figures 54A-D show distribution of marker genes delineating resident and circulating T and NK cells. Figure 54A shows NK markers from 5.1k panel ST data. Figure 54B shows T cell markers from 5.1k panel ST data. Figure 54C shows NK markers from PBMC scRNA-seq data. Figure 54D shows T cell markers from PBMC scRNA-seq data. [00109] Figures 55A-J show porcine transcriptional response from tissue and porcine resident immune cells. Figure 55A shows inflammatory gene clusters identified by bulk RNAseq longitudinal analysis. Expression over time (top) and top pathway enrichment (bottom) are shown. Figure 55B shows gene-set enrichment of genes overexpressed in specific PODs in the 478 ST panel. Figure 55C shows colocalization analysis of SPP1+ cells (top) and pig immune cells (bottom) from the 478 panel ST data. Figure 55D shows colocalization analysis of COLEC11+ cells (top) and pig immune cells (bottom) from the 5.1k panel ST data. Figures 55E-F show macrophage and T-cell marker expression across cell-types in the 5.1k panel (Figure 55E) and 478 panel (Figure 55F) ST data. Figure 55G shows T-cell marker expression across timepoints, shown in pig immune cells in the 5.1k panel (left) and 478 panel (right) ST data. Figure 55H shows the top 80 marker genes for POD33 pig immune cells, identified by Wilcoxon rank-sum analysis comparing their transcriptomic profile to all other time points. Figures 55I-J show expression of marker genes of interest across timepoints in specific pig cell populations, in snRNA-seq samples of the xenograft (Figure 55I) and in 5.1k ST data (Figure 55J). [00110] Figures 56A-G show B-HOT AbMR signature revealed by ST. Figures 56A-D show B-HOT AbMR signature expression in the 478-gene ST panel, using genes identified as differentially expressed (DEG post- vs. pre-transplantation) in xenografts from Loupy, et al. (Loupy, et al., 2023). Figures 56E-G show B-HOT AbMR signature expression in the 5.1k-gene ST panel, using the entire B-HOT panel, regardless of overlap with Loupy, et al. (Loupy, et al., 2023) results. Figure 56A shows spatial distribution of the B-HOT AbMR signature in POD33 tissue. Figure 56B shows B-HOT signature enrichment in UMAP, overlaid with cell-type composition. Figure 56C shows AbMR signature enrichment across time points. Figure 56D shows marker expression of B-HOT AbMR genes. Figure 56E shows expression of genes (human probes) belonging to the B-HOT AbMR signature across timepoints in all human cells. Figure 56F shows expression of pig genes whose human orthologues are part of the B-HOT 39 314164997v1 Attorney Docket No: 243735.000437 panel, across timepoints in all pig cells. Figure 56G shows spatial distribution in POD33 of the AbMR gene set score, and pig SERPINE1 expression. [00111] Figure 57 shows dynamics of selected complement pathway elements protein levels in the serum. Serum abundance over time is shown for the indicated complement factors. Statistical analysis of protein regulation was based on XT2 Proteograph XT measurements of serum samples. The middle line, shaded region, and dashed line bands correspond to the median and 50% and 95% credible intervals of the protein intensity posterior distribution, respectively. [00112] Figures 58A-D show expression of pig and human probes from Xenium ST data. Figures 58A-C show expression from the 5.1k ST panel data and Figure 58D shows expression from the 478 panel ST data. Figures 58A-B show expression of human and pig probes for orthologous genes across samples in pig (Figure 58A) and human (Figure 58B) immune cells. Figure 58C shows expression of human and pig probes targeting orthologous genes, across cell- types in human immune cells (5.1k panel). In this panel, only probes targeting orthologues with a mean log-normalized expression of at least 0.15 in at least one cell type are shown. Figure 58D shows expression of all human and pig probes targeting orthologous genes, across all human and pig cell types. [00113] Figures 59A-E show dissection of human cells found in the xenograft 478 panel Xenium ST data. Figure 59A shows a dimension reduction map of the data notated by timepoint. Figure 59B shows a dimension reduction map of the data notated by cell-type. Figure 59C shows cell type proportion across timepoints. Figure 59D shows Wilcoxon-based differential expression results (top genes for each cell type). Figure 59E shows a dimension reduction map of the data for each timepoint. [00114] Figures 60A-E show dissection of pig cells found in the xenograft 478 panel Xenium ST data. Figure 60A shows a dimension reduction map of the data notated by timepoint. Figure 60B shows dimension reduction map of the data notated by cell-type. Figure 60C shows cell type percentage across timepoints. Figure 60D shows Wilcoxon based differential expression results (top genes for each cell type). Figure 60E shows dimension reduction map of the data for each timepoint. [00115] Figures 61A-G show dissection of human cells found in the xenograft 5.1k panel Xenium ST data. Figure 61A shows dimension reduction map of the data notated by timepoint. Figure 61B shows dimension reduction map of the data notated by cell-type. Figure 61C shows 40 314164997v1 Attorney Docket No: 243735.000437 cell type percentage across timepoints. Figures 61D-G show Wilcoxon based differential expression results (top genes for each cell type), for all cells (Figure 61D), monocytes/macrophages and dendritic cells (Figure 61E), NK and T cells (Figure 61F), and Mast, B and plasma cells (Figure 61G). [00116] Figure 62 shows dimension reduction map of the data for each timepoint for the 5.1k panel Xenium ST data. [00117] Figures 63A-E show dissection of pig cells found in the xenograft 5.1k panel Xenium ST data. Figure 63A shows dimension reduction map of the data notated by timepoint. Figure 63B shows dimension reduction map of the data notated by cell-type. Figure 63C shows cell type proportion across timepoints. Figure 63D shows Wilcoxon based differential expression results (top genes for each cell type). Figure 63E shows dimension reduction map of the data for each timepoint. [00118] Figures 64A-E show dissection of human cells found in the xenograft snRNA seq data (all human cells). Figure 64A shows dimension reduction map of the data notated by timepoint. Figure 64B shows dimension reduction map of the data notated by cell-type. Figure 64C shows cell type proportion across timepoints. Figure 64D shows Wilcoxon based differential expression results (top genes for each cell type). Figure 64E shows dimension reduction map of the data for each timepoint. [00119] Figures 65A-E show dissection of human cells found in the xenograft snRNA seq data (lymphocytes and NK cells). Figure 65A shows dimension reduction map of the data notated by timepoint. Figure 65B shows dimension reduction map of the data notated by cell-type. Figure 65C shows cell type proportion across timepoints. Figure 65D shows Wilcoxon based differential expression results (top genes for each cell type). Figure 65E shows dimension reduction map of the data for each timepoint. [00120] Figures 66A-E show dissection of pig cells found in the xenograft snRNA-seq data. Figure 66A shows dimension reduction map of the data notated by timepoint. Figure 66B shows dimension reduction map of the data notated by cell-type. Figure 66C shows cell type proportion across timepoints. Figure 66D shows Wilcoxon based differential expression results (top genes for each cell type). Figure 66E shows dimension reduction map of the data for each timepoint. 41 314164997v1 Attorney Docket No: 243735.000437 [00121] Figures 67A-E show dissection of main cell types found in the PBMC scRNA-seq data. Figure 67A shows dimension reduction map of the data notated by timepoint. Figure 67B shows dimension reduction map of the data notated by cell-type. Figure 67C shows cell type proportion across timepoints. Figure 67D shows Wilcoxon based differential expression results (top genes for each cell type). Figure 67E shows dimension reduction map of the data for each timepoint. [00122] Figures 68A-E show dissection of T and NK cells found in the PBMC scRNA-seq data. Figure 68A shows dimension reduction map of the data notated by timepoint. Figure 68B shows dimension reduction map of the data notated by cell-type. Figure 68C shows cell type proportion across timepoints. Figure 68D shows Wilcoxon based differential expression results (top genes for each cell type). Figure 68E shows dimension reduction map of the data for each timepoint. [00123] Figures 69A-E show dissection of B, plasma and dendritic cells found in the PBMC scRNA-seq data. Figure 69A shows dimension reduction map of the data notated by timepoint. Figure 69B shows dimension reduction map of the data notated by cell-type. Figure 69C shows cell type proportion across timepoints. Figure 69D shows Wilcoxon based differential expression results (top genes for each cell type). Figure 69E shows dimension reduction map of the data for each timepoint. [00124] Figures 70A-B show dissection of B-HOT annotation in the Xenograft ST data. Figures 70A-B show levels of scores derived from the B-HOT signature gene set shown across cell types (Figure 70A) and timepoints (Figure 70B), in the human cells of the 5.1k genes panel ST dataset. [00125] Figure 71 shows dynamics of human complement pathway elements protein levels in the serum. Statistical analysis of protein regulation was based on XT2 Proteograph XT measurements of serum samples. The middle line, shaded region, and dashed line bands correspond to the median and 50% and 95% credible intervals of the protein intensity posterior distribution, respectively. [00126] Figure 72 shows dynamics of porcine complement pathway elements protein levels in the serum. Statistical analysis of protein regulation was based on XT2 and XT1 Proteograph XT measurements of serum samples. The middle line, shaded region, and dashed line bands 42 314164997v1 Attorney Docket No: 243735.000437 correspond to the median and 50% and 95% credible intervals of the protein intensity posterior distribution, respectively. DETAILED DESCRIPTION [00127] Considerable progress has been made in pig genome editing to reduce the immunological barriers and incompatibilities between pigs and humans (Cooper, et al., 2012; Montgomery, Mehta, et al., 2022). Advances in research using recently deceased brain-dead human donors (decedents) and genetic knockout models of key xeno-antigens, including α-1,3- galactosyltransferase (α-1,3-Gal), has enabled the transition of xenotransplantation from preclinical primate experiments (Ekser, et al., 2009; Pintore, et al., 2013) to humans (Mohiuddin, et al., 2023; Moazami, et al., 2023). Galactose-α-1,3-galactose (α-Gal), a ubiquitous terminal carbohydrate modification on glycoproteins and glycolipids in the animal kingdom, is synthesized by α-1,3-Gal (GGTA1) (Galili, et al., 1993; Galili, et al., 1988). Unlike most species, humans and some non-human primates (NHPs) have a GGTA1 pseudogene, allowing the development of strong anti-α-Gal antibodies (Abs) that precipitate hyperacute rejection of α-Gal- expressing xenografts (Griesemer, et al., 2014). [00128] By knocking out α-1,3-Gal (α-Gal-KO), genetically modified porcine organs can substantially reduce hyperacute rejection risk in non-human primates (Phelps, et al., 2003; Yamada, et al., 2005; Cooper, et al., 2007; Pintore, et al., 2013; Ekser, et al., 2009). This generation of GGTA1 pig knockout (GT-KO) mutants to overcome the described major immunological barrier in xenotransplantation (Wolbrom, et al., 2023) led to Food and Drug Administration (FDA) approval of the use of GalSafe™ GT-KO pigs for human food consumption and potential downstream therapeutics in December 2020. While genetic differences in pigs compared to primates constitute a substantial reservoir of potential xeno- antigens, gene-edited pig organs have been successfully transplanted into NHPs with survival rates in excess of a year (Adams, et al., 2018; Butler, et al., 2016; Kim, et al., 2019; Yamamoto, et al., 2020). [00129] These positive results led the way to the first pig-to-human kidney xenotransplantation into brain-dead decedents to evaluate the safety and feasibility of the genetically engineered porcine kidney in humans (Montgomery, Stern, et al., 2022; Porrett, et al., 2022). Xenograft transplantation into brain-dead humans (decedents) offers a unique opportunity to evaluate 43 314164997v1 Attorney Docket No: 243735.000437 xenografts in the intended recipient model, and to obtain extensive longitudinally-collected blood and tissue samples in a ‘fail-safe’ manner (Montgomery, et al., 2024). In 2021, the successful GalSafe™ thymus-kidney (“thymokidney”) xenograft transplants were performed in two decedents (Montgomery, Stern, et al., 2022; Loupy, et al., 2023). The surgical placement of thymus tissues under the capsule of the kidney several months before procurement is designed to have a tolerogenic effect through deletion of xeno-reactive T-cells after transplant into human recipients (Sykes, et al., 2022). [00130] In parallel with developments in xenotransplantation procedures, recent technological advancements in in situ spatial transcriptomics (ST), single-cell (sc)- and single nuclei (sn)-RNA profiling, immune repertoire sequencing, and deep proteomic analyses have enhanced the ability to generate dense, longitudinal omics datasets that offer an unprecedented view into the biomolecular underpinnings of health and disease (Donovan, et al., 2023; Ferdosi, et al., Adv Mater, 2022; Ferdosi, et al., Proc Natl Acad Sci U S A, 2022; Huang, et al., 2023). Longitudinal multi-omics profiling has emerged as a powerful tool for uncovering new biomarkers in biological and disease contexts, including obesity and space flight (Zhou, et al., 2019; Garrett- Bakelman, et al., 2019; Piening, et al., 2018; Schüssler-Fiorenza Rose, et al., 2019; Ghaemi, et al., 2019; Shaked, et al., 2017; Piening, et al., 2022). [00131] The key advantage of longitudinal ‘omics’ comparisons within individual subjects lies in using individual time points as internal controls, greatly enhancing statistical power compared to cross-sectional studies (Piening, et al., 2018) and allowing for deep insights into the biological dynamics of processes including post-transplant complications (Piening, et al., 2022; Shaked, et al., 2017; Krupickova, et al., 2021). Recent advancements in single-cell and spatial transcriptomics now allow for detailed RNA expression analysis and understanding of cellular interactions, offering critical insights into complex biological processes (Long, et al., 2023; Ospina, et al., 2023; Finn, et al., 2019). There remains a need to better understand the molecular landscape following xenotransplantation to improve outcomes as xenotransplantation of genetically engineered porcine organs has the potential to address the challenge of organ donor shortage. [00132] Two pig heart xenografts were transplanted into two human decedents (D1 and D2) with the primary aim of assessing the presence of hyperacute antibody-mediated rejection (AbMR) and sustained xenograft functioning over a 3-day protocol (Moazami, et al., 2023). 44 314164997v1 Attorney Docket No: 243735.000437 Integrative multi-omics profiling was performed in human decedents receiving pig heart xenografts. In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. In attempts to elucidate the molecular underpinnings of xenotransplantation success, longitudinal multi-omics profiling was performed to assess the dynamic interactions in these first two pig heart xenografts transplanted into two human decedents. To better understand the detailed molecular landscape following xenotransplantation, lipidomic, metabolomic, and comprehensive proteomic datasets, in addition to transcriptomics (bulk RNA sequencing (RNA-seq) and single- cell RNA sequencing (scRNA-seq)), were generated across blood and single-nucleotide RNA sequencing (snRNA-seq) and spatial transcriptomic datasets in tissue samples collected throughout the 3-day xenotransplant protocols (Figure 1). Also, systems-level analysis was performed through integration of the respective omics datasets to assess global changes. [00133] Substantial early immune responses were observed in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. In contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes. [00134] Further, two cases of porcine-to-human kidney xenotransplantation were performed, yet the physiological effects on the xenografts and the recipients’ immune responses remained largely uncharacterized. The porcine kidney transplants showed promising physiological functioning during the ~3 day study period, producing urine and not showing evidence of hyperacute rejection (Montgomery, Stern, et al., 2022). Initial histological analyses of the kidney biopsy samples during and after xenotransplantation did not detect obvious evidence of antibody- mediated rejection (AbMR), nor strong indications of porcine kidney injury (Montgomery, Stern, 45 314164997v1 Attorney Docket No: 243735.000437 et al., 2022). However, due to the short duration of these human xenotransplantation trials, it is possible that the histological markers were not yet strongly expressed to a detectable level, rendering standard methods ineffective to capture signs of pathophysiological changes of the xenograft and the recipient’s immune response post xenotransplantation (Halloran, et al., 2018). [00135] Single-cell transcriptomic technology enables a comprehensive analysis of cellular physiology of organ transplantation and the recipient’s immune response (Varma, et al., 2021; Wu, et al., 2018; Malone, et al., 2020; Andrijevic, et al., 2022). In the study herein (Figure 43), comprehensive single-cell RNA sequencing (scRNA-seq) analyses were conducted on the first porcine-to-human kidney xenografts to characterize the intricate xenotransplantation-associated cellular and molecular dynamics and xenograft-recipient interactions. Additionally, longitudinal scRNA-seq of the peripheral blood mononuclear cells (PBMCs) was performed to detect recipient immune responses across time. [00136] Such comprehensive analyses enabled the detection of major changes in both human and porcine cells upon xenotransplantation. Although no hyperacute rejection signals were detected, evidence of endothelial cell and immune response activation found within the scRNA- seq analyses of the xenografts revealed early signs of AbMR. Tracing the species origin of cells, evidence was found for human immune cell infiltration in both xenografts. Human transcripts in the longitudinal bulk-RNA-seq revealed that human immune cell infiltration into the porcine kidney and the activation of interferon gamma-induced chemokines expression occurred by 12 hours and 48 hours post-xenotransplantation, respectively. Concordantly, longitudinal scRNA- seq of recipient PBMCs also revealed two phases of the recipients’ biphasic immune responses at 12 and 48-53 hours post-xenotransplantation. Lastly, global expression signatures of xenotransplantation-associated kidney tissue damage were observed in the xenografts. Surprisingly, a rapid increase of proliferative cells in both porcine kidney xenografts was detected upon xenotransplantation. These proliferative cells express proximal tubule cell marker genes, indicating a rapid activation of a porcine kidney tissue repair program. Longitudinal and single-cell RNA-seq analyses of porcine kidneys and recipient PBMCs revealed time-resolved cellular dynamics (e.g., dynamic xenograft tissue physiology and time-resolved immune responses) of xenograft-recipient interactions during xenotransplantation. These cues can be leveraged for designing gene edits of pigs for xenotransplantation and immunosuppression regimens to optimize xenotransplantation outcomes. 46 314164997v1 Attorney Docket No: 243735.000437 [00137] Additionally, immune responses were studied over 61 days following pig-to-human thymokidney xenotransplantation, integrating spatial transcriptomics, single-nucleus and single- cell RNA-sequencing, bulk RNA-sequencing, and proteomics. High-throughput molecular characterization studies have used transcriptomics panels, single-cell, and spatial technologies to investigate pig-to-human kidney and heart xenografts in decedents, but these efforts were limited to 2–3 days post-transplant (Loupy, et al., 2023; Pan, et al., 2024; Schmauch, et al., 2024; Cheung, et al., 2024). It was found herein that blood plasmablasts, natural killer (NK) cells, and dendritic cells increased between postoperative day (POD)10 and 28, concordant with expansion of immunoglobulin G (IgG)/immunoglobulin A (IgA) B-cell clonotypes, and subsequent biopsy- confirmed antibody-mediated rejection (AbMR) at POD33. Human T-cell frequencies increased from POD21 and peaked around POD45 in the blood and xenograft, coinciding with T-cell receptor diversification, expansion of a restricted TRBV2/J1 clonotype, and histological evidence of a cell-mediated component to the rejection. At POD33, the most abundant human immune population in the graft was CXCL9+ macrophages, aligning with IFN-γ-driven inflammation and Type I immune response. In addition, activated pig-resident macrophages colocalized with infiltrating human cells, suggesting cross-species interactions. Xenograft tissue showed pro-fibrotic tubular and interstitial injury, marked by S100A6, SPP1 (secreted phosphoprotein 1, Osteopontin), and COLEC11, on POD21-POD33. Proteomics revealed complement activation through porcine and human pathways, peaking at POD20 and declining after AbMR therapy including complement inhibition. Collectively, the molecular orchestration of human immune responses to a porcine kidney reveals potential immunomodulatory targets for improving xenograft survival. Definitions [00138] To facilitate an understanding of the principles and features of the various embodiments of the invention, various illustrative embodiments are explained below. Although exemplary embodiments of the invention are explained in detail, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the invention is limited in its scope to the details of construction and arrangement of components set forth in the following description or examples. The invention is capable of other embodiments and of being practiced or carried out in various ways. Also, in describing the exemplary embodiments, specific terminology will be resorted to for the sake of clarity. 47 314164997v1 Attorney Docket No: 243735.000437 [00139] Unless otherwise defined herein, scientific and technical terms used in connection with the present invention shall have the meanings that are commonly understood by those of ordinary skill in the art. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular. Generally, nomenclatures used in connection with, and techniques of, cell and tissue culture, molecular biology, immunology, microbiology, genetics and protein and nucleic acid chemistry and hybridization described herein are those well-known and commonly used in the art. [00140] The methods and techniques of the present invention are generally performed according to conventional methods well known in the art and as described in various general and more specific references that are cited and discussed throughout the present specification unless otherwise indicated. See, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 2d ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (1989); Ausubel et al., Current Protocols in Molecular Biology, Greene Publishing Associates (1992); and Harlow and Lane Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (1990), which are incorporated herein by reference. Enzymatic reactions and purification techniques are performed according to manufacturer’s specifications, as commonly accomplished in the art or as described herein. The implementation of the present invention can utilize, unless otherwise specified, standard techniques in immunology, biochemistry, genetics, computational biology, molecular biology, cell biology, genomics, epigenomics, and bioinformatics, which are well-known to those skilled in the field. [00141] The term “about” or “approximately” means within a statistically meaningful range of a value. Such a range can be within an order of magnitude, preferably within 50%, more preferably within 20%, still more preferably within 10%, and even more preferably within 5% of a given value or range. The allowable variation encompassed by the term “about” or “approximately” depends on the particular system under study, and can be readily appreciated by one of ordinary skill in the art. [00142] It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural references unless the context clearly dictates otherwise. For example, reference to a component is intended also to include composition of a plurality of components. References to a composition containing “a” constituent is intended to include other constituents in addition to the one named. In other words, the terms “a,” “an,” and 48 314164997v1 Attorney Docket No: 243735.000437 “the” do not denote a limitation of quantity, but rather denote the presence of “at least one” of the referenced item. [00143] The terms “treat” or “treatment” of a state, disorder or condition include: (1) preventing, delaying, or reducing the incidence and/or likelihood of the appearance of at least one clinical or sub-clinical symptom of the state, disorder or condition developing in a subject that may be afflicted with or predisposed to the state, disorder or condition but does not yet experience or display clinical or subclinical symptoms of the state, disorder or condition; or (2) inhibiting the state, disorder or condition, i.e., arresting, reducing or delaying the development of the disease or a relapse thereof (in case of maintenance treatment) or at least one clinical or sub- clinical symptom thereof; or (3) relieving the disease, i.e., causing regression of the state, disorder or condition or at least one of its clinical or sub-clinical symptoms. The benefit to a subject to be treated is either statistically significant or at least perceptible to the patient or to the physician. [00144] The terms “diagnose” or “diagnosis” of xenograft status or outcome include predicting or diagnosing the xenograft status or outcome, determining predisposition to a xenograft status or outcome, monitoring treatment of a xenograft subject, diagnosing a therapeutic response of a xenograft subject, and prognosis of xenograft status or outcome, xenograft progression, and response to treatment. [00145] The terms “subject”, “patient”, “individual”, “recipient”, and “animal” are used interchangeably herein and refer to mammals, including, without limitation, human and veterinary animals (e.g., cats, dogs, cows, horses, sheep, pigs, etc.) and experimental animal models. In a preferred embodiment, the subject is a human. [00146] The terms “sample”, “subject sample” and “test sample” are used herein to refer to any biological specimen obtained from a subject or patient. Samples that can be used in the methods of the present disclosure include, without limitation, serum, plasma, whole blood, red blood cells, white blood cells (e.g., peripheral blood mononuclear cells (PBMCs)), polymorphonuclear (PMN) cells, pericardial fluid, ductal lavage fluid, nipple aspirate, lymph (e.g., disseminated tumor cells of the lymph node), bone marrow aspirate, buccal swabs, saliva, urine, stool (i.e., feces), sputum, bronchial lavage fluid, tears, fine needle aspirate (e.g., harvested by random periareolar fine needle aspiration), any other bodily fluid, xenograft sample, a tissue sample such as a biopsy of a site of the xenograft (e.g., needle biopsy), and cellular extracts thereof. In some 49 314164997v1 Attorney Docket No: 243735.000437 embodiments, the sample comprises peripheral blood mononuclear cells (PBMCs) or a biopsy of a site of the xenograft. [00147] Also, in describing the exemplary embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents which operate in a similar manner to accomplish a similar purpose. [00148] It is also to be understood that the mention of one or more method steps does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Similarly, it is also to be understood that the mention of one or more components in a composition does not preclude the presence of additional components than those expressly identified. [00149] The terms “comprising” or “containing” or “including” are meant that at least the named element, or method step is present in article or method, but does not exclude the presence of other elements or method steps, even if the other such elements or method steps have the same function as what is named. [00150] The materials described hereinafter as making up the various elements of the present invention are intended to be illustrative and not restrictive. Many suitable materials that would perform the same or a similar function as the materials described herein are intended to be embraced within the scope of the invention. Such other materials not described herein can include, but are not limited to, materials that are developed after the time of the development of the invention, for example. Any dimensions listed in the various drawings are for illustrative purposes only and are not intended to be limiting. Other dimensions and proportions are contemplated and intended to be included within the scope of the invention. Methods [00151] In one aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, 50 314164997v1 Attorney Docket No: 243735.000437 IGHM, and IGHD, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00152] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, or nine genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, or nine genes are compared to a corresponding control. [00153] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs); and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00154] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, or 51 314164997v1 Attorney Docket No: 243735.000437 nine genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, or nine genes are compared to a corresponding control. [00155] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00156] In some embodiments, the expression levels are determined for three or more genes, four or more genes, or five genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY. In some embodiments, the expression levels of the three or more genes, four or more genes, or five genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, or five genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY. In some embodiments, the expression levels of the two, three, four, or five genes are compared to a corresponding control. [00157] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00158] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more 52 314164997v1 Attorney Docket No: 243735.000437 genes, or nine genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, or nine genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, or nine genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, or nine genes are compared to a corresponding control. [00159] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00160] In some embodiments, the expression levels are determined for three or more genes, four or more genes, or five genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1. In some embodiments, the expression levels of the three or more genes, four or more genes, or five genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, or five genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1. In some embodiments, the expression levels of the two, three, four, or five genes are compared to a corresponding control. [00161] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3, 53 314164997v1 Attorney Docket No: 243735.000437 wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00162] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, or eight genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, or eight genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, or eight genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3. In some embodiments, the expression levels of the two, three, four, five, six, seven, or eight genes are compared to a corresponding control. [00163] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00164] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes are compared to a corresponding 54 314164997v1 Attorney Docket No: 243735.000437 control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes are compared to a corresponding control. [00165] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00166] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes selected from human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes selected from human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes are compared to a corresponding control. [00167] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: 55 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD3G, THEMIS, CD5, and CD6, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00168] In some embodiments, the expression levels are determined for three or more genes, or four genes selected from human genes CD3G, THEMIS, CD5, and CD6. In some embodiments, the expression levels of the three or more genes, or four genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, or four genes selected from human genes CD3G, THEMIS, CD5, and CD6. In some embodiments, the expression levels of the two, three, or four genes are compared to a corresponding control. [00169] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of CD8B and/or CD8A, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the gene(s) determined in step (a) to a corresponding control. [00170] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRG1, GZMK, CST7, and GZMH, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00171] In some embodiments, the expression levels are determined for three or more genes, or four genes selected from human genes KLRG1, GZMK, CST7, and GZMH. In some 56 314164997v1 Attorney Docket No: 243735.000437 embodiments, the expression levels of the three or more genes, or four genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, or four genes selected from human genes KLRG1, GZMK, CST7, and GZMH. In some embodiments, the expression levels of the two, three, or four genes are compared to a corresponding control. [00172] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00173] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, or six genes selected from human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, or six genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, or six genes selected from human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7. In some embodiments, the expression levels of the two, three, four, five, or six genes are compared to a corresponding control. [00174] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD4, CD40LG, CCR2, CCR6, and DPP4, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 57 314164997v1 Attorney Docket No: 243735.000437 [00175] In some embodiments, the expression levels are determined for three or more genes, four or more genes, or five genes selected from human genes CD4, CD40LG, CCR2, CCR6, and DPP4. In some embodiments, the expression levels of the three or more genes, four or more genes, or five genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, or five genes selected from human genes CD4, CD40LG, CCR2, CCR6, and DPP4. In some embodiments, the expression levels of the two, three, four, or five genes are compared to a corresponding control. [00176] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00177] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, or seven genes selected from human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, or seven genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, or seven genes selected from human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA. In some embodiments, the expression levels of the two, three, four, five, six, or seven genes are compared to a corresponding control. [00178] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: 58 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00179] In some embodiments, the expression levels are determined for three or more genes, or four genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67. In some embodiments, the expression levels of the three or more genes, or four genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, or four genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67. In some embodiments, the expression levels of the two, three, or four genes are compared to a corresponding control. [00180] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GZMA, PRF1, and FASLG, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00181] In another embodiment, the expression levels are determined for two or three genes selected from human genes GZMA, PRF1, and FASLG. In some embodiments, the expression levels of the two or three genes are compared to a corresponding control. [00182] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes TOP2A, TYMS, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and 59 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00183] In another embodiment, the expression levels are determined for two or three genes selected from human genes TOP2A, TYMS, and MKI67. In some embodiments, the expression levels of the two or three genes are compared to a corresponding control. [00184] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00185] In some embodiments, the expression levels are determined for three or more genes, four or more genes, or five genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1. In some embodiments, the expression levels of the three or more genes, four or more genes, or five genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, or five genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1. In some embodiments, the expression levels of the two, three, four, or five genes are compared to a corresponding control. [00186] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 60 314164997v1 Attorney Docket No: 243735.000437 [00187] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, or six genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, or six genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, or six genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2. In some embodiments, the expression levels of the two, three, four, five, or six genes are compared to a corresponding control. [00188] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00189] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, or eight genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, or eight genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, or eight genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2. In some embodiments, the expression levels of the two, three, four, five, six, seven, or eight genes are compared to a corresponding control. [00190] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: 61 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00191] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or 62 314164997v1 Attorney Docket No: 243735.000437 more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty- five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy-six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty-three or more genes, eighty-four or more genes, eighty-five or more genes, eighty- six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, ninety-one or more genes, ninety-two or more genes, ninety-three or more genes, ninety-four or more genes, ninety-five or more genes, ninety-six or more genes, ninety-seven or more genes, ninety-eight or more genes, ninety-nine or more genes, one hundred or more genes, one hundred and one or more genes, one hundred and two or more genes, one hundred and three or more genes, one hundred and four or more genes, one hundred and five or more genes, one hundred and six or more genes, one hundred and seven or more genes, one hundred and eight or more genes, one hundred and nine or more genes, one hundred and ten or more genes, one hundred and eleven or more genes, one hundred and twelve or more genes, one hundred and thirteen or more genes, or one hundred and fourteen genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, 63 314164997v1 Attorney Docket No: 243735.000437 seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty-five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy-six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty-three or more genes, eighty-four or more genes, eighty-five or more genes, eighty-six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, ninety-one or more genes, ninety- two or more genes, ninety-three or more genes, ninety-four or more genes, ninety-five or more genes, ninety-six or more genes, ninety-seven or more genes, ninety-eight or more genes, ninety- nine or more genes, one hundred or more genes, one hundred and one or more genes, one hundred and two or more genes, one hundred and three or more genes, one hundred and four or more genes, one hundred and five or more genes, one hundred and six or more genes, one hundred and seven or more genes, one hundred and eight or more genes, one hundred and nine or more genes, one hundred and ten or more genes, one hundred and eleven or more genes, one hundred and twelve or more genes, one hundred and thirteen or more genes, or one hundred and 64 314164997v1 Attorney Docket No: 243735.000437 fourteen genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty- two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty- nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy- nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty-six, eighty- seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety-two, ninety-three, ninety-four, ninety- five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred and one, one hundred and two, one hundred and three, one hundred and four, one hundred and five, one hundred and six, one hundred and seven, one hundred and eight, one hundred and nine, one hundred and ten, one hundred and eleven, one hundred and twelve, one hundred and thirteen, or one hundred and fourteen genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty- 65 314164997v1 Attorney Docket No: 243735.000437 seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty- five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty- four, eighty-five, eighty-six, eighty-seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety- two, ninety-three, ninety-four, ninety-five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred and one, one hundred and two, one hundred and three, one hundred and four, one hundred and five, one hundred and six, one hundred and seven, one hundred and eight, one hundred and nine, one hundred and ten, one hundred and eleven, one hundred and twelve, one hundred and thirteen, or one hundred and fourteen genes are compared to a corresponding control. [00192] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 66 314164997v1 Attorney Docket No: 243735.000437 [00193] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty- five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy-six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty-three or more genes, eighty-four or more genes, eighty-five or more genes, eighty- six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, or ninety-one genes, selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, 67 314164997v1 Attorney Docket No: 243735.000437 FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty- four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty-five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy- six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty- three or more genes, eighty-four or more genes, eighty-five or more genes, eighty-six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, or ninety-one genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty- seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty- 68 314164997v1 Attorney Docket No: 243735.000437 five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty- four, eighty-five, eighty-six, eighty-seven, eighty-eight, eighty-nine, ninety, or ninety-one genes, selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty- three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty- seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty- six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty-six, eighty-seven, eighty- eight, eighty-nine, ninety, or ninety-one genes are compared to a corresponding control. [00194] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: 69 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00195] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty- five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two 70 314164997v1 Attorney Docket No: 243735.000437 or more genes, or seventy-three genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty- four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty-five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, or seventy-three genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- 71 314164997v1 Attorney Docket No: 243735.000437 seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, or seventy-three genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty- seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty- five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, or seventy-three genes are compared to a corresponding control. [00196] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 72 314164997v1 Attorney Docket No: 243735.000437 [00197] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, or thirteen genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen genes are compared to a corresponding control. [00198] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00199] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, or ten genes selected from porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, or ten genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, or ten genes selected from porcine genes C3, CXCL2, 73 314164997v1 Attorney Docket No: 243735.000437 C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, or ten genes are compared to a corresponding control. [00200] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00201] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, or twenty-three genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, or twenty-three genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, or twenty-three genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, 74 314164997v1 Attorney Docket No: 243735.000437 CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, or twenty-three genes are compared to a corresponding control. [00202] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00203] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, or twenty-five genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, 75 314164997v1 Attorney Docket No: 243735.000437 or twenty-five genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, or twenty-five genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, or twenty-five genes are compared to a corresponding control. [00204] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00205] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four 76 314164997v1 Attorney Docket No: 243735.000437 or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty- five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, or seventy-one genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty- seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more 77 314164997v1 Attorney Docket No: 243735.000437 genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty- six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty-five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, or seventy-one genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, or seventy-one genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty- one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, 78 314164997v1 Attorney Docket No: 243735.000437 sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, or seventy-one genes are compared to a corresponding control. [00206] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. [00207] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more 79 314164997v1 Attorney Docket No: 243735.000437 genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty- five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy-six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty-three or more genes, eighty-four or more genes, eighty-five or more genes, eighty- six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, ninety-one or more genes, ninety-two or more genes, ninety-three or more genes, ninety-four or more genes, ninety-five or more genes, ninety-six or more genes, ninety-seven or more genes, ninety-eight or more genes, ninety-nine or more genes, one hundred or more genes, one hundred and one or more genes, one hundred and two or more genes, one hundred and three or more genes, one hundred and four or more genes, one hundred and five or more genes, one hundred and six or more genes, one hundred and seven or more genes, one hundred and eight or more genes, one hundred and nine or more genes, one hundred and ten or more genes, one hundred and eleven or more genes, one hundred and twelve or more genes, one hundred and thirteen or more genes, one hundred and fourteen or more genes, one hundred and 80 314164997v1 Attorney Docket No: 243735.000437 fifteen or more genes, one hundred and sixteen or more genes, one hundred and seventeen or more genes, one hundred and eighteen or more genes, one hundred and nineteen or more genes, one hundred and twenty or more genes, one hundred and twenty-one or more genes, one hundred and twenty-two or more genes, one hundred and twenty-three or more genes, one hundred and twenty-four or more genes, one hundred and twenty-five or more genes, one hundred and twenty-six or more genes, one twenty-seven or more genes, one hundred and twenty-eight or more genes, one hundred and twenty-nine or more genes, one hundred and thirty or more genes, one hundred and thirty-one or more genes, one hundred and thirty-two or more genes, one hundred and thirty-three or more genes, one hundred and thirty-four or more genes, one hundred and thirty-five or more genes, one hundred and thirty-six or more genes, one hundred and thirty- seven or more genes, one hundred and thirty-eight or more genes, one hundred and thirty-nine or more genes, one hundred and forty or more genes, one hundred and forty-one or more genes, one hundred and forty-two or more genes, one hundred and forty-three or more genes, one hundred and forty-four or more genes, one hundred and forty-five or more genes, one hundred and forty- six or more genes, one hundred and forty-seven or more genes, one hundred and forty-eight or more genes, one hundred and forty-nine or more genes, one hundred and fifty or more genes, one hundred and fifty-one or more genes, one hundred and fifty-two or more genes, one hundred and fifty-three or more genes, one hundred and fifty-four or more genes, one hundred and fifty-five or more genes, one hundred and fifty-six or more genes, one hundred and fifty-seven or more genes, one hundred and fifty-eight or more genes, one hundred and fifty-nine or more genes, one hundred and sixty or more genes, one hundred and sixty-one or more genes, one hundred and sixty-two or more genes, one hundred and sixty-three or more genes, one hundred and sixty-four or more genes, one hundred and sixty-five or more genes, one hundred and sixty-six or more genes, one hundred and sixty-seven or more genes, one hundred and sixty-eight or more genes, one hundred and sixty-nine or more genes, one hundred and seventy or more genes, one hundred and seventy-one or more genes, one hundred and seventy-two or more genes, one hundred and seventy-three or more genes, one hundred and seventy-four or more genes, one hundred and seventy-five or more genes, one hundred and seventy-six or more genes, one hundred and seventy-seven or more genes, one hundred and seventy-eight or more genes, one hundred and seventy-nine or more genes, one hundred and eighty or more genes, one hundred and eighty-one or more genes, one hundred and eighty-two or more genes, one hundred and eighty-three or more 81 314164997v1 Attorney Docket No: 243735.000437 genes, one hundred and eighty-four or more genes, one hundred and eighty-five or more genes, one hundred and eighty-six or more genes, one hundred and eighty-seven or more genes, one hundred and eighty-eight or more genes, one hundred and eighty-nine or more genes, one hundred and ninety or more genes, one hundred and ninety-one or more genes, one hundred and ninety-two or more genes, one hundred and ninety-three or more genes, one hundred and ninety- four or more genes, one hundred and ninety-five or more genes, one hundred and ninety-six or more genes, one hundred and ninety-seven or more genes, one hundred and ninety-eight or more genes, or one hundred and ninety-nine genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or 82 314164997v1 Attorney Docket No: 243735.000437 more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty- two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty- nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty- four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty-five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy- six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty- three or more genes, eighty-four or more genes, eighty-five or more genes, eighty-six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, ninety-one or more genes, ninety-two or more genes, ninety-three or more genes, ninety-four or more genes, ninety-five or more genes, ninety-six or more genes, ninety- seven or more genes, ninety-eight or more genes, ninety-nine or more genes, one hundred or more genes, one hundred and one or more genes, one hundred and two or more genes, one hundred and three or more genes, one hundred and four or more genes, one hundred and five or more genes, one hundred and six or more genes, one hundred and seven or more genes, one hundred and eight or more genes, one hundred and nine or more genes, one hundred and ten or more genes, one hundred and eleven or more genes, one hundred and twelve or more genes, one hundred and thirteen or more genes, one hundred and fourteen or more genes, one hundred and fifteen or more genes, one hundred and sixteen or more genes, one hundred and seventeen or more genes, one hundred and eighteen or more genes, one hundred and nineteen or more genes, one hundred and twenty or more genes, one hundred and twenty-one or more genes, one hundred 83 314164997v1 Attorney Docket No: 243735.000437 and twenty-two or more genes, one hundred and twenty-three or more genes, one hundred and twenty-four or more genes, one hundred and twenty-five or more genes, one hundred and twenty-six or more genes, one twenty-seven or more genes, one hundred and twenty-eight or more genes, one hundred and twenty-nine or more genes, one hundred and thirty or more genes, one hundred and thirty-one or more genes, one hundred and thirty-two or more genes, one hundred and thirty-three or more genes, one hundred and thirty-four or more genes, one hundred and thirty-five or more genes, one hundred and thirty-six or more genes, one hundred and thirty- seven or more genes, one hundred and thirty-eight or more genes, one hundred and thirty-nine or more genes, one hundred and forty or more genes, one hundred and forty-one or more genes, one hundred and forty-two or more genes, one hundred and forty-three or more genes, one hundred and forty-four or more genes, one hundred and forty-five or more genes, one hundred and forty- six or more genes, one hundred and forty-seven or more genes, one hundred and forty-eight or more genes, one hundred and forty-nine or more genes, one hundred and fifty or more genes, one hundred and fifty-one or more genes, one hundred and fifty-two or more genes, one hundred and fifty-three or more genes, one hundred and fifty-four or more genes, one hundred and fifty-five or more genes, one hundred and fifty-six or more genes, one hundred and fifty-seven or more genes, one hundred and fifty-eight or more genes, one hundred and fifty-nine or more genes, one hundred and sixty or more genes, one hundred and sixty-one or more genes, one hundred and sixty-two or more genes, one hundred and sixty-three or more genes, one hundred and sixty-four or more genes, one hundred and sixty-five or more genes, one hundred and sixty-six or more genes, one hundred and sixty-seven or more genes, one hundred and sixty-eight or more genes, one hundred and sixty-nine or more genes, one hundred and seventy or more genes, one hundred and seventy-one or more genes, one hundred and seventy-two or more genes, one hundred and seventy-three or more genes, one hundred and seventy-four or more genes, one hundred and seventy-five or more genes, one hundred and seventy-six or more genes, one hundred and seventy-seven or more genes, one hundred and seventy-eight or more genes, one hundred and seventy-nine or more genes, one hundred and eighty or more genes, one hundred and eighty-one or more genes, one hundred and eighty-two or more genes, one hundred and eighty-three or more genes, one hundred and eighty-four or more genes, one hundred and eighty-five or more genes, one hundred and eighty-six or more genes, one hundred and eighty-seven or more genes, one hundred and eighty-eight or more genes, one hundred and eighty-nine or more genes, one 84 314164997v1 Attorney Docket No: 243735.000437 hundred and ninety or more genes, one hundred and ninety-one or more genes, one hundred and ninety-two or more genes, one hundred and ninety-three or more genes, one hundred and ninety- four or more genes, one hundred and ninety-five or more genes, one hundred and ninety-six or more genes, one hundred and ninety-seven or more genes, one hundred and ninety-eight or more genes, or one hundred and ninety-nine genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty- seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty- five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty- four, eighty-five, eighty-six, eighty-seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety- two, ninety-three, ninety-four, ninety-five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred one, one hundred two, one hundred three, one hundred four, one hundred five, one hundred six, one hundred seven, one hundred eight, one hundred nine, one hundred ten, one hundred eleven, one hundred twelve, one hundred thirteen, one hundred fourteen, one hundred fifteen, one hundred sixteen, one hundred seventeen, one hundred eighteen, one hundred nineteen, one hundred twenty, one hundred twenty-one, one hundred twenty-two, one hundred twenty-three, one hundred twenty-four, one hundred twenty-five, one hundred twenty-six, one hundred twenty-seven, one hundred twenty-eight, one hundred twenty- nine, one hundred thirty, one hundred thirty-one, one hundred thirty-two, one hundred thirty- three, one hundred thirty-four, one hundred thirty-five, one hundred thirty-six, one hundred thirty-seven, one hundred thirty-eight, one hundred thirty-nine, one hundred forty, one hundred forty-one, one hundred forty-two, one hundred forty-three, one hundred forty-four, one hundred forty-five, one hundred forty-six, one hundred forty-seven, one hundred forty-eight, one hundred forty-nine, one hundred fifty, one hundred fifty-one, one hundred fifty-two, one hundred fifty- three, one hundred fifty-four, one hundred fifty-five, one hundred fifty-six, one hundred fifty- 85 314164997v1 Attorney Docket No: 243735.000437 seven, one hundred fifty-eight, one hundred fifty-nine, one hundred sixty, one hundred sixty-one, one hundred sixty-two, one hundred sixty-three, one hundred sixty-four, one hundred sixty-five, one hundred sixty-six, one hundred sixty-seven, one hundred sixty-eight, one hundred sixty-nine, one hundred seventy, one hundred seventy-one, one hundred seventy-two, one hundred seventy- three, one hundred seventy-four, one hundred seventy-five, one hundred seventy-six, one hundred seventy-seven, one hundred seventy-eight, one hundred seventy-nine, one hundred eighty, one hundred eighty-one, one hundred eighty-two, one hundred eighty-three, one hundred eighty-four, one hundred eighty-five, one hundred eighty-six, one hundred eighty-seven, one hundred eighty-eight, one hundred eighty-nine, one hundred ninety, one hundred ninety-one, one hundred ninety-two, one hundred ninety-three, one hundred ninety-four, one hundred ninety-five, one hundred ninety-six, one hundred ninety-seven, one hundred ninety-eight, or one hundred ninety-nine genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, 86 314164997v1 Attorney Docket No: 243735.000437 eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty- one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy- eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty- six, eighty-seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety-two, ninety-three, ninety- four, ninety-five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred one, one hundred two, one hundred three, one hundred four, one hundred five, one hundred six, one hundred seven, one hundred eight, one hundred nine, one hundred ten, one hundred eleven, one hundred twelve, one hundred thirteen, one hundred fourteen, one hundred fifteen, one hundred sixteen, one hundred seventeen, one hundred eighteen, one hundred nineteen, one hundred twenty, one hundred twenty-one, one hundred twenty-two, one hundred twenty-three, one hundred twenty-four, one hundred twenty-five, one hundred twenty-six, one hundred twenty- seven, one hundred twenty-eight, one hundred twenty-nine, one hundred thirty, one hundred thirty-one, one hundred thirty-two, one hundred thirty-three, one hundred thirty-four, one hundred thirty-five, one hundred thirty-six, one hundred thirty-seven, one hundred thirty-eight, one hundred thirty-nine, one hundred forty, one hundred forty-one, one hundred forty-two, one hundred forty-three, one hundred forty-four, one hundred forty-five, one hundred forty-six, one hundred forty-seven, one hundred forty-eight, one hundred forty-nine, one hundred fifty, one hundred fifty-one, one hundred fifty-two, one hundred fifty-three, one hundred fifty-four, one hundred fifty-five, one hundred fifty-six, one hundred fifty-seven, one hundred fifty-eight, one hundred fifty-nine, one hundred sixty, one hundred sixty-one, one hundred sixty-two, one hundred sixty-three, one hundred sixty-four, one hundred sixty-five, one hundred sixty-six, one hundred sixty-seven, one hundred sixty-eight, one hundred sixty-nine, one hundred seventy, one hundred seventy-one, one hundred seventy-two, one hundred seventy-three, one hundred seventy-four, one hundred seventy-five, one hundred seventy-six, one hundred seventy-seven, one hundred seventy-eight, one hundred seventy-nine, one hundred eighty, one hundred eighty- 87 314164997v1 Attorney Docket No: 243735.000437 one, one hundred eighty-two, one hundred eighty-three, one hundred eighty-four, one hundred eighty-five, one hundred eighty-six, one hundred eighty-seven, one hundred eighty-eight, one hundred eighty-nine, one hundred ninety, one hundred ninety-one, one hundred ninety-two, one hundred ninety-three, one hundred ninety-four, one hundred ninety-five, one hundred ninety-six, one hundred ninety-seven, one hundred ninety-eight, or one hundred ninety-nine genes are compared to a corresponding control. [00208] In another aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 88 314164997v1 Attorney Docket No: 243735.000437 [00209] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty- five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy-six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty-three or more genes, eighty-four or more genes, eighty-five or more genes, eighty- six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, ninety-one or more genes, ninety-two or more genes, ninety-three or more genes, ninety-four or more genes, ninety-five or more genes, ninety-six or more genes, ninety-seven or more genes, ninety-eight or more genes, ninety-nine or more genes, one hundred or more genes, one hundred and one or more genes, one hundred and two or more genes, one hundred and three or more genes, one hundred and four or more genes, one hundred and five or more genes, one hundred and six or more genes, one hundred and seven or more genes, one hundred and eight or more genes, one hundred and nine or more genes, one hundred and ten or 89 314164997v1 Attorney Docket No: 243735.000437 more genes, one hundred and eleven or more genes, one hundred and twelve or more genes, one hundred and thirteen or more genes, one hundred and fourteen or more genes, one hundred and fifteen or more genes, one hundred and sixteen or more genes, one hundred and seventeen or more genes, one hundred and eighteen or more genes, one hundred and nineteen or more genes, one hundred and twenty or more genes, one hundred and twenty-one or more genes, one hundred and twenty-two or more genes, one hundred and twenty-three or more genes, one hundred and twenty-four or more genes, one hundred and twenty-five or more genes, one hundred and twenty-six or more genes, one twenty-seven or more genes, one hundred and twenty-eight or more genes, one hundred and twenty-nine or more genes, one hundred and thirty or more genes, one hundred and thirty-one or more genes, one hundred and thirty-two or more genes, one hundred and thirty-three or more genes, one hundred and thirty-four or more genes, one hundred and thirty-five or more genes, one hundred and thirty-six or more genes, one hundred and thirty- seven or more genes, one hundred and thirty-eight or more genes, one hundred and thirty-nine or more genes, one hundred and forty or more genes, one hundred and forty-one or more genes, one hundred and forty-two or more genes, one hundred and forty-three or more genes, one hundred and forty-four or more genes, one hundred and forty-five or more genes, one hundred and forty- six or more genes, one hundred and forty-seven or more genes, one hundred and forty-eight or more genes, one hundred and forty-nine or more genes, one hundred and fifty or more genes, one hundred and fifty-one or more genes, one hundred and fifty-two or more genes, one hundred and fifty-three or more genes, one hundred and fifty-four or more genes, one hundred and fifty-five or more genes, one hundred and fifty-six or more genes, one hundred and fifty-seven or more genes, one hundred and fifty-eight or more genes, one hundred and fifty-nine or more genes, one hundred and sixty or more genes, one hundred and sixty-one or more genes, or one hundred and sixty-two genes selected from 1) human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, 90 314164997v1 Attorney Docket No: 243735.000437 CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty-five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy-six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty-three or more genes, eighty-four or more genes, eighty-five or more genes, eighty-six or more genes, eighty-seven or more genes, eighty-eight or 91 314164997v1 Attorney Docket No: 243735.000437 more genes, eighty-nine or more genes, ninety or more genes, ninety-one or more genes, ninety- two or more genes, ninety-three or more genes, ninety-four or more genes, ninety-five or more genes, ninety-six or more genes, ninety-seven or more genes, ninety-eight or more genes, ninety- nine or more genes, one hundred or more genes, one hundred and one or more genes, one hundred and two or more genes, one hundred and three or more genes, one hundred and four or more genes, one hundred and five or more genes, one hundred and six or more genes, one hundred and seven or more genes, one hundred and eight or more genes, one hundred and nine or more genes, one hundred and ten or more genes, one hundred and eleven or more genes, one hundred and twelve or more genes, one hundred and thirteen or more genes, one hundred and fourteen or more genes, one hundred and fifteen or more genes, one hundred and sixteen or more genes, one hundred and seventeen or more genes, one hundred and eighteen or more genes, one hundred and nineteen or more genes, one hundred and twenty or more genes, one hundred and twenty-one or more genes, one hundred and twenty-two or more genes, one hundred and twenty- three or more genes, one hundred and twenty-four or more genes, one hundred and twenty-five or more genes, one hundred and twenty-six or more genes, one twenty-seven or more genes, one hundred and twenty-eight or more genes, one hundred and twenty-nine or more genes, one hundred and thirty or more genes, one hundred and thirty-one or more genes, one hundred and thirty-two or more genes, one hundred and thirty-three or more genes, one hundred and thirty- four or more genes, one hundred and thirty-five or more genes, one hundred and thirty-six or more genes, one hundred and thirty-seven or more genes, one hundred and thirty-eight or more genes, one hundred and thirty-nine or more genes, one hundred and forty or more genes, one hundred and forty-one or more genes, one hundred and forty-two or more genes, one hundred and forty-three or more genes, one hundred and forty-four or more genes, one hundred and forty- five or more genes, one hundred and forty-six or more genes, one hundred and forty-seven or more genes, one hundred and forty-eight or more genes, one hundred and forty-nine or more genes, one hundred and fifty or more genes, one hundred and fifty-one or more genes, one hundred and fifty-two or more genes, one hundred and fifty-three or more genes, one hundred and fifty-four or more genes, one hundred and fifty-five or more genes, one hundred and fifty-six or more genes, one hundred and fifty-seven or more genes, one hundred and fifty-eight or more genes, one hundred and fifty-nine or more genes, one hundred and sixty or more genes, one hundred and sixty-one or more genes, or one hundred and sixty-two genes are compared to a 92 314164997v1 Attorney Docket No: 243735.000437 corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty- one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty- nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty- eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy-nine, eighty, eighty-one, eighty-two, eighty- three, eighty-four, eighty-five, eighty-six, eighty-seven, eighty-eight, eighty-nine, ninety, ninety- one, ninety-two, ninety-three, ninety-four, ninety-five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred one, one hundred two, one hundred three, one hundred four, one hundred five, one hundred six, one hundred seven, one hundred eight, one hundred nine, one hundred ten, one hundred eleven, one hundred twelve, one hundred thirteen, one hundred fourteen, one hundred fifteen, one hundred sixteen, one hundred seventeen, one hundred eighteen, one hundred nineteen, one hundred twenty, one hundred twenty-one, one hundred twenty-two, one hundred twenty-three, one hundred twenty-four, one hundred twenty-five, one hundred twenty-six, one hundred twenty-seven, one hundred twenty-eight, one hundred twenty- nine, one hundred thirty, one hundred thirty-one, one hundred thirty-two, one hundred thirty- three, one hundred thirty-four, one hundred thirty-five, one hundred thirty-six, one hundred thirty-seven, one hundred thirty-eight, one hundred thirty-nine, one hundred forty, one hundred forty-one, one hundred forty-two, one hundred forty-three, one hundred forty-four, one hundred forty-five, one hundred forty-six, one hundred forty-seven, one hundred forty-eight, one hundred forty-nine, one hundred fifty, one hundred fifty-one, one hundred fifty-two, one hundred fifty- three, one hundred fifty-four, one hundred fifty-five, one hundred fifty-six, one hundred fifty- seven, one hundred fifty-eight, one hundred fifty-nine, one hundred sixty, one hundred sixty-one, or one hundred sixty-two genes selected from 1) human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, 93 314164997v1 Attorney Docket No: 243735.000437 STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty- two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty- nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy- nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty-six, eighty- seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety-two, ninety-three, ninety-four, ninety- five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred one, one hundred two, one hundred three, one hundred four, one hundred five, one hundred six, one hundred seven, one hundred eight, one hundred nine, one hundred ten, one hundred eleven, one hundred twelve, one hundred thirteen, one hundred fourteen, one hundred fifteen, one hundred sixteen, one hundred seventeen, one hundred eighteen, one hundred nineteen, one hundred twenty, one hundred twenty-one, one hundred twenty-two, one hundred twenty-three, one hundred twenty-four, one hundred twenty-five, one hundred twenty-six, one hundred twenty- seven, one hundred twenty-eight, one hundred twenty-nine, one hundred thirty, one hundred 94 314164997v1 Attorney Docket No: 243735.000437 thirty-one, one hundred thirty-two, one hundred thirty-three, one hundred thirty-four, one hundred thirty-five, one hundred thirty-six, one hundred thirty-seven, one hundred thirty-eight, one hundred thirty-nine, one hundred forty, one hundred forty-one, one hundred forty-two, one hundred forty-three, one hundred forty-four, one hundred forty-five, one hundred forty-six, one hundred forty-seven, one hundred forty-eight, one hundred forty-nine, one hundred fifty, one hundred fifty-one, one hundred fifty-two, one hundred fifty-three, one hundred fifty-four, one hundred fifty-five, one hundred fifty-six, one hundred fifty-seven, one hundred fifty-eight, one hundred fifty-nine, one hundred sixty, one hundred sixty-one, or one hundred sixty-two genes are compared to a corresponding control. [00210] In a further aspect, provided herein is a method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 95 314164997v1 Attorney Docket No: 243735.000437 [00211] In some embodiments, the expression levels are determined for three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty-two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty-five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty- five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy-six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty-three or more genes, eighty-four or more genes, eighty-five or more genes, eighty- six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, ninety-one or more genes, ninety-two or more genes, ninety-three or more genes, ninety-four or more genes, ninety-five or more genes, ninety-six or more genes, ninety-seven or more genes, ninety-eight or more genes, ninety-nine or more genes, one hundred or more genes, one hundred and one or more genes, one hundred and two or more genes, one hundred and three or more genes, one hundred and four or more genes, one hundred and five or more genes, one hundred and six or more genes, one hundred and seven or more genes, one hundred and eight or more genes, one hundred and nine or more genes, one hundred and ten or 96 314164997v1 Attorney Docket No: 243735.000437 more genes, one hundred and eleven or more genes, one hundred and twelve or more genes, one hundred and thirteen or more genes, one hundred and fourteen or more genes, one hundred and fifteen or more genes, one hundred and sixteen or more genes, one hundred and seventeen or more genes, one hundred and eighteen or more genes, one hundred and nineteen or more genes, one hundred and twenty or more genes, one hundred and twenty-one or more genes, one hundred and twenty-two or more genes, one hundred and twenty-three or more genes, one hundred and twenty-four or more genes, one hundred and twenty-five or more genes, one hundred and twenty-six or more genes, one twenty-seven or more genes, one hundred and twenty-eight or more genes, one hundred and twenty-nine or more genes, one hundred and thirty or more genes, one hundred and thirty-one or more genes, one hundred and thirty-two or more genes, one hundred and thirty-three or more genes, one hundred and thirty-four or more genes, one hundred and thirty-five or more genes, one hundred and thirty-six or more genes, one hundred and thirty- seven or more genes, one hundred and thirty-eight or more genes, one hundred and thirty-nine or more genes, one hundred and forty or more genes, one hundred and forty-one or more genes, one hundred and forty-two or more genes, one hundred and forty-three or more genes, or one hundred and forty-four genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the three or more genes, four or more genes, five or more genes, six or more genes, seven or more genes, eight or more genes, nine or more genes, ten or 97 314164997v1 Attorney Docket No: 243735.000437 more genes, eleven or more genes, twelve or more genes, thirteen or more genes, fourteen or more genes, fifteen or more genes, sixteen or more genes, seventeen or more genes, eighteen or more genes, nineteen or more genes, twenty or more genes, twenty-one or more genes, twenty- two or more genes, twenty-three or more genes, twenty-four or more genes, twenty-five or more genes, twenty-six or more genes, twenty-seven or more genes, twenty-eight or more genes, twenty-nine or more genes, thirty or more genes, thirty-one or more genes, thirty-two or more genes, thirty-three or more genes, thirty-four or more genes, thirty-five or more genes, thirty-six or more genes, thirty-seven or more genes, thirty-eight or more genes, thirty-nine or more genes, forty or more genes, forty-one or more genes, forty-two or more genes, forty-three or more genes, forty-four or more genes, forty-five or more genes, forty-six or more genes, forty-seven or more genes, forty-eight or more genes, forty-nine or more genes, fifty or more genes, fifty-one or more genes, fifty-two or more genes, fifty-three or more genes, fifty-four or more genes, fifty- five or more genes, fifty-six or more genes, fifty-seven or more genes, fifty-eight or more genes, fifty-nine or more genes, sixty or more genes, sixty-one or more genes, sixty-two or more genes, sixty-three or more genes, sixty-four or more genes, sixty-five or more genes, sixty-six or more genes, sixty-seven or more genes, sixty-eight or more genes, sixty-nine or more genes, seventy or more genes, seventy-one or more genes, seventy-two or more genes, seventy-three or more genes, seventy-four or more genes, seventy-five or more genes, seventy-six or more genes, seventy-seven or more genes, seventy-eight or more genes, seventy-nine or more genes, eighty or more genes, eighty-one or more genes, eighty-two or more genes, eighty-three or more genes, eighty-four or more genes, eighty-five or more genes, eighty-six or more genes, eighty-seven or more genes, eighty-eight or more genes, eighty-nine or more genes, ninety or more genes, ninety-one or more genes, ninety-two or more genes, ninety-three or more genes, ninety-four or more genes, ninety-five or more genes, ninety-six or more genes, ninety-seven or more genes, ninety-eight or more genes, ninety-nine or more genes, one hundred or more genes, one hundred and one or more genes, one hundred and two or more genes, one hundred and three or more genes, one hundred and four or more genes, one hundred and five or more genes, one hundred and six or more genes, one hundred and seven or more genes, one hundred and eight or more genes, one hundred and nine or more genes, one hundred and ten or more genes, one hundred and eleven or more genes, one hundred and twelve or more genes, one hundred and thirteen or more genes, one hundred and fourteen or more genes, one hundred and fifteen or more genes, 98 314164997v1 Attorney Docket No: 243735.000437 one hundred and sixteen or more genes, one hundred and seventeen or more genes, one hundred and eighteen or more genes, one hundred and nineteen or more genes, one hundred and twenty or more genes, one hundred and twenty-one or more genes, one hundred and twenty-two or more genes, one hundred and twenty-three or more genes, one hundred and twenty-four or more genes, one hundred and twenty-five or more genes, one hundred and twenty-six or more genes, one twenty-seven or more genes, one hundred and twenty-eight or more genes, one hundred and twenty-nine or more genes, one hundred and thirty or more genes, one hundred and thirty-one or more genes, one hundred and thirty-two or more genes, one hundred and thirty-three or more genes, one hundred and thirty-four or more genes, one hundred and thirty-five or more genes, one hundred and thirty-six or more genes, one hundred and thirty-seven or more genes, one hundred and thirty-eight or more genes, one hundred and thirty-nine or more genes, one hundred and forty or more genes, one hundred and forty-one or more genes, one hundred and forty-two or more genes, one hundred and forty-three or more genes, or one hundred and forty-four genes are compared to a corresponding control. In another embodiment, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty- three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty- seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty- six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty-six, eighty-seven, eighty- eight, eighty-nine, ninety, ninety-one, ninety-two, ninety-three, ninety-four, ninety-five, ninety- six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred one, one hundred two, one hundred three, one hundred four, one hundred five, one hundred six, one hundred seven, one hundred eight, one hundred nine, one hundred ten, one hundred eleven, one hundred twelve, one hundred thirteen, one hundred fourteen, one hundred fifteen, one hundred sixteen, one hundred seventeen, one hundred eighteen, one hundred nineteen, one hundred twenty, one hundred twenty-one, one hundred twenty-two, one hundred twenty-three, one hundred twenty-four, one 99 314164997v1 Attorney Docket No: 243735.000437 hundred twenty-five, one hundred twenty-six, one hundred twenty-seven, one hundred twenty- eight, one hundred twenty-nine, one hundred thirty, one hundred thirty-one, one hundred thirty- two, one hundred thirty-three, one hundred thirty-four, one hundred thirty-five, one hundred thirty-six, one hundred thirty-seven, one hundred thirty-eight, one hundred thirty-nine, one hundred forty, one hundred forty-one, one hundred forty-two, one hundred forty-three, or one hundred forty-four genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1. In some embodiments, the expression levels of the two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty- one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy- eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty- six, eighty-seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety-two, ninety-three, ninety- four, ninety-five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred 100 314164997v1 Attorney Docket No: 243735.000437 one, one hundred two, one hundred three, one hundred four, one hundred five, one hundred six, one hundred seven, one hundred eight, one hundred nine, one hundred ten, one hundred eleven, one hundred twelve, one hundred thirteen, one hundred fourteen, one hundred fifteen, one hundred sixteen, one hundred seventeen, one hundred eighteen, one hundred nineteen, one hundred twenty, one hundred twenty-one, one hundred twenty-two, one hundred twenty-three, one hundred twenty-four, one hundred twenty-five, one hundred twenty-six, one hundred twenty- seven, one hundred twenty-eight, one hundred twenty-nine, one hundred thirty, one hundred thirty-one, one hundred thirty-two, one hundred thirty-three, one hundred thirty-four, one hundred thirty-five, one hundred thirty-six, one hundred thirty-seven, one hundred thirty-eight, one hundred thirty-nine, one hundred forty, one hundred forty-one, one hundred forty-two, one hundred forty-three, or one hundred forty-four genes are compared to a corresponding control. [00212] In some embodiments, the sample can comprise functionally-related cells or adjacent cells. Samples may comprise complex populations of cells, which can be assayed together as a population or separated into sub-populations. Cellular and acellular samples can be separated by elutriation, centrifugation, centrifugation with Hypaque, apheresis, density gradient separation, affinity selection, panning, FACS, etc. A homogeneous population of cells may be obtained via use of antibodies specific for markers identified with particular cell types. Instead, a heterogeneous cell population can be used. Cells may be separated using filters. For example, whole blood can be applied to filters that contain pore sizes that select for the desired cell class or type. Cells can be filtered out of diluted, whole blood after the lysis of red blood cells through the use of filters with pore sizes (e.g., between 5 to 10 μm, see U.S. Pat. No.09/790,673). Other devices can separate cells from the bloodstream (see e.g., Demirci, Toner, Direct etch method for microfluidic channel and nanoheight post-fabrication by picoliter droplets, Applied Physics Letters, 2006; 88 (5), 053117; and Irimia, Geba, Toner, Universal microfluidic gradient generator, Analytical Chemistry, 2006; 78: 3472-3477). Once a sample is obtained, it may be used directly, frozen, or maintained in suitable culture medium. [00213] To obtain a blood sample, any technique or protocol known in the art may be used (e.g., a syringe or other device that uses vacuum suction). A blood sample may be optionally pre- treated or processed before enrichment. Examples of pre-treatment steps may include the addition of a reagent (e.g., a fixant, a stabilizer, a preservative, a lysing reagent, a diluent, a magnetic property regulating reagent, an anti-apoptotic reagent, an anti-thrombotic reagent, an 101 314164997v1 Attorney Docket No: 243735.000437 anti-coagulation reagent, a buffering reagent, a cross-linking reagent, an osmolality regulating reagent, and/or a pH regulating reagent). [00214] Once a blood sample is obtained, a preservative (e.g., an anti-coagulation agent and/or a stabilizer) may be added to the sample before enrichment. Addition of a preservative allows for extended time for analysis and/or detection. Thus, a sample (e.g., a blood sample) may be analyzed using any of the methods and systems herein within over 1 week, 1 week, 6 days, 5 days, 4 days, 3 days, 2 days, 1 day, 18 hours, 12 hours, 6 hours, 3 hours, 2 hours, 1 hour, or less than 1 hour from the time the sample is obtained. [00215] In some embodiments, a blood sample may be combined with an agent that selectively lyses one or more components (e.g., one or more cells) in a blood sample. For example, enucleated red blood cells and/or platelets can be selectively lysed to generate a sample enriched in nucleated cells. Afterwards, cells of interest can be separated from the sample using methods and/or processes known in the art. [00216] When obtaining a sample (e.g., a blood sample) from a subject, the amount of sample can vary depending upon the condition being screened and the subject size. In some embodiments, up to 50 mL, 40 mL, 30 mL, 20 mL, 15 mL, 10 mL, 9 mL, 8 mL, 7 mL, 6 mL, 5 mL, 4 mL, 3 mL, 2 mL, or 1 mL of a sample is obtained. In some embodiments, about 1-50 mL, about 2-40 mL, about 3-30 mL, or about 4-20 mL of sample is obtained. In some embodiments, more than 5 mL, 10 mL, 15 mL, 20 mL, 25 mL, 30 mL, 35 mL, 40 mL, 45 mL, 50 mL, 55 mL, 60 mL, 65 mL, 70 mL, 75 mL, 80 mL, 85 mL, 90 mL, 95 mL, or 100 mL of a sample is obtained. [00217] In some embodiments, the method further comprises step c) (i) determining that the subject has a xenograft rejection or is at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are increased as compared to the control by 1.5-fold or more; or (ii) determining that the subject does not have a xenograft rejection or is not at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are decreased, not increased or increased by less than 1.5-fold as compared to the control. [00218] In some embodiments, the expression levels are determined for at least two of the genes, at least three of the genes, at least four of the genes, at least five of the genes, at least six of the genes, at least seven of the genes, at least eight of the genes, at least nine of the genes, at 102 314164997v1 Attorney Docket No: 243735.000437 least ten of the genes, at least eleven of the genes, at least twelve of the genes, at least thirteen of the genes, at least fourteen of the genes, at least fifteen of the genes, at least sixteen of the genes, at least seventeen of the genes, at least eighteen of the genes, at least nineteen of the genes, at least twenty of the genes, at least twenty-one of the genes, at least twenty-two of the genes, at least twenty-three of the genes, at least twenty-four of the genes, at least twenty-five of the genes, at least twenty-six of the genes, at least twenty-seven of the genes, at least twenty-eight of the genes, at least twenty-nine of the genes, at least thirty of the genes, at least thirty-one of the genes, at least thirty-two of the genes, at least thirty-three of the genes, at least thirty-four of the genes, at least thirty-five of the genes, at least thirty-six of the genes, at least thirty-seven of the genes, at least thirty-eight of the genes, at least thirty-nine of the genes, at least forty of the genes, at least forty-one of the genes, at least forty-two of the genes, at least forty-three of the genes, at least forty-four of the genes, at least forty-five of the genes, at least forty-six of the genes, at least forty-seven of the genes, at least forty-eight of the genes, at least forty-nine of the genes, at least fifty of the genes, at least fifty-one of the genes, at least fifty-two of the genes, at least fifty-three of the genes, at least fifty-four of the genes, at least fifty-five of the genes, at least fifty-six of the genes, at least fifty-seven of the genes, at least fifty-eight of the genes, at least fifty-nine of the genes, at least sixty of the genes, at least sixty-one of the genes, at least sixty-two of the genes, at least sixty-three of the genes, at least sixty-four of the genes, at least sixty-five of the genes, at least sixty-six of the genes, at least sixty-seven of the genes, at least sixty-eight of the genes, at least sixty-nine of the genes, at least seventy of the genes, at least seventy-one of the genes, at least seventy-two of the genes, at least seventy-three of the genes, at least seventy-four of the genes, at least seventy-five of the genes, at least seventy-six of the genes, at least seventy-seven of the genes, at least seventy-eight of the genes, at least seventy- nine of the genes, at least eighty of the genes, at least eighty-one of the genes, at least eighty-two of the genes, at least eighty-three of the genes, at least eighty-four of the genes, at least eighty- five of the genes, at least eighty-six of the genes, at least eighty-seven of the genes, at least eighty-eight of the genes, at least eighty-nine of the genes, at least ninety of the genes, at least ninety-one of the genes, at least ninety-two of the genes, at least ninety-three of the genes, at least ninety-four of the genes, at least ninety-five of the genes, at least ninety-six of the genes, at least ninety-seven of the genes, at least ninety-eight of the genes, at least ninety-nine of the genes, at least one hundred of the genes, at least one hundred one of the genes, at least one 103 314164997v1 Attorney Docket No: 243735.000437 hundred two of the genes, at least one hundred three of the genes, at least one hundred four of the genes, at least one hundred five of the genes, at least one hundred six of the genes, at least one hundred seven of the genes, at least one hundred eight of the genes, at least one hundred nine of the genes, at least one hundred ten of the genes, at least one hundred eleven of the genes, at least one hundred twelve of the genes, at least one hundred thirteen of the genes, at least one hundred fourteen of the genes, at least one hundred fifteen of the genes, at least one hundred sixteen of the genes, at least one hundred seventeen of the genes, at least one hundred eighteen of the genes, at least one hundred nineteen of the genes, at least one hundred twenty of the genes, at least one hundred twenty-one of the genes, at least one hundred twenty-two of the genes, at least one hundred twenty-three of the genes, at least one hundred twenty-four of the genes, at least one hundred twenty-five of the genes, at least one hundred twenty-six of the genes, at least one hundred twenty-seven of the genes, at least one hundred twenty-eight of the genes, at least one hundred twenty-nine of the genes, at least one hundred thirty of the genes, at least one hundred thirty-one of the genes, at least one hundred thirty-two of the genes, at least one hundred thirty- three of the genes, at least one hundred thirty-four of the genes, at least one hundred thirty-five of the genes, at least one hundred thirty-six of the at least one hundred thirty-seven of the genes, at least one hundred thirty-eight of the genes, at least one hundred thirty-nine of the genes, at least one hundred forty of the genes, at least one hundred forty-one of the genes, at least one hundred forty-two of the genes, at least one hundred forty-three of the genes, at least one hundred forty-four of the genes, at least one hundred forty-five of the genes, at least one hundred forty-six of the genes, at least one hundred forty-seven of the genes, at least one hundred forty- eight of the genes, at least one hundred forty-nine of the genes, at least one hundred fifty of the genes, at least one hundred fifty-one of the genes, at least one hundred fifty-two of the genes, at least one hundred fifty-three of the genes, at least one hundred fifty-four of the genes, at least one hundred fifty-five of the genes, at least one hundred fifty-six of the genes, at least one hundred fifty-seven of the genes, at least one hundred fifty-eight of the genes, at least one hundred fifty-nine of the genes, at least one hundred sixty of the genes, at least one hundred sixty-one of the genes, at least one hundred sixty-two of the genes, at least one hundred sixty- three of the genes, at least one hundred sixty-four of the genes, at least one hundred sixty-five of the genes, at least one hundred sixty-six of the genes, at least one hundred sixty-seven of the genes, at least one hundred sixty-eight of the genes, at least one hundred sixty-nine of the genes, 104 314164997v1 Attorney Docket No: 243735.000437 at least one hundred seventy of the genes, at least one hundred seventy-one of the genes, at least one hundred seventy-two of the genes, at least one hundred seventy-three of the genes, at least one hundred seventy-four of the genes, at least one hundred seventy-five of the genes, at least one hundred seventy-six of the genes, at least one hundred seventy-seven of the genes, at least one hundred seventy-eight of the genes, at least one hundred seventy-nine of the genes, at least one hundred eighty of the genes, at least one hundred eighty-one of the genes, at least one hundred eighty-two of the genes, at least one hundred eighty-three of the genes, at least one hundred eighty-four of the genes, at least one hundred eighty-five of the genes, at least one hundred eighty-six of the genes, at least one hundred eighty-seven of the genes, at least one hundred eighty-eight of the genes, at least one hundred eighty-nine of the genes, at least one hundred ninety of the genes, at least one hundred ninety-one of the genes, at least one hundred ninety-two of the genes, at least one hundred ninety-three of the genes, at least one hundred ninety-four of the genes, at least one hundred ninety-five of the genes, at least one hundred ninety-six of the genes, at least one hundred ninety-seven of the genes, at least one hundred ninety-eight of the genes, or one hundred ninety-nine genes in step (a). In some embodiments, the expression levels are determined for two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty- seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy- eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty- six, eighty-seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety-two, ninety-three, ninety- four, ninety-five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred one, one hundred two, one hundred three, one hundred four, one hundred five, one hundred six, one hundred seven, one hundred eight, one hundred nine, one hundred ten, one hundred eleven, one hundred twelve, one hundred thirteen, one hundred fourteen, one hundred fifteen, one hundred sixteen, one hundred seventeen, one hundred eighteen, one hundred nineteen, one 105 314164997v1 Attorney Docket No: 243735.000437 hundred twenty, one hundred twenty-one, one hundred twenty-two, one hundred twenty-three, one hundred twenty-four, one hundred twenty-five, one hundred twenty-six, one hundred twenty- seven, one hundred twenty-eight, one hundred twenty-nine, one hundred thirty, one hundred thirty-one, one hundred thirty-two, one hundred thirty-three, one hundred thirty-four, one hundred thirty-five, one hundred thirty-six, one hundred thirty-seven, one hundred thirty-eight, one hundred thirty-nine, one hundred forty, one hundred forty-one, one hundred forty-two, one hundred forty-three, one hundred forty-four, one hundred forty-five, one hundred forty-six, one hundred forty-seven, one hundred forty-eight, one hundred forty-nine, one hundred fifty, one hundred fifty-one, one hundred fifty-two, one hundred fifty-three, one hundred fifty-four, one hundred fifty-five, one hundred fifty-six, one hundred fifty-seven, one hundred fifty-eight, one hundred fifty-nine, one hundred sixty, one hundred sixty-one, one hundred sixty-two, one hundred sixty-three, one hundred sixty-four, one hundred sixty-five, one hundred sixty-six, one hundred sixty-seven, one hundred sixty-eight, one hundred sixty-nine, one hundred seventy, one hundred seventy-one, one hundred seventy-two, one hundred seventy-three, one hundred seventy-four, one hundred seventy-five, one hundred seventy-six, one hundred seventy-seven, one hundred seventy-eight, one hundred seventy-nine, one hundred eighty, one hundred eighty- one, one hundred eighty-two, one hundred eighty-three, one hundred eighty-four, one hundred eighty-five, one hundred eighty-six, one hundred eighty-seven, one hundred eighty-eight, one hundred eighty-nine, one hundred ninety, one hundred ninety-one, one hundred ninety-two, one hundred ninety-three, one hundred ninety-four, one hundred ninety-five, one hundred ninety-six, one hundred ninety-seven, one hundred ninety-eight, or one hundred ninety-nine genes in step (a). In some embodiments, the expression levels are determined for each of the genes in step (a). [00219] In some embodiments, the control is a predetermined value or a value determined from a sample taken from the subject before the xenograft transplantation. [00220] In some embodiments, the control is a predetermined value or a value determined from a sample taken from the xenograft before the xenograft transplantation. [00221] In some embodiments, the corresponding control is a predetermined standard. In some embodiments, the corresponding control is a level or proportion of the expression levels of the one or more genes determined in a sample obtained from the subject at an earlier time point. In some embodiments, the corresponding control is generated in one or more subjects that did not undergo xenograft transplantation. In some embodiments, the corresponding control is generated 106 314164997v1 Attorney Docket No: 243735.000437 in one or more subjects on the day of xenograft transplantation (POD 0). In some embodiments, the corresponding control is generated in one or more subjects before the day of xenograft transplantation (before POD 0). In some embodiments, the corresponding control is generated is generated in the xenograft (e.g., organ, tissue, or cells) to be transplanted on the day of xenograft transplantation (POD 0). In some embodiments, the corresponding control is generated in the xenograft (e.g., organ, tissue, or cells) to be transplanted before the day of xenograft transplantation (before POD 0). [00222] In some embodiments, the method further comprises administering to the subject a treatment that targets plasma cells and/or a complement inhibitory agent, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. [00223] In some embodiments, the method further comprises administering to the subject a treatment that targets T cells when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. [00224] In some embodiments, the method further comprises administering to the subject a treatment that targets plasma cells, a treatment that targets T cells, a complement inhibitory agent, or a combination thereof, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. [00225] Treatments that target plasma cells may include, but are not limited to, proteasome inhibitors (e.g., bortezomib (Velcade) and carfilzomib (Kyprolis)), immunomodulatory drugs (IMiDs) (e.g., lenalidomide (Revlimid) and pomalidomide (Pomalyst)), monoclonal antibodies (e.g., daratumumab (Darzalex) and isatuximab (Sarclisa)), antibody-drug conjugates (e.g., Belantamab mafodotin (Blenrep)), B-cell maturation antigen (BCMA) targeted therapies (e.g., idecabtagene vicleucel (Abecma) and bispecific antibodies like teclistamab (Tecvayli)). [00226] In some embodiments, the treatment that targets plasma cells is plasmapheresis. Plasmapheresis (i.e., plasma exchange) may be used to removes plasma from the blood, which can help reduce the levels of certain antibodies and other proteins circulating in the bloodstream. [00227] Treatments that target T cells may comprise CAR T-cell therapy, checkpoint inhibitors (e.g., inhibitors that target PD-1, PD-L1, and/or CTLA-4), T-cell transfer therapy (i.e., adoptive cell therapy), immunosuppressive drugs (e.g., cyclosporine and tacrolimus), vaccines, and bi- specific T-cell engagers (BiTEs). Rabbit anti-thymocyte globulin (rATG) is an immunosuppressive medication that targets T cells. Other treatments to target T cells may 107 314164997v1 Attorney Docket No: 243735.000437 include, but are not limited to, horse anti-thymocyte globulin (hATG), alemtuzumab (Campath), basiliximab (Simulect), daclizumab, and muromonab-CD3 (Orthoclone OKT3). [00228] In some embodiments, the treatment that targets T cells is rabbit anti-thymocyte globulin (rATG). [00229] Without wishing to be limited by theory, the complement system, consisting of approximately 30 proteins, is a crucial component of the immune system's effector mechanisms. It is primarily activated through two pathways: the classical pathway, which typically depends on antibodies, and the alternative pathway, which generally operates independently of antibodies. Activation through either pathway results in the formation of C3 convertase, the central enzymatic complex of the cascade. This complex cleaves serum C3 into C3a and C3b. C3b binds covalently at the activation site, facilitating further C3 convertase generation in an amplification loop. C3b, along with C4b (produced exclusively via the classical pathway), and their breakdown products serve as significant opsonins. They play a role in promoting cell-mediated lysis of target cells by phagocytes and NK cells, as well as in the transport and solubilization of immune complexes. Additionally, C3/C4 activation products and their receptors on various immune cells are vital for modulating the cellular immune response. [00230] C3 convertases also contribute to the formation of C5 convertase, which cleaves C5 into C5a and C5b. C5a possesses strong pro-inflammatory and chemotactic properties, enabling it to recruit and activate immune effector cells. The formation of C5b triggers the terminal complement pathway, leading to the sequential assembly of complement proteins C6, C7, C8, and (C9)n, forming the membrane attack complex (MAC or C5b-9). The presence of MAC in a target cell membrane can cause direct cell lysis or induce cell activation, resulting in the expression and release of various inflammatory modulators. [00231] There are two main categories of membrane complement inhibitors: those that block the complement activation pathway by preventing C3 convertase formation, and those that inhibit the terminal complement pathway by stopping MAC formation. Membrane inhibitors of complement activation include complement receptor 1 (CR1), decay-accelerating factor (DAF or CD55), and membrane cofactor protein (MCP or CD46). These proteins share a structural feature of repeating units, known as short consensus repeats (SCR), each consisting of about 60-70 amino acids, which are common in C3/C4 binding proteins. In rodents, homologues of human complement activation inhibitors have been identified. The rodent protein CR1 is a broadly 108 314164997v1 Attorney Docket No: 243735.000437 distributed inhibitor of complement activation, functioning similarly to both DAF and MCP. In addition, complement activation blocker-2 (CAB-2), a recombinant soluble chimeric protein derived from human DAF and MCP, inhibits C3 and C5 convertases of both classical and alternative pathways. [00232] In some embodiments, the complement inhibitory agent is a C3 inhibitor. [00233] In some embodiments, the method further comprises administering an immunosuppressive agent to the subject when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. [00234] In some embodiments, the treatment may comprise an immunosuppressive agent, including, but not limited to a Janus kinase inhibitor, a corticosteroid, a mTOR inhibitor, a calcineurin inhibitor, an inosine-5′-monophosphate dehydrogenase (IMPDH) inhibitor, a biologic (e.g., infliximab, adalimumab, abatacept, certolizumab, anakinra, etanercept, golimumab, natalizumab, tocilizumab, ustekinumab, rituximab, secukinumab, vedolizumab, ixekizumab), or a monoclonal antibody (e.g., daclizumab, basilivimab). [00235] In some embodiments, the treatment may comprise an anti-inflammatory agent, including, but not limited to a steroidal anti-inflammatory agent or a non-steroidal anti- inflammatory agent (e.g., naproxen, ketorolac, diclofenac, meloxicam, etodolac, esomeprazole, misoprostol, ibuprofen, famotidine, nabumetone, mefenamic acid, indomethacin, piroxicam, sulindac, ketoprofen, flurbiprofen, diflunisal, oxaprozin, nabumetone, tolmetin). In some embodiments, the treatment may comprise an antibacterial or antiviral therapy. [00236] In some embodiments, the method further comprises administering to the subject a treatment for elevated immune activity, inflammation, xenograft damage or dysfunction, or rejection response. In some embodiments, the method further comprises administering to the subject a treatment for the xenograft damage or dysfunction, or rejection response based on the type of xenograft damage or dysfunction, or rejection response. In some embodiments, the method comprises administering a treatment to the subject when elevated immune activity, inflammation, xenograft damage or dysfunction, or rejection response is detected. Detection of elevated immune activity, inflammation, xenograft damage or dysfunction, or rejection response may be in accordance with the methods described herein. 109 314164997v1 Attorney Docket No: 243735.000437 [00237] In some embodiments, the type of xenograft damage or dysfunction, or rejection response described herein is an antibody-mediated rejection, acute cellular rejection (e.g., T-cell mediated rejection), podocyte damage, ischemia damage, and any other cellular damage. [00238] In some embodiments, the method comprises determining cell type(s) of recipient- derived cells present in a graft after the graft has been transplanted in a subject. Cellular origins are associated with different types of tissue, for example, mature B-cells can be associated with blood or bone marrow; naïve B-cells can be associated with blood or bone marrow; biliary epithelial cells can be associated with liver tissue; breast basal cells can be associated with breast tissue; breast luminal cells can be associated with breast tissue; bulk endothelial cells can be associated with blood vessels; bulk epithelial cells can be associated with any epithelia; bulk immune cells can be associated with immune organs; cardiomyocytes can be associated with heart tissue; cardiopulmonary endothelial cells can be associated with heart or lung tissues; colon epithelial cells can be associated with colon tissue; dermal epithelial cells can be associated with skin tissue; granulocytes can be associated with blood or bone marrow; hepatocytes can be associated with liver tissue; keratinocytes can be associated with skin tissue; kidney epithelial cells can be associated with kidney tissue; liver endothelial cells can be associated with liver tissue; liver stromal cells can be associated with liver tissue; liver resident immune cells can be associated with liver tissue; lung epithelial cells can be associated with lung tissue; megakaryocytes can be associated with bone marrow; monocytes and macrophages can be associated with blood; neurons can be associated with neural tissue; natural killer cells can be associated with blood; pancreatic cells can be associated with pancreas tissue; prostate epithelial cells can be associated with prostate tissue; skeletal muscular cells can be associated with skeletal muscle tissue; and mature T-cells can be associated with blood. [00239] In some embodiments, the cell type(s) identified may be indicative of the type of xenograft damage or dysfunction, or rejection response. For example, the method may involve determining the type of xenograft damage or dysfunction, or rejection response is an antibody- mediated rejection when the cell type is determined to be endothelial cells. [00240] In some embodiments, the invention herein provides methods for diagnosis of xenograft or graft rejection. Xenograft or graft rejection encompasses both acute and chronic xenograft or graft rejection. Acute xenograft or graft rejection is the rejection by the immune system of a xenograft or graft transplant recipient when the transplanted xenograft or graft is 110 314164997v1 Attorney Docket No: 243735.000437 immunologically foreign. Acute xenograft or graft rejection is characterized by infiltration of the transplanted xenograft or graft by immune cells of the recipient, which carry out their effector function and work to destroy the transplanted xenograft or graft. Acute xenograft or graft rejection onset is rapid and generally occurs in humans within a few weeks after xenograft or graft transplant surgery. Generally, acute xenograft or graft rejection can be suppressed or inhibited with immunosuppressive agents such as a Janus kinase inhibitor, a corticosteroid, a mTOR inhibitor, a calcineurin inhibitor, an inosine-5′-monophosphate dehydrogenase (IMPDH) inhibitor, a biologic (e.g., infliximab, adalimumab, abatacept, certolizumab, anakinra, etanercept, golimumab, natalizumab, tocilizumab, ustekinumab, rituximab, secukinumab, vedolizumab, ixekizumab), or a monoclonal antibody (e.g., daclizumab, basilivimab), or the like. [00241] Chronic xenograft or graft rejection generally occurs in humans within months to years after transplant, after successful immunosuppression of acute xenograft or graft rejection. Fibrosis is a common factor in chronic xenograft or graft rejection of organ xenograft or graft transplants. Chronic xenograft or graft rejection can typically be described by a range of disorders that are characteristic of the particular organ. For example, in heart transplants or transplants of cardiac tissue (e.g., valve replacements), such disorders may include fibrotic atherosclerosis; in lung transplants, such disorders may include fibroproliferative destruction of the airway (e.g., bronchiolitis obliterans); in liver transplants, such disorders may include disappearing bile duct syndrome; and in kidney transplants, such disorders may include obstructive nephropathy, nephrosclerosis, or tubulointerstitial nephropathy. Chronic xenograft or graft rejection can also be characterized by denervation of the transplanted tissue, ischemic insult, hypertension accompanying immunosuppressive drugs, and hyperlipidemia. [00242] In some embodiments, the invention herein provides methods for diagnosis of xenograft damage or dysfunction. Xenograft damage or dysfunction may include, but is not limited to, viral infection, ischemic injury, reperfusion injury, peri-operative ischemia, hypertension, injuries due to reactive oxygen species, injuries caused by pharmaceutical agents, and physiological stress. [00243] The xenograft transplant may comprise any cell, tissue, or organ from a donor of one species to a recipient of a different species. In some embodiments, the donor may be a non- human primate, cat, cow, dog, goat, horse, pig, rodent, sheep, reptile, or another non-human animal. In some embodiments, the rodent may be a beaver, guinea pig, hamster, mouse, 111 314164997v1 Attorney Docket No: 243735.000437 porcupine, prairie dog, rat, squirrel, or another rodent. In some embodiments, the non-human primate may be an ape or a monkey. In some embodiments, the non-human primate may be an African green monkey, baboon, bonobo, capuchin monkey, chimpanzee, cynomolgus monkey, gorilla, marmoset, orangutan, owl monkey, pig-tailed monkey, rhesus monkey, spider monkey, squirrel monkey, vervet monkey, or another non-human primate. In some embodiments, the donor is a pig. [00244] In some embodiments, the donor is a genetically modified animal. In one embodiment, the donor is a genetically modified pig. [00245] In some embodiments, the xenotransplant recipient may be a human, and the xenotransplant donor may be a pig. In some embodiments, the pig may be a Sus scrofa, Sus scrofa domesticus, Phacochoerus aethiopicus, Potamochoerus porcus, or Babirousa babyrussa species. In some embodiments, the pig may be a Hanford pig, mini- or micro-pig, Yucatan pig, Yucatan micro pig, Sinclair pig, Gottingen pig, Duroc pig, Yorkshire pig, Landrace pig, or a combination, hybrid, or cross-species thereof. [00246] The transplant may be any biological material comprising cells that have DNA and that can be transplanted from a donor to a subject. The cells may be organized as a tissue or portion thereof, an organ or portion thereof, or a population of cells not organized as a tissue or organ. The population of cells may be a population of the same cell type or of different cell types. [00247] In some embodiments, the xenograft transplant may comprise an organ or portion thereof. Examples include, but are not limited to, kidney, blood vessel, liver, heart, pancreas, lung, colon, skin, bone, prostate, and muscle. In one embodiment, the xenograft transplant comprises a kidney. In one embodiment, the xenograft transplant comprises a heart. [00248] In some embodiments, the xenograft transplant comprises a tissue or portion thereof. Examples include, but are not limited to, cardiac tissue, liver tissue, pancreatic tissue, vascular tissue, esophageal tissue, splenic tissue, intestinal tissue, gastric tissue, colon tissue, tracheal tissue, lung tissue, skin tissue, subcutaneous tissue, kidney tissue, hair tissue, connective tissue, muscular tissue, cartilage tissue, skeletal tissue, prostate tissue, bladder tissue, uterine tissue, penile tissue, gonadal tissue, neural tissue, ophthalmologic tissue, corneal tissue, and bone marrow tissue. 112 314164997v1 Attorney Docket No: 243735.000437 [00249] In some embodiments, the xenograft transplant comprises a population of cells. Examples include, but are not limited to, natural killer cells, granulocytes, mature B-cells, naïve B-cells, mature T-cells, macrophages, monocytes, and stem cells. [00250] In some embodiments, the subject has received a kidney, heart, lung, liver, bone marrow, pancreas, or islet cell transplantation from a porcine donor. [00251] In some embodiments, the cell comprises an adipocyte, adrenal cell, antigen presenting cell, aortic endothelial cell, aortic smooth muscle cell, astrocyte, basophil, B cell, bladder cell, blood cell, blood precursor cell, bone cell, bone precursor cell, cardiac muscle cell, cardiac myocyte, cervical cell, chondrocyte, ciliated cell, cumulus cell, columnar epithelial cell, cone cell, dopaminergic cell, egg cell, embryonic stem cell, endothelial cell, endometrial cell, epidermal cell, epithelial cell, erythrocyte, fibroblast cell, fibroblast and fetal fibroblast, follicle cell, germ cell, glial cell, goblet cell, granulosa cell, hair cell, heart cell, hematopoietic cell, hepatocyte, Islets of Langerhans cell, keratinized epithelial cell, keratinocyte, kidney cell, Kupffer cell, leydig cell, liver stellate cell, lung cell, lutein cell, lymphocyte (B and T), macrophage, mammary cell, melanocyte, memory cell, microvascular endothelial cell, monocyte, mononuclear cell, mucous cell, muscle cell, neural cell, neuron, neuronal stem cell, neutrophil, nonkeratinized epithelial cell, ovarian cell, pacemaker cell, pancreatic alpha-1 cell, pancreatic alpha-2 cell, pancreatic beta cell, pancreatic insulin secreting cell, pancreatic islet cell, parathyroid cell, parotid cell, peritubular cell, pituitary cell, plasma cell, platelet, primordial stem cell, prostate cell, red blood cell, retinal cell, rod cell, Schwann cell, sertoli cell, smooth muscle cell, somatic cell, sperm cell, spleen cell, squamous epithelial cell, testicular cell, thyroid cell, T cell, tumor cell, umbilical vein endothelial cell, uterine cell, vaginal epithelial cell, and/or white blood cell. [00252] In some embodiments, the subject has received a cartilage, bone, adipose, pancreatic islet, muscle, vascular tissue, heart valve, retinal tissue, neural tissue, or corneal tissue transplantation from a porcine donor. [00253] In some embodiments, the tissue comprises adipose, areolar, blood, bone, bone marrow, brown adipose, cancellous, cartilage, cartilaginous, cavernous, chondroid, chromaffin, connective tissue, dartoic, elastic, epithelial, epithelium, fatty, fibro-hyaline, fibrous, Gamgee, gelatinous, granulation, gut-associated lymphoid, Haller's vascular, hard hemopoietic, indifferent, interstitial, investing, islet, lymphatic, lymphoid, mesenchymal, mesonephric, 113 314164997v1 Attorney Docket No: 243735.000437 mucous connective, multilocular adipose, muscle, myeloid, nasion soft, nephrogenic, nerve, nodal, osseous, osteogenic, osteoid, periapical, reticular, retiform, rubber, skeletal muscle, smooth muscle, subcutaneous tissue, vascular tissue, heart valve, retinal tissue, neural tissue, and/or corneal tissue. [00254] In some embodiments, the subject has received a heart, liver, kidney, pancreas, lung, thyroid or skin organ transplantation from a porcine donor. [00255] In some embodiments, the organ comprises adrenal glands, anus, bladder, blood, blood vessels, bones, brain, cartilage, ears, esophagus, eye, glands, gums, hair, heart, hypothalamus, intestines, kidneys, large intestine, ligaments, lips, liver, lungs, lymph, lymph nodes, lymph vessels, mammary glands, mouth, nails, nose, ovaries, oviducts, pancreas, penis, pharynx, pituitary, pylorus, rectum, salivary glands, seminal vesicles, skeletal muscles, skin, small intestine, smooth muscles, spinal cord, spleen, stomach, suprarenal capsule, teeth, tendons, testes, thymus gland, thyroid gland, tongue, tonsils, trachea, ureters, urethra, uterus, and/or vagina. [00256] In some embodiments, the expression levels are determined based on RNA expression, protein expression, epigenetic regulation, or a combination thereof. [00257] In some embodiments, the expression levels are determined using RNA sequencing, targeted RNA panel, a quantitative PCR assay, an antibody-based method, an epigenetic assay, a single-cell technology, a spatial transcriptomics technology, or a multiplexed approach combining RNA and protein measurements. [00258] In some embodiments, the antibody-based method is flow cytometry, immunohistochemistry, or enzyme-linked immunosorbent assay (ELISA). [00259] In some embodiments, the single-cell technology is single-cell RNA sequencing (scRNA-seq) or cellular indexing of transcriptomes and epitopes (CITE-seq). [00260] In some embodiments of any of the above-described methods, the expression level may be determined at the protein, mRNA, and/or gene level using any suitable methodology known in the art. [00261] In some embodiments of any of the above-described methods, the expression level is determined by measuring protein expression level. Protein expression level may be determined using any conventional methodology known to those of skill in the art. As a non-limiting example, protein level may be determined using an enzyme-linked immunosorbent assay 114 314164997v1 Attorney Docket No: 243735.000437 (ELISA), a proteomic array, flow cytometry, a radioimmunoassay, a Western blot, an activity assay, and/or a multiplex electrochemiluminescence-based assay. In some embodiments, protein expression level may be determined using approaches comprising, e.g., Biuret methods, UV absorption, Colorimetric dye-based methods, Fluorescent dye-based methods, Turbimetric method, BCA assay, Lowry assay, and/or Bradford assay. [00262] In some embodiments, the expression level is determined by measuring the expression level of nucleic acids. Nucleic acids from samples that may be analyzed include, but are not limited to, DNA (e.g., double-stranded, single-stranded, or single-stranded hairpins), DNA/RNA hybrids, or RNA (e.g., mRNA, miRNA, or hairpins). Examples of genetic analyses that can be performed on nucleic acids include, but are not limited to, sequencing, STR detection, SNP detection, gene expression, and RNA expression analysis. [00263] In some embodiments, the expression level is determined by measuring mRNA expression level. mRNA expression level may be determined using any conventional methodology known to those of skill in the art. As a non-limiting example, mRNA expression level may be determined using, e.g., RNA-seq, real-time polymerase chain reaction (RT-PCR) (e.g., quantitative RT-PCR), Northern blot analysis and/or a Ribonuclease protection assay. [00264] In some embodiments, detection, identification, and/or quantitation of the donor- specific markers or the recipient-specific markers may be performed by mapping one or more nucleic acids (e.g., RNA or DNA) to the species genome to determine whether the nucleic acids come from the transplant donor or the recipient. [00265] In some embodiments, less than 1 pg, 5 pg, 10 pg, 15 pg, 20 pg, 25 pg, 30 pg, 40 pg, 50 pg, 100 pg, 200 pg, 500 pg, 1 ng, 5 ng, 10 ng, 20 ng, 30 ng, 40 ng, 50 ng, 100 ng, 200 ng, 500 ng, 1 ug, 5 ug, 10 ug, 20 ug, 30 ug, 40 ug, 50 ug, 100 ug, 200 ug, 500 ug, or 1 mg of nucleic acids are obtained from the sample for measuring expression level. In some cases, about 1 pg-5 pg, 5 pg-10 pg, 10 pg-100 pg, 100 pg-1 ng, 1 ng-5 ng, 5 ng-10 ng, 10 ng-100 ng, 100 ng-1 ug, 5 ug- 100 ug, or 200 ug-1 mg of nucleic acids are obtained from the sample for measuring expression level. [00266] In some embodiments, the methods described herein can be used to measure expression level for one or more target nucleic acid molecule(s). The methods described herein can analyze at least 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 50,000, 115 314164997v1 Attorney Docket No: 243735.000437 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 2,000,000, 3,000,000, 4,000,000, or 5,000,000 different target nucleic acids. [00267] In some embodiments, the methods described herein can be used to differentiate between target nucleic acids that diverge from a different nucleic acid by 1 nt. In some embodiments, the methods described herein are used to differentiate between target nucleic acids that diverge from a different nucleic acid by 1 nt or at least 1 nt, 2 nt, 3 nt, 4 nt, 5 nt, 6 nt, 7 nt, 8 nt, 9 nt, 10 nt, 15 nt, 20 nt, 25 nt, 30 nt, 35 nt, 40 nt, 45 nt, or 50 nt. [00268] In certain embodiments, additional genes beyond those listed herein may be assayed and may include additional genes whose expression levels can be used to determine elevated immune activity, inflammation, xenograft damage or dysfunction, or rejection response as well as additional genes whose expression levels can be used to evaluate additional xenotransplant characteristics, including, but not limited to: a graft tolerant phenotype in a subject, chronic rejection in blood, immunosuppressive drug toxicity or adverse side effects including drug- induced hypertension, age- or body mass index- associated genes that correlate with renal pathology or differences in recipient age-related graft acceptance, immune tolerance markers in whole blood, and/or genes with immune modulatory roles that may play a role in transplant outcomes. [00269] In some embodiments, the sample is collected from the subject 3 days after the xenograft transplantation. [00270] In some embodiments, the response of the subject to the xenograft or graft transplantation is monitored. The monitoring comprises determining expression levels of one or more genes in a sample obtained from the subject at one or more time points after receiving the xenograft or graft transplantation. In some embodiments, the method further comprises determining expression levels of one or more genes in a sample obtained from the subject at multiple time points after receiving the xenograft or graft transplantation. [00271] The time points may be the same day or one or more days between and including postoperative day (POD) 0 (day of receiving the xenograft transplantation) through POD 150 or greater, such as POD 0, POD 1, POD 2, POD 3, POD 4, POD 5, POD 6, POD 7, POD 8 POD 9, POD 10, POD 11, POD 12, POD 13, POD 14, POD 15, POD 16, POD 17, POD 18, POD 19, POD 20, POD 21, POD 22, POD 23, POD 24, POD 25, POD 26, POD 27, POD 28, POD 29, POD 30, POD 31, POD 32, POD 33, POD 34, POD 35, POD 36, POD 37, POD 38, POD 39, 116 314164997v1 Attorney Docket No: 243735.000437 POD 40, POD 41, POD 42, POD 43, POD 44, POD 45, POD 46, POD 47, POD 48, POD 49, POD 50, POD 51, POD 52, POD 53, POD 54, POD 55, POD 56, POD 57, POD 58, POD 59, POD 60, POD 61, POD 62, POD 63, POD 64, POD 65, POD 66, POD 67, POD 68, POD 69, POD 70, POD 71, POD 72, POD 73, POD 74, POD 75, POD 76, POD 77, POD 78, POD 79, POD 80, POD 81, POD 82, POD 83, POD 84, POD 85, POD 86, POD 87, POD 88, POD 89, POD 90, POD 91, POD 92, POD 93, POD 94, POD 95, POD 96, POD 97, POD 98, POD 99, POD 100, POD 101, POD 102, POD 103, POD 104, POD 105, POD 106, POD 107, POD 108, POD 109, POD 110, POD 111, POD 112, POD 113, POD 114, POD 115, POD 116, POD 117, POD 118, POD 119, POD 120, POD 121, POD 122, POD 123, POD 124, POD 125, POD 126, POD 127, POD 128, POD 129, POD 130, POD 131, POD 132, POD 133, POD 134, POD 135, POD 136, POD 137, POD 138, POD 139, POD 140, POD 141, POD 142,143, POD 144, POD 145, POD 146, POD 147, POD 148, POD 149, or POD 150, or greater. In certain embodiments, the subject may be monitored at time points later than POD 150. [00272] In some embodiments, the samples are collected from the subject at least one time a week. In some embodiments, the samples are collected from the subject at least two times a week. In some embodiments, the samples are collected from the subject at least three times a week. In some embodiments, the samples are collected from the subject at least four times a week. In some embodiments, the samples are collected from the subject at least five times a week. In some embodiments, the samples are collected from the subject at least six times a week. In some embodiments, the samples are collected from the subject at least seven times a week. [00273] In some embodiments, the method for treating the xenograft damage or dysfunction, or rejection response based on the type of xenograft damage or dysfunction, or rejection response comprises administering a treatment for xenograft damage or dysfunction, or rejection response. In some embodiments, the methods described herein may further comprise monitoring the efficacy of the treatment. The monitoring may comprise determining expression levels of one or more genes in a sample obtained from the subject. [00274] In some embodiments, the method comprises obtaining two or more samples from the subject at different time points after the xenograft transplantation and repeating the method for each sample. [00275] In some embodiments, the methods described herein may be used in combination with one or more other assessments for detecting a xenograft rejection in a subject, or monitoring a 117 314164997v1 Attorney Docket No: 243735.000437 xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject. In some embodiment, the other assessment may be the Banff Criteria described in Loupy, A. et al. The Banff 2019 Kidney Meeting Report (I): Updates on and clarification of criteria for T cell– and antibody-mediated rejection. Am. J. Transplant.20, 2318–2331 (2020), which is incorporated herein by reference in its entirety. Table 1. List of human and pig gene signatures and corresponding clinical archetypes and interventions. de , k t ni l ion ion ion 118 314164997v1 Attorney Docket No: 243735.000437 NUGGC, SDC1 ion ion ion ion ion 119 314164997v1 Attorney Docket No: 243735.000437 Interferon Transplant A,B,C POD21- x MX1, STAT1, Plasmapheresis Targeting increasing activation of 45 1.5 IFIT3, IFIT1, and or C3 plasma cells immunosuppression ion ion ion ion ion ion ion 120 314164997v1 Attorney Docket No: 243735.000437 NK and / or TNFRSF9, inhibition and and or C3 T cells MKI67 or rATG and or T cells ion ion ion ion ion ion 121 314164997v1 Attorney Docket No: 243735.000437 THBS1, HIF1A, ion ion ion 122 314164997v1 Attorney Docket No: 243735.000437 VCAM1, MKI67, IFIT1, , , T Table 2. Gene Annotations. Gene Ensembl ID Gene Name Symbol 123 314164997v1 Attorney Docket No: 243735.000437 TNFRSF1B ENSG00000028137 TNF receptor superfamily member 1B CD14 ENSG00000170458 CD14 molecule 124 314164997v1 Attorney Docket No: 243735.000437 CD38 ENSG00000004468 CD38 molecule TAP1 ENSG00000168394 transporter 1, ATP binding cassette subfamily B member 125 314164997v1 Attorney Docket No: 243735.000437 STAB1 ENSG00000010327 stabilin 1 IFIT3 ENSG00000119917 interferon induced protein with tetratricopeptide repeats 3 126 314164997v1 Attorney Docket No: 243735.000437 CXCR2 ENSG00000180871 C-X-C motif chemokine receptor 2 CSF3R ENSG00000119535 colony stimulating factor 3 receptor [00276] Also provided herein are reagents, systems and kits thereof for practicing one or more of the above-described methods. The reagents, systems and kits thereof may vary greatly. Reagents of interest can include reagents designed for use in production of the above-described gene panels. [00277] One type of such reagent can be an array of probe nucleic acids in which the genes of interest described herein can be detected. A variety of different array formats are known in the art, with a wide variety of different probe structures, substrate compositions and attachment technologies (e.g., dot blot arrays, microarrays, etc.). Representative array structures of interest include those described in U.S. Pat. Nos.5,143,854; 5,288,644; 5,324,633; 5,432,049; 5,470,710; 5,492,806; 5,503,980; 5,510,270; 5,525,464; 5,547,839; 5,580,732; 5,661,028; 5,800,992; WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP 373203; the disclosures of which are herein incorporated by reference in their entirety. [00278] In certain embodiments, the arrays include probes for at least 1 of the genes listed in Table 1. In certain embodiments, probes for any combination of genes in Table 1 may be used. In certain embodiments, the number of genes that are from Table 1 that are represented on the array is at least 2, at least 3, at least 4, at least 5, at least 8 or more, including all of the genes listed in 127 314164997v1 Attorney Docket No: 243735.000437 Table 1. In certain embodiments, the number of genes that are from Table 1 that are represented on the array is at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, at least 61, at least 62, at least 63, at least 64, at least 65, at least 66, at least 67, at least 68, at least 69, at least 70, at least 71, at least 72, at least 73, at least 74, at least 75, at least 76, at least 77, at least 78, at least 79, at least 80, at least 81, at least 82, at least 83, at least 84, at least 85, at least 86, at least 87, at least 88, at least 89, at least 90, at least 91, at least 92, at least 93, at least 94, at least 95, at least 96, at least 97, at least 98, at least 99, at least 100, at least 101, at least 102, at least 103, at least 104, at least 105, at least 106, at least 107, at least 108, at least 109, at least 110, at least 111, at least 112, at least 113, at least 114, at least 115, at least 116, at least 117, at least 118, at least 119, at least 120, at least 121, at least 122, at least 123, at least 124, at least 125, at least 126, at least 127, at least 128, at least 129, at least 130, at least 131, at least 132, at least 133, at least 134, at least 135, at least 136, at least 137, at least 138, at least 139, at least 140, at least 141, at least 142, at least 143, at least 144, at least 145, at least 146, at least 147, at least 148, at least 149, at least 150 or more, including all of the genes listed in Table 1. [00279] The arrays may include only those genes that are listed in Table 1 or additional genes that are not listed in Table 1, such as probes for genes whose expression pattern can be used to evaluate additional transplant characteristics, including but not limited to: chronic allograft injury (chronic rejection) in blood; immunosuppressive drug toxicity or adverse side effects including drug-induced hypertension; age or body mass index associated genes that correlate with renal pathology or account for differences in recipient age-related graft acceptance; immune tolerance markers in whole blood; genes with immune modulatory roles that may play a role in transplant outcomes; as well as other array assay function related genes, e.g., for assessing sample quality (3’- to 5’-bias in probe location), sampling error in biopsy-based studies, cell surface markers, and normalizing genes for calibrating hybridization results; and the like. Where the arrays include probes for such additional genes, in certain embodiments the percent of additional genes 128 314164997v1 Attorney Docket No: 243735.000437 that are represented and are not directly or indirectly related to transplantation does not exceed about 50%, and may not exceed about 25%. In certain embodiments where additional genes are included, a great majority of genes in the collection are transplant characterization genes, whereby a great majority is meant to encompass at least about 75%, typically at least about 80%, and sometimes at least about 85%, at least about 90%, at least about 95%, or higher, including embodiments where 100% of the genes in the collection are phenotype-determinative genes. Transplant characterization genes can be genes whose expression can be employed to characterize transplant function in some manner (e.g., presence of rejection, etc.). [00280] Another type of reagent that can be included in the kits described herein is a collection of gene-specific primers that is designed to selectively amplify such genes (e.g., using a PCR- based technique, e.g., real-time RT-PCR). Gene specific primers and methods for using the same are described in U.S. Pat. No.5,994,076, the disclosure of which is herein incorporated by reference. Of particular interest are collections of gene specific primers that have primers for at least 1 of the genes listed in Table 1, and in particular a plurality of these genes (e.g., at least 2, at least 4, at least 8 or more). In certain embodiments, all of genes that are from Table 1 have primers in the collection, The gene specific primer collections may include only those genes that are listed in Table 1, or they may include primers for additional genes that are not listed in Table 1, such as probes for genes whose expression pattern can be used to evaluate additional transplant characteristics, including, but not limited to: chronic allograft injury (chronic rejection) in blood; immunosuppressive drug toxicity or adverse side effects including drug-induced hypertension; age or body mass index associated genes that correlate with renal pathology or account for differences in recipient age-related graft acceptance; immune tolerance markers in whole blood; genes with immune modulatory roles that may play a role in transplant outcomes; as well as other array assay function related genes, e.g., for assessing sample quality (3’- to 5’-bias in probe location), sampling error in biopsy-based studies, cell surface markers, and normalizing genes for calibrating hybridization results; and the like. Where the arrays include probes for such additional genes, in certain embodiments the percent of additional genes that are represented and are not directly or indirectly related to transplantation does not exceed about 50%, and may not exceed about 25%. In certain embodiments where additional genes are included, a great majority of genes in the collection are transplant characterization genes, whereby a great majority is meant to encompass at least about 75%, typically at least about 80%, 129 314164997v1 Attorney Docket No: 243735.000437 and sometimes at least about 85%, at least about 90%, at least about 95% or higher, including embodiments where 100% of the genes in the collection are phenotype-determinative genes. [00281] The systems and kits described herein may include the above-described arrays and/or gene specific primer collections. The systems and kits may further include one or more additional reagents employed in the various methods, such as primers for generating target nucleic acids, dNTPs and/or rNTPs, which may be either premixed or separate, one or more uniquely labeled dNTPs and/or rNTPs (e.g., biotinylated or Cy3 or Cy5 tagged dNTPs), gold or silver particles with different scattering spectra, other post synthesis labeling reagents (e.g., chemically active derivatives of fluorescent dyes), enzymes (e.g., reverse transcriptases, DNA polymerases, RNA polymerases, and the like), various buffer mediums (e.g., hybridization and washing buffers), prefabricated probe arrays, labeled probe purification reagents and components (e.g., spin columns, etc.), signal generation and detection reagents (e.g., streptavidin-alkaline phosphatase conjugate, chemifluorescent or chemiluminescent substrate), and the like. [00282] The systems and kits described herein may also include in some embodiments, a reference or control that can be employed, e.g., by a suitable computing means, to make a xenograft status determination based on an “input” expression profile, e.g., that has been determined with the above described gene signatures. Representative xenograft status determination elements include databases of gene expression profiles, e.g., reference or control profiles, as described herein. [00283] In addition to the above components, the kits may further include instructions for practicing the described methods. These instructions may be present in the kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc. Yet another means would be a computer readable medium, e.g., diskette, CD, etc., on which the information has been recorded. Yet another means that may be present is a website address which may be used via the Internet to access the information at a removed site. [00284] In one aspect, provided herein is a kit comprising: 1) one or more sets of probe nucleic acids useful for detecting two or more genes selected from: 130 314164997v1 Attorney Docket No: 243735.000437 i. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD; ii. human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1; iii. human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY; iv. human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14; v. human genes IRF7, TLR7, TLR9, MYD88, and STAT1; vi. human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3; vii. human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A; viii. human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1; ix. human genes CD3G, THEMIS, CD5, and CD6; x. human genes CD8B, and CD8A; xi. human genes KLRG1, GZMK, CST7, and GZMH; xii. human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7; xiii. human genes CD4, CD40LG, CCR2, CCR6, DPP4; xiv. human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA; xv. human genes ITGAE, CD38, TNFRSF9, and MKI67; xvi. human genes GZMA, PRF1, and FASLG; xvii. human genes TOP2A, TYMS, and MKI67; xviii. human genes GBP1, IRF1, TAP1, WARS1, and IDO1; xix. human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2; xx. human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, CXCR2; xxi. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, 131 314164997v1 Attorney Docket No: 243735.000437 GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2; xxii. human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2; xxiii. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67; xxiv. porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP; xxv. porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A; 132 314164997v1 Attorney Docket No: 243735.000437 xxvi. porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1; xxvii. porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1; xxviii. porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1; or any combination of the genes listed above, and 2) optionally, packaging and/or instructions for using the same. EXAMPLES [00285] The following examples are provided to further describe some of the embodiments disclosed herein. The examples are intended to illustrate, not to limit, the disclosed embodiments. Example 1. PBMC scRNA-seq in two human decedents post-xenotransplant. [00286] 26 longitudinal blood scRNA-seq (Figure 17) time points were integrated (14 for D1 and 12 for D2) to a two-dimensional (2D)-space embedding and Leiden clustering was used to assign peripheral blood mononuclear cell (PBMC) types (Figure 7A), with the dynamic proportion of these cell types shown (Figures 2A-C). Cell-type annotations were confirmed through marker gene expression (Figure 7B). Changes in cell-type proportions were found with a marked increase of T and natural killer (NK) cells in later time points of D1 (Figures 2A-B). A substantial increase in B cell populations was observed in D1 in the early time points (Figure 2C). In D2, there was no major shift in global cell-type distribution. 133 314164997v1 Attorney Docket No: 243735.000437 [00287] Thymoglobulin immunosuppression treatment dynamics of T cell depletion in D1 and D2 were explored. The overall differences in the proportion of T cell and NK cell subtypes (Figure 2B) and across the individual decedent time courses were studied using the specific marker expression for each cell type (Figures 7C-D). A strong blunting of all T cell subtypes to negligible readings was observed in D2, after the 24.5 h post-transplant rabbit anti-thymocyte globulin (rATG) infusion, compared to D1, who did not receive a second rATG dose and had a pronounced increase in CD4+ and CD8+ T cells (Figure 2B). In D1, CD8+ T cells constantly expressed CD69 and interleukin (IL)-7R, whereas they were mostly negative for CCR7, consistent with effector memory T cells (Figure 7E); however, peripheral blood CD4+ T cells show loss of CD69 expression over time, acquiring stronger CCR7 expression, as well as constant expression of IL-7R (Figure 7F). These circulating T cells in D1 are central memory T cells, which circulate to nonlymphoid sites (Martin, et al., 2018; Golubovskaya, et al., 2016). [00288] B cell subtypes were identified across the two xeno time courses using their marker genes (Figures 7G-H) and it was found that the B cell increase in the early time points of D1 mostly corresponds to TCL1+ B cells and, to a lesser degree, CD70+ B cells (Figure 2C). TCL1 is commonly associated with immune tolerance (Brinas, et al., 2021). A more detailed assessment of the cell-subtype proportion changes in D1 highlighted an increase in dividing T cells, FOS+ T cells and regulatory T (Treg) cells in the later time points, in addition to a spike in plasma cells after an initial B cell peak. Furthermore, an early increase of active B cells in D2 was apparent, which decreased toward the latest time points (Figure 7I). [00289] scRNA-seq cell proportion observations in D1 and D2 were confirmed through analysis of bulk RNA-seq of PBMCs from the same time points (Figures 2D-F), with cell-type specific markers (Figures 7J-K). The D1-specific T/NK cell increase at later time points was validated (Figure 2D) and this pattern was confirmed to be specific to CD8+ T and CD4+ T cells, based on increases in their marker genes (Figure 2E). Similarly, the observed increase in B cells was validated (Figure 2F). Flow cytometry was used to validate the changes observed transcriptionally (Figures 2G, 18). The higher prevalence of NK cells in D2 was confirmed, whereas CD4+ and CD8+ T cells increased from 24-66 h only in D1. The spike of B cells and plasma cell prevalence was also confirmed. [00290] To explore PBMC transcriptomic changes beyond proportional changes, differential expression analysis (DEA) were performed systematically; first, at the pseudobulk level for main 134 314164997v1 Attorney Docket No: 243735.000437 scRNA-seq cell types/subtypes and second from the bulk RNA-seq data. scRNA-seq-based differential expression (DE) results in both decedents in the T/NK cells populations are shown (Figure 7L). D1 displays more differentially expressed genes (DEGs) than D2 across cell types and subtypes. A similar pattern can be observed at the bulk RNA-seq DE level, where significantly overexpressed and under-expressed genes are more numerous in D1 than in D2. Immune activation signals from the bulk RNA-seq DEA are clear in D1 with pathways such as neutrophil extracellular trap formation and tumor necrosis factor (TNF) signaling via NF-κB observed, which are not present in D2 (Figure 2H). Here, the findings reveal a pronounced immune response in the blood immune cells of D1 following transplantation, characterized by a rise in T and NK cells and notable transcriptional alterations across various cell types. This response was notably absent in D2. Example 2. Integrative multi-omics of blood-based datasets. [00291] Temporal DE of PBMCs was analyzed using bulk RNA-seq, along with proteomics, lipidomics, and metabolomics in decedents' plasma after pig heart xenotransplants (Figure 8). Multi-omics data were processed from time points every 6 h from pre-transplant (0 h) until ~66 h in xenograft D1 and D2, with no significant batch effects evident using principal-component analysis (PCA) analysis for each of the single -omic procedures. [00292] Using longitudinal analysis and fuzzy c-means clustering, the multi-omics datasets were integrated and normalized to identify signatures related to post-xenotransplant time and clinical features. Post-xenotransplant analyte changes in RNA-seq, proteins, lipids, cytokines and metabolites increased from 42 h, especially in D1 (Figure 3). The changes track consistently with the following three biomarkers shown across all time points: liver function markers; alanine transaminase (ALT) and aspartate transferase (AST); and INR (international normalized ratio), a metric of clotting time. This cluster was highly enriched for: metabolic processes, lipid metabolism and immune response (antigen presentation and processing q < 0.01). The dynamics of this cluster align well with clinical findings including systolic and diastolic blood pressure as well as troponin levels, and in particular with IL-6, IL-8, IL-10 and IL-13 (Figures 3D, 9A) and cardiac output metrics (Figure 9B). Thus, multi-omics integration presents a clear increase of markers related to immune response and metabolic shifts, specific to D1 and absent from D2, strengthening and validating the immune cell responses that were described above. 135 314164997v1 Attorney Docket No: 243735.000437 Example 3. snRNA-seq of pig xenograft collected from D1 at day 3. [00293] To further explore the transcriptomic signal associated with heart xenotransplant, tissue-specific snRNA-seq was performed. Pig and human nuclei were computationally identified (Figure 19) and the data were of high quality (Figure 20). It was observed that the human nuclei expressed macrophage, T cell, B cell, and NK marker genes (Figure 10A). In all samples, the human nuclei were immune cells from these categories, demonstrating that diverse human immune cells migrated into the pig heart xenograft. To compare composition and transcriptional signals to in vivo pig heart under normal and ischemia reperfusion injury (IRI) conditions, the pig xenograft snRNA-seq data were integrated with a published pig heart snRNA- seq dataset (Andrijevic, et al., 2022), which includes 15 samples from five different conditions: ischemia at 0, 1, and 7 h; extracorporeal membrane oxygenation (ECMO); and OrganEx reperfusion. Integration with this dataset (Figure 4A) enabled use of the non-ischemic samples as a transcriptional baseline and the ability to compare signals arising from ischemic and reperfusion conditions. The main cardiac cell types were observed, including cardiomyocytes (CMs), vascular endothelial cells (VECs), fibroblasts (FBs), immune cells, smooth muscle cells (SMCs), pericytes (PERs), and Schwann cells for the mapped pig cells (Figures 4A-B, 10B-C, 21). Overall, a higher proportion of FB and lower proportion of VEC nuclei were observed in xenograft samples compared to the public pig heart snRNA-seq dataset, potentially due to differences in biopsy location (Figure 4B). In addition, all xenograft samples contain both human macrophages and NK/T cells (Figures 4C-D, 10D); however, the total number of NK and T cells was higher in D1 (Figure 4D) and CD4+ and CD8+ T cells markers were only expressed in D1 (Figure 10E). The human NK/T cell population in the explanted heart xenograft for D1 shows strong expression of IL-7R and CCR7 with weak expression of CD69, suggestive of the blood circulating central memory T cell origin (Martin, et al., 2018; Sathaliyawala, et al., 2013) (Figure 10F). In contrast, the NK T cell human nuclei in D2 are CD69+/CCR7- cells with strong expression of CX3CR1, STAT4 and ID2, a population of effector memory T cells with known strong cytotoxic activity (Martin, et al., 2018) (Figure 10F). [00294] Pseudobulk level DEA with DESeq2 (Love, et al., 2014) was used to compare each ischemic/reperfusion condition, and each decedent to the non-ischemic baseline. Pseudobulk level analysis allowed inference of statistical significance based on the number of samples. It 136 314164997v1 Attorney Docket No: 243735.000437 was observed that the xenografts (in D1 more than D2) had the highest number of significant DEGs compared to conditions from the public dataset (Andrijevic, et al., 2022), both for overexpressed and under-expressed genes (Figure 4E). Enrichment analysis of these results was performed for the two most abundant cell types in the xenograft samples, CMs and FBs, in addition to further subclustering analysis (Figures 23-24). [00295] In CMs, DEA revealed signals specifically associated with D1, such as Myc targets and NIK to non-canonical NF-κB signaling pathways (Figures 4F-G), whereas some of the reperfusion signals (ECMO) were shared between D1 and not D2, including VEGFA and IL signaling (Figures 22A-C). IL signaling was driven by overexpression of FOS, CD80, IL15, RIPK2, HMOX1, IL1RAP, TIMP1, and IL25 in both decedents, whereas IL17RB, CCL2, IL4R, STAT3, and HIF1A were only significantly overexpressed in D1 (Figure 10G). Non-canonical NF-κB enrichment was driven by proteasome-associated genes, specifically overexpressed in D1 (Figure 10H). Other pathways of interest, such as MTOR, EMT, VEGFA, NOTCH and TNF are detailed (Figure 22G). [00296] In FBs, the most enriched pathways in D1 were also enriched in D2 (Figure 4G) and vice versa (Figures 22D-F); these mostly constituted replication and cell division-related signals, in line with the observed higher percentage of FBs compared to the reference (Figure 4B). The genes that were generating this DE signal were identified (Figure 10I). Further subclustering analysis of FBs (Figures 4H, 23) confirms their prevalent division in the xenograft, as subcluster FB-6, enriched for expression of genes associated with cell division (high level of S and G2M cell cycle scores, Figure 4I), was more prevalent in the xenograft for both decedents (Figure 4J). In addition, a deeper inflammatory response was observed in D1 compared to D2, as shown by higher proportion of subcluster FB-4, which is also prevalent in the reperfusion condition (Figures 4J-K). Subclustering analysis of CM also identifies a population more prevalent in D1 and reperfusion condition (Figure 24). Finally, global cell-cell communication analysis (Figures 4L, 25) performed on both xenograft and the publicly available pig heart dataset shows an increase of interactions between FBs and CMs in xenograft compared to other conditions. Example 4. Spatial transcriptomics of D1 pig xenograft at day 3. [00297] For spatial transcriptomics, the same fresh-frozen optimal cutting temperature (OCT) tissue section as the snRNA-seq samples was used. A standard clustering of the Visium data 137 314164997v1 Attorney Docket No: 243735.000437 (selecting only counts originating from pig genes) was performed and ten broad specific groups of cells were observed (Figures 5A, 11A). Clusters 5 and 7, almost exclusive in D1 and cluster 8 in D2, are specific clusters of interest, and enriched for vascular markers. Clusters 7 and 8 both harbor arteries/arterioles, with arterial PERs. Overlap of arteries with these clusters was confirmed from the associated histological slides (Figures 5B-C). In addition, deconvolution with Cell2location and DestVi were used to further annotate these spatial transcriptomics clusters with their respective cell-type composition (Figures 5D-E, 11B-D, 26-28). Thus, the enrichment in PERs of cluster 7 and 8, and to a lesser extent cluster 5, were confirmed. Cluster 7 also concentrates a high level of FBs and macrophages. Cluster 7 is exclusive to D1 (observed mostly in the left ventricle, Figure 11E). Damage-associated proteins (DAMPs), IL-33 and its ligand IL1RL1 (Roh, et al., 2018) were also enriched in clusters 7 and 8 (Figures 5F, 11F-G), and were more prevalent in D1 (Figure 5G). In addition, other DAMPs (Roh, et al., 2018) were found to be in the top 60 DEGs for cluster 7 or 8, including BGN, DCN and S100A2 (Figures 5F, 29). The IL-33–IL1RL1 axis is of high interest here, with observed interaction with SMCs, FBs, and lymphocytes (all enriched in cluster 7, and in the surrounding clusters), and is confirmed in the snRNA-seq data. Cell-cell communication reveals that this axis is activated exclusively in the heart xenograft, especially in D1-LV (Figures 5H-I, 11E). Specifically, these interactions involve cross talk between lymphocytes, SMCs, and FBs in D1-LV. [00298] Hematoxylin and eosin (H&E) stains in D1 (Figures 5J, 12A-D) show patchy contraction band necrosis with interstitial edema and endothelial cell swelling in the small interstitial capillary (Figures 5J, 12A). Also in D1, an intramuscular artery shows endothelial swelling and detachment with few inflammatory cells and perivascular edema (Figure 12B). Electron microscopy (EM) in D1 (Figures 5K, 12E-J) shows endothelial cell swelling resulting in the narrowing of the capillary lumen and sarcolemmal disruption, a feature of irreversible myocyte injury, in concordance with the cytoplasmic staining of C4d. Furthermore, widened disorganized Z-lines and accumulation of mitochondria are present, confirming degenerative changes in CMs. Swelling of endothelial cells, though to a lesser extent, is also seen in D2, both in the intramyocardial artery and the capillaries (Figure 5K). There seem to be specific patterns in the vascular structures (Figures 5B-C), reflected in the damage signal observed on the overlapping transcriptome, which may be indicative of the endothelial changes observed in the H&E stains and EM. 138 314164997v1 Attorney Docket No: 243735.000437 [00299] Many known IRI/cardiomyopathy markers such as the IL33–IL1RL1 axis, IL17B, CCDC3, CCN3 and MYL4 are expressed more in D1 (Figure 6G). Moreover, within the vascular-associated clusters (7 and 8), hypoxia response associated genes such as HIF1A, THBS1 and ENO1 display stronger expression in D1 (Figure 13A). Cluster marker enrichment analysis reveals that these vascular clusters are enriched for epithelial–mesenchymal transition and extracellular matrix (ECM) organization (Figure 13B). Moreover, sample-based DE Gene Set Enrichment Analysis (GSEA) analysis on overall Visium data shows upregulation of YAP/TAZ mechanoregulation, inflammation, chemokine and cytokine, RAGE, IL-1 signaling and other immune/ inflammatory/vascular response pathways (Figure 13C). Here, spatial transcriptomics and histology provides evidence for a vascular damage and vascular remodeling process in xenografts, in addition to hypoxic signals. This is observed across both decedents but much more pronounced in D1 and its left ventricle, where they are also associated with mechanoregulation and immune-related signals. Example 5. Validating observed changes in vascular tissue remodeling. [00300] The snRNA-seq data were revisited to identify signals associated with vascular cell types (Figures 5L-M, 13D). The observed hypoxia signal was more prevalent in D1, with HIF1A levels surpassing those of ischemic and reperfusion controls at 0, 1, and 7 h and ECMO, OrganEx in CMs, MPs, and FBs. While hypoxia genes display a gradual increase from non- ischemic samples to 1 h, 7 h, and reperfusion, IL17B overexpression is specific to the pig heart xenograft samples (CM and L2). Similarly, IL33 and IL1RL1 levels did not change in the ischemic/reperfusion conditions, but displayed strong expression in the heart xenograft samples, particularly in D1, in CMs, DIVs, FBs, and especially VECs (Figure 5L). On the other hand, IL1R1 (receptor to DAMP IL1A) is increased both in reperfusion conditions and in the xenograft samples in FBs, MPs, and VECs, and sometimes not significantly for D2 but for D1. [00301] A previous study exploring overall transcriptional changes accompanying pig-to- primate transplants, describes genes associated with perioperative cardiac xenograft dysfunction (PCXD) (Byrne, et al., 2011). Mapping these genes back to the spatial transcriptomics data and the snRNA data reveals specific trends: several of the genes described as overrepresented in PCXD samples, such as CXCL14 and PHLDA2, were more prominently expressed in the xenograft and specifically in D1 (Figures 6A, 13F-J). 139 314164997v1 Attorney Docket No: 243735.000437 [00302] Exploring DE patterns arising from vascular-associated cell types confirms the transcriptional specificity of D1 (Figures 6B, 14A-B). Pseudobulk-level DE of VECs (Figure 6B) and PERs (Figure 14A) revealed remodeling (integrins in angiogenesis) and division (E2F targets) pathways specifically for D1. Signals such as epithelial–mesenchymal transition in VECs and collagen fibril organization in PERs are shared among both decedents. In SMCs (Figure 14B), the signal was analyzed at the nuclei level, with a nuclear-level Wilcoxon DE analysis, and it was found that a strong and specific signal arises from the left ventricle of D1, with epithelial–mesenchymal transition and ECM organization. In pig-to-primate xenotransplantation, the strongest activated pathway specific to PCXD is also ECM receptor interaction (Byrne, et al., 2011). Decomposition of the enrichment signal allows better understanding of the DEGs giving rise to it, and which ones are specific to D1 (Figure 14C). Overall, this suggests broad vascular remodeling, consistent with the endothelial damage observed on EM (Figures 12E-F). In addition, TNF signaling was observed in VECs (Figure 14C), for both D1 and D2 which aligns with the presence of spatially concentrated clusters of inflammatory markers, corresponding to Visium cluster 10 (Figure 26). This cluster is enriched for VECs and lymphocytes in the deconvolution results (Figures 11B, 12D). Then, the VEC transcriptional signature was further dissected through subclustering (Figures 6C, 30), identifying arterial populations (VEC-8) and potentially tip cells (VEC-2) (Linna-Kuosmanen, et al., 2022) (Figure 6D). The analysis showed that VEC inflammation is prevalent in subclusters VEC-4 and VEC-9, which are enriched in the reperfusion samples and D1 (Figures 6E-F). A similar pattern is observed in the PER-SMC population, with PER-SMC-3 (Figures 6G-J, 31). In addition, PER-SMC-6 is a dividing cell population based on its cell cycle score and is specifically present in the xenograft, with a higher prevalence in D1. Example 6. Lymphatic endothelial cells remodeling. [00303] Visium cluster 9 is enriched in lymphatic endothelial cells (Figure 11B) and is more prevalent in D1 (Figures 11D-E). DEA at the pseudobulk level in that population reveals a transcriptomic signal specific to D1, mostly related to cell division and mitosis (Figure 15A), whereas subclustering analysis of that population (Figures 15B-C, 32) revealed a dividing subcluster LEC-8 specific to the xenograft and more prevalent in D1 (Figures 15D-E). In addition, the LEC-3 population displayed an inflammatory response (TNF signaling) that is 140 314164997v1 Attorney Docket No: 243735.000437 present in both xenograft and reperfusion samples and more prevalent in D1 than D2 (Figures 15D-F). This suggests heart lymphangiogenesis remodeling due to ischemia reperfusion, more evident in D1 (Shimizu, et al., 2018). [00304] A major process observed in the xenograft samples is related to cellular division and DNA replication (in both decedents for FBs and more specific to D1 in CMs, PERs, and VECs). In addition, a population of dividing cells was identified that clustered separately to the other cell types (Figure 4A) and displayed higher abundance (percentage) in D1 samples (Figure 4B). To better characterize this population, subclustering analysis was performed and labeling was performed based on associated cell type (Figure 16). The DIV-PER population was enriched for epithelial–mesenchymal transition and the DIV–VEC-2 population with TGF-β signaling (Figures 16B-C). DIV–PER and DIV–VEC-2 were almost exclusively observed in the xenograft samples and more prominently in D1 (Figure 16D). DIV–VEC-2 population displays markers, such as IL1R1, IL33 and HIF1A (Figure 16E). Additional evidence was identified supporting vascular inflammation with high expression of ICAM1, P-selectin (SELP), and E-selectin (SELE) in VECs and dividing endothelial cells, predominantly in D1 (Figures 16F-I). These results are aligned with the vascular remodeling pattern seen in the xenograft samples' vasculature. This pattern includes associated damage, hypoxia, and inflammation, with a pronounced occurrence in D1. This observed signal differs from what might be solely attributed to hypoxia or reperfusion in the control dataset. [00305] Genetically edited domesticated pigs, Sus scrofa domesticus, hold significant promise as a robust source of donor organs that could solve the current lack of availability of human heart allografts. The first integrative multi-omics profiling of pig-heart-to-human xenotransplantation was performed herein using a dense time course of large-scale single-cell transcriptomics and bulk multi-omics from PBMCs and tissues. Thereby, a detailed dynamic portrait of several biological systems occurring over a 3-day xenotransplant protocol was generated. Histological analysis (using H&E and EM) combined with spatial transcriptomics were used to validate these findings and assess the underlying biological mechanisms. [00306] The initial clinical assessment of why D1 had a poor clinical trajectory from ~36-42 h onwards was that the pig heart xenograft was undersized, resulting in an insufficient preserved ejection fraction from the xenograft left ventricle (Moazami, et al., 2023). Selection of donor heart size in this study was limited by the availability of suitably sized 10-gene-edited pigs at the 141 314164997v1 Attorney Docket No: 243735.000437 time of D1 brain death. The clinical assumption was that the relatively smaller xenograft heart for D1’s size may have catalyzed systemic hypo-perfusion and subsequent ischemic injury. In addition, this size mismatch necessitated placement of bovine pericardial patches to both the aorta and anterior pulmonary artery to bridge size discrepancies between the xenograft heart and the decedent’s vessels. This increased warm ischemia time may have initiated complement activation and worsened hypo-perfusion of the smaller xenograft heart. [00307] Overall, the multi-omics profiling shows a markedly different trajectory between the two pig-heart-to-human decedent xenotransplant procedures, which mirrors their differing clinical trajectories. D1 showed a dramatic increase of several pathways in the blood and tissue, enriched for metabolic processes (glycolysis and pyruvate metabolism), lipid metabolism (peroxisome and fatty acid degradation), liver injury as well as immune response (antigen presentation and processing) and necroptosis. These findings mirrored the clinical course of the two procedures (Moazami, et al., 2023) with the trajectory of multiple clinical biomarkers for liver dysfunction (INR, ALT, and AST) and represented the onset of organ failure consistent with the detection of necroptosis pathway activation. The significant upregulation of glycolysis- related pathways may be due to a transition to an acute hypoxic state associated with impaired oxygen perfusion from the distressed xenograft and vasopressor use. Cell-subtype proportion changes in D1 showed a massive increase of dividing T cells, FOS+ T cells, Treg cells and NK cells comprising over 22% of all cells in the blood at the final time point. Notable in D2 is the lack of molecular signatures for immune activation or tissue injury. Among the responses occurring in D1, a rapid remodeling of both the tissue immune microenvironment (using spatial RNA-seq and snRNA-seq) and in the circulating immune response (using scRNA-seq) was observed, with a rapid and robust induction of B cells that quickly tapered off. This was followed by plasma cell activation and a robust T cell-mediated response. D2 showed a stable, higher level of B cells but a lower level of T cells, with a marked decrease after the second thymoglobulin injection. While several of the molecular pathway signatures from tissue (snRNA-seq and spatial transcriptomics) are consistent with molecular AbMR (Giarraputo, et al., 2023), conventional histopathology did not show a manifestation of these early molecular events. Furthermore, positive cytoplasmic staining for C4d in CMs confirmed myocyte injury, indicative of hypoxia and vascular injury (Moazami, et al., 2023). This vascular damage is consistent with the tissue spatial transcriptomic findings and snRNA-seq analysis, pointing to hypoxia and damage- 142 314164997v1 Attorney Docket No: 243735.000437 associated patterns in arteries specific to the xenograft samples and more prevalent in D1. Many of the upregulated hypoxia and ischemic genes associated with D1 mirrors reperfusion signals in the IRI model (ECMO and OrganEx) when analyzed with known (Andrijevic, et al., 2022) datasets, although the strong IL33–IL1RL1 signals are unique to the pig heart xenograft datasets herein and more prominent in D1. The IL33–IL1RL1 axis has been linked with cardiomyopathy (Florens, et al., 2023). Other relevant molecular patterns associated with vascular damage include IL17B, which is linked with IRI in the kidney and heart (Mehrotra, et al., 2017; Baban, et al., 2014), and with CCDC3, CCN3 and MYL4, which have been linked with heart ischemia (Azad, et al., 2014; Flinn, et al., 2023; Banaszkiewicz, et al., 2021). General patterns of tissue remodeling, seen in FBs and vascular cells, were absent from the reference dataset-associated hypoxic and reperfusion signals. This suggests a response specific to the transplantation. [00308] PCXD occurs when cardiac xenografts show significant dysfunction in the first 24-48 h after transplantation in the absence of rejection (Byrne, et al., 2012). PCXD includes xenograft deterioration from both poor preservation protection resulting in ischemia reperfusion and immune-mediated injury, most often from non-^-Gal antibodies, and is observed in 40-60% of pig-to-nonhuman primate cases (Byrne, et al., 2012). Further dissection of the pig xenograft transcriptome data herein supports PCXD occurrence in D1. Genes previously described (Byrne, et al., 2011) as linked with PCXD are notably overexpressed in D1 with the most-specific D1 xenograft tissue expression pattern, identified in vascular SMCs, being directly related to ECM interactions. Strikingly, ECM interaction is the top gene set associated with PCXD in pig-to- primate heart xenografts. Moreover, dissection of human lymphoid cells found in the xenograft revealed a presence of CD4+ and CD8+ T cells, exclusively in D1, suggesting that the increase of T cells observed in the blood led to migration of T cells into the xenograft, which could act as a mediator of the observed IRI and PCXD signals. Furthermore, the presence of CD8+ T cells in the xenograft early on after transplant leads to granulocytic infiltration and necrosis. Increased cold ischemic time results in a more pronounced presence of macrophage, neutrophil and memory T cells (Koritzinsky, et al., 2021). The therapeutic T cell depletion predominantly decreases naïve T and Treg cells, with essentially no impact on effector memory T cells (Pearl, et al., 2005). This selective depletion of naïve T cells can result in homeostatic expansion of memory CD8+ T cells (White, et al., 2017). Although only described in mouse models to date (Koritzinsky, et al., 2021; Nakamura, et al., 2019; El-Sawy, et al., 2004), the more prolonged 143 314164997v1 Attorney Docket No: 243735.000437 ischemia and lack of the second rATG dose could be potential causes for such extensive molecular changes in D1 and distinct cell-type trajectories and dynamic composition of D1 over the course of the 3-day study. It is well established in the xenotransplantation field that co- stimulatory blockade drugs are essential for the successful protection of xenografts in primates (Mohiuddin, et al., 2016), and will be essential for long-term immune responses in the pig-to- human model. [00309] While these data represent a new window into post-xenotransplantation molecular dynamics, only two xenotransplants were performed and characterized and only over a 3-day period. These 3-day timeframes were allowable as this is the typical timeframe that a recently deceased donor candidate with acceptable organs for transplantation is maintained while allocation and procurement proceed. Extending the timeframe of these pig-to-human decedent xenotransplants to >1 month would allow prolonged studies of immune responses. A meta- analysis of 56 brain-dead humans conducted in the late 1990s reported that 50% of individuals were maintained for >1 month, 33% for >2 months, and 7% for >1 year (Shewmon, et al., 1998). [00310] Here, a wealth of multi-omics data is presented that shows that the rapid decline of D1 was the result of an altered immune microenvironment and tissue injury, consistent with PCXD. There was likely interaction with underlying factors including the combination(s) of the size mismatch, longer ischemic time, use of bovine pericardial patches, the underlying history of heart failure in the individual, and lack of a second rATG dose. Dense longitudinal sampling was necessary to clearly delineate the trajectories of many of these analytes and showcases the value of using detailed integrative molecular monitoring on all future xenotransplantation cases, particularly in the early time points. This is the first reported case of PCXD in the pig heart- xenograft-to-human model and the importance of mitigating this phenotype using perfusion preservation and other approaches is clearly warranted. Example 7. Single-cell transcriptomic landscape of pig-to-human kidney xenotransplantation. [00311] To characterize pig-to-human xenotransplantation-associated cellular dynamics in the xenografts, single-cell RNA sequencing (scRNA-seq) was performed on the xenografts and their contralateral (untransplanted) kidneys from two cases of xenotransplantation using α-Gal-KO kidneys (Montgomery, Stern, et al., 2022). Single cell samples were collected from the xenograft 144 314164997v1 Attorney Docket No: 243735.000437 and its contralateral untransplanted kidney in parallel at the end point of each experiment (54h, Figure 33A). After quality filtering, 23,868 cells were obtained in total, including 7,080 cells from the first transplanted xenograft (and 2,819 cells from the matched control kidney), and 5,999 cells from the second transplanted xenograft (and 7,090 cells from the matched control kidney) (Figure 38). Integrative analyses of scRNA-seq data of the four samples identified 16 unsupervised cell clusters based on their transcriptomic profiles (Figure 33B), with a balanced representation of cells in the samples of both xenotransplantation cases (Figure 33C). These cell clusters represent the major cell types previously identified in mammalian kidneys, including proximal tubule cells (PTCs), thick ascending limb of the Loop of Henle (TAL), distal tubule cells (DTCs), intercalated cells (ICs), endothelial cells (ECs), and immune cells (Figures 33D, 39). Podocytes were not detected as a separate cluster of cells due to their sparsity among kidney cells, though a small population of putative podocytes was identified in the data (Figures 39E-F) (Malone, et al., 2020; Andrijevic, et al., 2022). Example 8. Human immune cells infiltrate porcine xenografts. [00312] To test whether human immune cells had infiltrated into the xenograft, scRNA-seq raw sequencing reads were mapped to both human and porcine reference genomes to compare their cell-specific mapping efficiency. In principle, human cells should detect more mappable reads when mapped to the human reference genome due to sequence specificity, and vice versa. Using this approach, 211 human cells were identified in the two xenografts (146 cells in xenograft 1 and 65 cells in xenograft 2, Figure 34A). As expected, no human cells were detected in the two untransplanted control kidneys (Figure 34A). The vast majority (199/211) of the detected human cells correspond to macrophage (LYZ+ and CD163+) and NK cells (NKG7+ and GNLY+) (Figures 34B, 40A-C), while no evidence of human neutrophils was detected. Coclustering with the human infiltrated immune cells are several porcine immune cell types, including macrophages, natural killer T (NKT) cells, and T cells (Figure 34B). No other abundant human blood cell types, such as T cells or erythrocytes, were detected among these human cells, indicating that they are unlikely to come from contamination of circulating human blood cells after reperfusion. [00313] To determine the time of human immune cell infiltration into the xenografts, a longitudinal bulk RNA-seq of core biopsy samples from the second xenograft was performed at 145 314164997v1 Attorney Docket No: 243735.000437 times 0, 12, 24, and 48 hours post-xenotransplantation (pXTx) (Tables 3-4). It was sought to detect human transcripts from human infiltrated macrophages and NK cells by comparing raw reads of bulk RNA sequencing data mapped to the human reference genome versus those that mapped to the porcine reference genome. It was found that human transcripts of both NK cell marker genes (e.g., NKG7, GNLY, KLRD1) and macrophage marker genes (e.g., LYZ, CD163, TYROBP) substantially increase at 12 hours pXTx (Figure 34C). These results suggest that human immune cell infiltration into the xenografts occurred by 12 hours pXTx. Table 3. Normalized gene expression levels mapped to porcine gene (Ssc11). gene Kidney_T0- Kidney_T0- Kidney_T12.count Kidney_T24.count Kidney_T48.count 1.counts.rev.txt 2.counts.rev.txt s.rev.txt s.rev.txt s.rev.txt 146 314164997v1 Attorney Docket No: 243735.000437 TNFSF8 38.77951009 19.88350659 65.91874266 103.7708405 204.2983855 USP1 3708.829256 4037.594556 6687.189205 5521.140873 6415.351169 hp_ortho_genes time_0h1 time_0h2 time_12h time_24h time_28h ANKRD22 0.05882353 0.1 0.73431734 0.9039548 3.24719101 147 314164997v1 Attorney Docket No: 243735.000437 [00314] Studying scRNA-seq data collected at 54 hours pXTx, significantly increased expression of interferon-gamma signaling was found in the xenografts compared to their untransplanted control kidneys (Figure 34D). Specifically, infiltrated human NK cells express interferon-gamma (IFNG, Figure 34D), while the infiltrated human macrophages express interferon-gamma-stimulated chemokines such as CXCL9, CXCL10 and CXCL11 (Figure 34D). These results indicate a potential early signal of impending rejection as seen in human kidney allotransplantation (Halloran, et al., 2018; Magnone, et al., 1995; Hidalgo, et al., 2002). [00315] To identify the temporal- and species-specific activation of interferon-gamma signaling, gene expression dynamics were analyzed in the longitudinal bulk RNA-seq. Strongly increased expression of interferon-gamma signaling was found in the xenografts from 12 hours pXTx, which was maintained throughout the period of the study (Figure 34E). Interestingly, the persistent high chemokine expression levels over time were predominantly consisted of porcine chemokine transcripts 12 hours pXTx and 24 hours pXTx (human/porcine transcript ratio < 1), but transition to be predominantly consisted of human chemokine transcripts at 48 hours pXTx (human/porcine transcript ratio > 1, Figure 34F). Together, these results suggest early activation of host innate immune response, possibly indicating an early rejection signal in both xenografts, and revealed the temporal dynamics of xenograft-recipient immune response. Example 9. Transcriptomic signatures consistent with an antibody-mediated response. [00316] Having detected expression of marker genes corresponding to early rejection signals during xenotransplantation, it was sought to distinguish different types of rejection between antibody-mediated rejection (AbMR) and acute cellular rejection (or T-cell-mediated rejection, TCMR). Though clinical diagnosis of transplantation rejection types requires a more comprehensive histological analysis (Roufosse, et al., 2018), analyzing specific marker gene transcripts can provide early evidence of rejection types in the xenograft (Halloran, et al., 2018). To do so, transcriptomic analyses of a curated list of rejection marker genes were performed across AbMR-selective genes and TCMR-selective genes (Halloran, et al., 2018) (Figure 35). [00317] Endothelial cell activation due to antibody recognition is a key feature in antibody- mediated rejection (Nankivell, et al., 2010; Kenta, et al., 2020). Thus, the AbMR-selective marker gene expressions in the endothelial cells collected from the xenografts were compared to those of the control kidneys. Significantly increased expression of endothelial-specific AbMR 148 314164997v1 Attorney Docket No: 243735.000437 marker genes was found, including CDH5, CDH13, PECAM1, RAMP3, and TM4SF18 (Figure 35A). These results suggest porcine endothelial cell activation in the xenografts, indicating AbMR. Analyzing the longitudinal bulk RNA-seq transcriptome of the second xenograft, it was found that many of the AbMR-selective marker genes also increased during the period of the study (Figure 35C). [00318] Studying TCMR marker genes, it was found that most of these genes fluctuate without significant changes (>2 fold) during the period of the study (Figure 35B). However, expression levels of two macrophage-expressed TCMR-selective marker genes (ANKRD22, SLAMF8) (Halloran, et al., 2018; Venner, et al., 2014; Teng, et al., 2022) significantly increased across the time of the study (Figures 35B, 35D). Example 10. Global xenograft tissue damage. [00319] To fully characterize the cellular changes of porcine kidneys upon exposure to the human immune environment, differentially expressed gene (DEG) analysis was performed on the proximal tubule cell populations between xenografts and control kidneys (Figure 36A). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene set enrichment analyses of the top 1000 xenograft-upregulated genes indicate increased catabolic processes and disease profiles (Figures 40D-E). The top differentially expressed genes in both xenografts are biomarkers of kidney injury and inflammatory response, including profibrotic osteopontin (SPP1) (Khamissi, et al., 2022; Sinha, et al., 2023), phagocytic osteoactivin (GPNMB) (Patel- Chamberlin, et al., 2011; Adler, et al., 2010), and S100A6 (Cheng, et al., 2005; Chen, et al., 2023) (Figure 36A). Kidney injury-related genes were globally increased in the nephron cell types in both xenografts including PTCs, DTCs, TAL cells, and ICs (Figure 36B), indicating signs of holistic tissue damage when xenografts were exposed to the human immune environment. Example 11. Xenograft-specific proliferating cells indicate porcine kidney tissue repair. [00320] Concurrent with detecting global tissue damage in the nephron cells, a unique proliferating cell population was identified among all cell types (Figure 36C). These cells were almost exclusively of porcine origin, and they highly expressed cell cycle-related genes, including Stathmin 1 (STMN1), PCNA Clamp Associated Factor (PCLAF), High Mobility Group 149 314164997v1 Attorney Docket No: 243735.000437 Nucleosomal Binding Domain 2 (HMGN2), and High Mobility Group Box 2 (HMGB2) (Figures 36D-F). Sub-clustering these proliferating cells, two distinct cell groups were identified, with the majority of the cells expressing PTC marker genes, such as SLC34A1 (Figures 36G-I). Stratifying the proliferating proximal tubule cells by their sample of origin revealed that the majority of these populations was found within the two porcine xenografts (Figure 36G and Table 5). In the first case, 2 and 122 proliferating cells were detected from control kidney and xenograft, representing 0.1% and 2.4% of total PTCs (Table 5), respectively. Similarly in the second case, 12 and 322 proliferating cells were detected from the control kidney and xenograft, representing 0.2% and 6.3% of total PTCs (Table 5), respectively. Table 5. Proliferating cell population. Xenotransplantation 1 Xenotransplantation 2 Control 1 Xenograft 1 Control 2 Xenograft 2 d [00321] It was speculated that these proliferating cells were stimulated by xenotransplantation- associated porcine kidney damage. Indeed, compared to xenografts, fewer proliferating SLC34A1-positive cells were observed in the control kidneys (Figure 36H), which also experienced ischemic conditions during prolonged cold storage. Moreover, it was expected to see a temporal order of kidney damage marker gene expression preceding the cell proliferation signals. This hypothesis was tested through longitudinal bulk RNA-seq data, which indeed revealed that the expression of genes indicating kidney injury (SPP1, GPNMB, and S100A6) peaks at 12 hours post-xenotransplantation, while the expression of genes indicating cell proliferating start to increase at 24 hours pXTx and continue to rise through 48 hours pXTx (Figure 36J). 150 314164997v1 Attorney Docket No: 243735.000437 Example 12. Longitudinal scRNA-seq of recipient PBMCs revealed two waves of immune activation. [00322] To understand how the recipient responded to the grafting of porcine kidney across the period of xenotransplantation, longitudinal PBMC samples were collected at pre-transplantation (0 h), and 6, 12, 24, 48, and 53 hours pXTx for scRNA-seq analyses (Figure 33A). scRNA-seq of xenograft recipient’s PBMCs captured all major cell types in human PBMCs (Figures 37A, 41). The proportions of cell types within each PBMC sample collected across time points were also visualized (Figure 37B). [00323] Antigen presentation is a pivotal process to trigger an adaptive immune response. Analyzing expression levels of major histocompatibility complex (MHC) class II genes in antigen presenting cells (monocytes, macrophages, and megakaryocyte progenitor cells), two waves of increased MHC gene expression were found: a first wave at 12 hours pXTx, and a second at 48-53 hours pXTx (Figure 37C). These two waves of increased MHC gene expression are correlated with two phases of immune gene activation at 12 hours pXTx and 48-53 hours pXTx (Figures 37D-E). PBMCs at 12 hours pXTx have upregulated gene sets in most cell types including monocytes, macrophages, T cells, B cells, and NK cells (Figure 37D, Table 6). Macrophages, NK cells, T cells, and megakaryocytes have upregulated gene sets at 48-53 hours pXTx (Figure 37E, Table 7). These genes are enriched in functions related to the activation of predominantly innate immune responses at 12 hours pXTx (Figure 42A) and the early activation of adaptive immune responses at 48 hours pXTx (Figure 42B). Interestingly, these two waves of gene expression are correlated with the trajectory of interferon-gamma expression in NK and NKT cells in the recipient’s PBMCs (Figure 42C). Together, these results from time-resolved scRNA-seq of PBMCs revealed the dynamic immune response of the recipient’s immune system to the porcine xenograft kidney. Table 6. Gene sets enriched at 12 hours pXTx in PBMC single-cell data (Figure 37D). gene 0 6 12 24 48 53 cell_type 14 14 14 14 14 14 14 151 314164997v1 Attorney Docket No: 243735.000437 8 RGS2 0.29078218 0.42341197 0.68191074 0.44580494 0.82708939 0.63444832 Monocytes_CD14 9 BASP1 0.38323097 0.19012052 1.32759791 0.40522206 0.39563357 0.19604234 Monocytes_CD14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 152 314164997v1 Attorney Docket No: 243735.000437 48 GCC2 0.27854224 0.21485906 0.49228086 0.2805998 0.20469517 0.1793463 Monocytes_CD14 49 ARID4A 0.30428591 0.25697176 0.83698215 0.2673073 0.25050231 0.23302472 Monocytes_CD14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 153 314164997v1 Attorney Docket No: 243735.000437 87 REL 0.38444425 0.23107935 1.51718179 0.38550042 0.24518128 0.2676216 NKT1 88 CEMIP2 0.74772399 0.42927097 1.67985289 0.79280507 0.51965374 0.54118365 NKT1 154 314164997v1 Attorney Docket No: 243735.000437 127 YES1 0.15734713 0.11569642 0.65327158 0.23991115 0.04816749 0.16287143 NKT1 128 BAZ1A 0.30976597 0.26066908 0.518858 0.28593782 0.32082904 0.36489381 NKT1 155 314164997v1 Attorney Docket No: 243735.000437 166 ZFAND5 0.29886089 0.23021497 0.55235897 0.28326101 0.29733609 0.21166298 NKT1 167 DHX36 0.36315371 0.38208425 0.98981529 0.36792308 0.3701131 0.40533899 NKT1 156 314164997v1 Attorney Docket No: 243735.000437 205 RHOH 0.37452651 0.26050135 0.72501246 0.36206825 0.35789695 0.20330459 NKT1 206 YPEL5 0.45366009 0.48966893 0.77863775 0.59895212 0.54214873 0.29499735 NKT1 157 314164997v1 Attorney Docket No: 243735.000437 244 CMTM3 0.37344015 0.25324986 0.91234138 0.35701195 0.31861556 0.19931673 Macrophage_1 245 ARRDC31 0.38861775 0.27209779 0.95232473 0.38093691 0.2899568 0.28616186 Macrophage_1 158 314164997v1 Attorney Docket No: 243735.000437 283 KMT2E- 0.21314112 0.19308951 0.27209504 0.24953106 0.24538977 0.13976467 Macrophage_1 AS1 284 ZNF267 0.19678258 0.17106077 0.64701721 0.27736973 0.1932414 0.17653233 Macrophage 1 159 314164997v1 Attorney Docket No: 243735.000437 322 NEAT11 2.50836231 0.93806116 1.84183584 1.18340795 1.32308159 1.10236516 T_CD4 323 SRGN1 0.89807866 0.67134801 2.71777281 1.12627361 0.8731065 0.55692866 T_CD4 160 314164997v1 Attorney Docket No: 243735.000437 361 UBC1 0.95557463 0.66647568 1.98591723 1.03555382 0.92408201 0.72548194 T_CD4 362 EZR1 0.65017752 0.5065859 1.42931984 0.66532366 0.83885399 0.65284267 T_CD4 161 314164997v1 Attorney Docket No: 243735.000437 400 WAKMAR 0.34083214 0.17113335 0.26425127 0.37233764 0.20836051 0.19423234 T_CD4 2 401 CYCS 0.99215354 0.5364851 0.82608016 1.09180977 0.52002209 0.36858883 T CD4 162 314164997v1 Attorney Docket No: 243735.000437 439 IRF2BP2 0.4736422 0.32491764 0.42827523 0.67651291 0.34468502 0.24107563 T_CD4 440 H3F3B2 1.71582599 0.9016738 1.15579493 1.25400231 0.99567038 0.74569048 T_CD4 163 314164997v1 Attorney Docket No: 243735.000437 478 CEBPB 0.66615681 0.43982307 1.01919043 0.59101144 0.60196413 0.27004044 NK 479 SUPV3L1 0.3464585 0.18840157 0.88159527 0.31185491 0.21247728 0.20600343 NK 164 314164997v1 Attorney Docket No: 243735.000437 516 HIPK1 0.34687474 0.26823857 0.82811779 0.51302111 0.27337479 0.27129157 NK 517 COPA 0.50280784 0.32021612 1.15934868 0.38578985 0.28224141 0.32964512 NK 165 314164997v1 Attorney Docket No: 243735.000437 555 TSPYL22 0.55290385 0.41179043 0.93503412 0.76095053 0.15015788 0.12028028 Macrophage_2 556 MAFF1 1.24231313 0.51701262 0.945726 1.46284676 0.49712107 0.0687026 Macrophage_2 166 314164997v1 Attorney Docket No: 243735.000437 594 FAM107B20.28794909 0.26701895 1.04356645 0.32787521 0.42634055 0.23149053 Macrophage_2 595 FTH16 0.80952416 0.68756034 3.17501394 0.91610847 1.0534644 0.74770761 T_CD8 167 314164997v1 Attorney Docket No: 243735.000437 633 CD99 0.37613035 0.35360454 0.50608259 0.3161691 0.35858087 0.26976651 T_CD8 634 CHD7 0.18561925 0.21733997 0.33335809 0.22321863 0.26818575 0.15799925 T_CD8 168 314164997v1 Attorney Docket No: 243735.000437 673 CLEC2B4 0.57343838 0.3286781 1.28907348 0.49240219 0.32329671 0.3108185 Naive_B 674 YWHAH 0.18940367 0.20678218 0.59538544 0.20117452 0.22102974 0.13734472 Naive_B 169 314164997v1 Attorney Docket No: 243735.000437 713 CD554 0.82832093 0.41482369 1.38207829 0.63985346 0.4943002 0.43060138 Naive_B 714 SIPA1L12 0.27149984 0.16858374 0.9807822 0.31090183 0.19604109 0.18981233 Naive_B 170 314164997v1 Attorney Docket No: 243735.000437 751 PHIP2 0.55486086 0.69055791 1.20386479 0.86663983 0.44643978 0.46023742 T_Reg 752 CLEC2B5 0.61170684 0.32576267 1.08744716 0.52733349 0.50914957 0.29612933 T_Reg _P _P 171 314164997v1 Attorney Docket No: 243735.000437 789 AC245014.30.26376004 0.71637032 1.00669015 0.62061185 0.25585784 0.39273094 Megakaryocyte_P ro 790 BASP12 0.36720105 0.18503491 1.26812457 0.18360053 0.39000857 0.12134847 Megakaryocyte_P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P _P 172 314164997v1 Attorney Docket No: 243735.000437 816 PDE4D4 0.21690985 0.13864976 0.59845106 0.19719077 0.11574241 0.13864976 Megakaryocyte_P ro 817 SNHG83 0.48044603 0.35658104 0.92178599 0.56052037 0.6848594 0.20494026 Megakaryocyte_P _P _P _P _P _P _P _P _P _P _P 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 173 314164997v1 Attorney Docket No: 243735.000437 850 MALAT14 1.1713939 1.20333099 1.8192777 1.37674401 1.15328836 0.80883228 Monocytes_CD16 851 CXCL161 0.51534587 0.25958163 1.53282363 0.64670855 0.41464611 0.12368301 Monocytes_CD16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 174 314164997v1 Attorney Docket No: 243735.000437 888 TFDP11 0.05717354 0.10164185 0.44566042 0.10762078 0.12617609 0.0274433 Monocytes_CD16 889 MED23 0.0576925 0.10256445 0.18934976 0.16289648 0.11140621 0.09230801 Monocytes_CD16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 175 314164997v1 Attorney Docket No: 243735.000437 927 CSF2RA1 0.20425837 0.19971929 0.62848728 0.38448634 0.23947533 0.13889569 Monocytes_CD16 928 KLHL243 0.33149387 0.17679673 0.4759912 0.31199423 0.06096439 0.05303902 Monocytes_CD16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 176 314164997v1 Attorney Docket No: 243735.000437 967 SAV1 0.05558631 0.07905609 0.47889748 0.20926613 0.16867571 0.07115048 Monocytes_CD16 968 KRAS 0.09295561 0.24788164 0.74364491 0.52492582 0.38464392 0.23796637 Monocytes_CD16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 177 314164997v1 Attorney Docket No: 243735.000437 1007 VTI1A 0.0554939 0.17758048 0.36426765 0.20891821 0.13777796 0.08879024 Monocytes_CD16 1008 RAB1A 0.21315085 0.2273609 0.28420113 0.30091884 0.1764007 0.17052068 Monocytes_CD16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 178 314164997v1 Attorney Docket No: 243735.000437 1046 EMP31 0.56877904 0.64103716 1.66477095 0.30097554 0.74161058 0.51848053 Memory_B 1047 ARL4C2 0.25053157 0.27596625 0.37810489 0.34063504 0.39300254 0.26561358 Memory_B 179 314164997v1 Attorney Docket No: 243735.000437 1085 MAP3K22 0.19666197 0.37136169 0.7019021 0.36097822 0.30758051 0.29415714 Memory_B 1086 KLF31 0.18565949 0.17946635 0.66533799 0.18752073 0.23031682 0.235151 Memory_B B B B B B B B B 180 314164997v1 Attorney Docket No: 243735.000437 1121 AREG7 0 0.09833925 1.60733813 1.49400019 1.05291442 0.06640001 Erythrocytes_RB C 1122 CTSS 0.14786964 0.47168544 1.07842859 1.58220516 1.94765442 0.69005833 Erythrocytes RB B B B B B B B B B B B B B B B B B B B B B B B B B 181 314164997v1 Attorney Docket No: 243735.000437 1148 AIF11 0 0.34300267 1.26315225 1.95702081 1.252111 0.45998352 Erythrocytes_RB C 1149 NAMPT2 0.10206644 0.14728575 1.31982465 1.00025111 0.96234071 0.03402215 Erythrocytes RB B B B B B B B B B B B B B B B B B B B B B B Table 7. Gene sets enriched at 48-53 hours pXTx in PBMC single-cell data (Figure 37E). gene 0 6 12 24 48 53 cell_type 182 314164997v1 Attorney Docket No: 243735.000437 2 GZMK 0.741289 0.859094910.240232551.024288221.039373460.60494394 NKT1 3 LTB 0.261477160.520095040.222507010.435150611.271468630.39630665 NKT1 183 314164997v1 Attorney Docket No: 243735.000437 41 SMC1A 0.279508010.295106490.141097790.328711340.552872980.33070863 NKT1 42 EIF5B 0.6701232 0.507669090.509119580.471964890.717989150.46022109 NKT1 184 314164997v1 Attorney Docket No: 243735.000437 80 HIST1H4C 0.705292470.634593670.627429770.703380591.913271060.29516543 T_CD4 81 STMN1 0.139851730.488740440.154938210.380250441.086108110.409776 T_CD4 185 314164997v1 Attorney Docket No: 243735.000437 120 CD52 1.220815050.932771590.487010121.159029742.017876661.11572319 T_CD4 121 UCP2 0.6514048 0.559194370.115659990.364252281.384472680.71326453 T_CD4 186 314164997v1 Attorney Docket No: 243735.000437 159 FKBP5 0.569694070.732575380.626737770.464443931.082772840.90591349 T_CD4 160 B2M 1.097698250.975702960.719852811.106653091.411467381.25112678 T_CD4 187 314164997v1 Attorney Docket No: 243735.000437 199 DOK2 0.401916830.375493870.257018660.389307670.625426680.52639372 T_CD4 200 PARP9 0.151373870.253741430.141380250.175288390.5123307 0.4472774 T_CD4 188 314164997v1 Attorney Docket No: 243735.000437 239 C4orf48 0.246945660.200295620.169475450.232678850.610436130.23374338 T_CD4 240 UBE2F 0.196065170.191421220.084549140.221328470.315692630.28032128 T_CD4 189 314164997v1 Attorney Docket No: 243735.000437 279 FGFBP21 1.086077480.984183750.748098571.065606771.036955391.26680164 NK 280 GZMB1 0.859919460.814594940.711650380.814414621.733084121.33389073 NK 190 314164997v1 Attorney Docket No: 243735.000437 319 LUCAT1 0.334747390.260299410.280257580.292684350.450614090.19545985 Macrophage_2 320 LYZ 1.384795980.697334180.842237961.155733932.061519010.9625657 Macrophage_2 191 314164997v1 Attorney Docket No: 243735.000437 357 FTL 0.742421241.024806970.963258190.7545542 1.515457110.905747 T_Reg 358 IRF13 0.356656790.683238660.974647690.697462171.5384525 0.95042246 T_Reg Pro Pro Pro Pro Pro Pro Pro Pro 192 314164997v1 Attorney Docket No: 243735.000437 395 NFIA 0.659281840.787794510.158348290.216157980.821668840.75537082 Megakaryocyte_Pro 396 PLAC8 0.212351370.3027666 0.203709160.176959470.400631850.29115363 Megakaryocyte_Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro Pro C C C C C 193 314164997v1 Attorney Docket No: 243735.000437 435 KLF31 0.496247641.093001120.605764350.238198861.287408150.97595368 Erythrocytes_RBC 436 S100A122 0 0.341590670.296080951.206177381.933388430.32369167 Erythrocytes_RBC C C C C C C C C C C C C C C C C C C C C [00324] Xenotransplantation of genetically engineered porcine organs has the potential to transform human health (Wolbrom, et al., 2023; Elisseeff, et al., 2021). Although multiple trials have been performed in model organisms including non-human primates, how xenografts respond to the human physiological environment and the reciprocal activation of human immune responses remains largely unknown. Additionally, since it remains unclear how translatable the results of studies in non-human models are to human xenotransplantation, this study represents a unique opportunity to apply powerful scRNA-seq technologies to study xenotransplantation in the human decedent model. In this study, scRNA-seq was performed on two cases of pig-to- human kidney xenotransplantation to fully characterize the cellular dynamics of xenografts and the recipient’s immune response to them. Together with longitudinal bulk RNA-seq on the xenograft and scRNA-seq on the recipient’s PBMCs, the data revealed the time-resolved map of cellular and immune dynamics of xenograft-recipient interactions. 194 314164997v1 Attorney Docket No: 243735.000437 [00325] Although no hyperacute rejection signal was observed through standard histological analyses (Montgomery, Stern, et al., 2022), the results herein provide strong evidence that both kidney xenografts presented early signs of an AbMR phenotype at 54 hour post- xenotransplantation. The evidence obtained in this study includes 1) the infiltration and the activation of human macrophage and NK cells in the xenografts (Figures 34–35); 2) the detection of AbMR-associated marker gene expression through time-resolved RNA-seq of xenograft biopsies (Figure 35); 3) activation of endothelial cells in xenografts (Figure 35); and 4) the holistic xenograft tissue damage signals (Figure 36). Notably, these expression changes were not observed in the contralateral untransplanted kidneys that also experienced ischemia, indicating that the observed evidence reflect xenotransplantation-induced responses but not ischemic injury-induced effects. Reperfusion is associated with its own set of expressed genes, tissue injury and innate immune responses. Because the control tissues did not recapitulate this process, the full ischemia-reperfusion-induced signal cannot be subtracted in these experiments. [00326] The results indicating an early sign of AbMR in the xenografts are consistent with the initial observation that the recipients had low to moderate levels of preformed xenoreactive antibodies in crossmatch tests before xenotransplantation (Montgomery, Stern, et al., 2022). [00327] Immune cell infiltration into transplanted organs may indicate activation of the recipient immune response and predict rejection of the graft (Spahn, et al., 2014; Puga Yung, et al., 2017; Itescu, et al., 1998; Parkes, et al., 2017). Recipient’s macrophage and NK cell infiltration into the transplanted organ is seen in innate immune response but the extent of the response in the kidney xenograft and degree of endothelial injury seems to represent a unique phenotype. This phenomenon has been observed in studies using ex vivo perfusion of pig organ with human blood, pig-to-non-human-primate xenotransplantation, and to a lesser extent, in human allotransplantation (Itescu, et al., 1998; Lin, et al., 1997; Khalfoun, et al., 2000; Ezzelarab, et al., 2009). The time-resolved transcriptomic monitoring of the human transcripts in the second xenograft herein revealed that human immune cell infiltration happened as early as 12 hours pXTx. Notably, the infiltration of human immune cells was not likely to be induced by environmental infection as the donor pigs were exhaustively surveilled for known zoonotic pathogens and the recipients were also tested for human infections that replicate in immunocompromised hosts. 195 314164997v1 Attorney Docket No: 243735.000437 [00328] The longitudinal RNA-seq monitoring of the xenograft revealed a two-phase activation of interferon-gamma signaling and the expression of multiple pro-inflammatory chemokines including CXCL9 and CXCL10. It was found that, at 12-to-24 hours pXTx, these pro- inflammatory chemokines were mainly expressed by porcine immune cells. In contrast, at 48 hours pXTx, these pro-inflammatory chemokines were mainly expressed by the infiltrated human macrophages, indicating the activation of the recipient’s immune response. These results may explain the two waves of immune-activation gene expression observed in the recipient’s PBMCs. The results herein demonstrate that porcine immune cells rapidly activated interferon- gamma signaling during xenotransplantation, and thus might have contributed to the first wave of immune response between xenograft and the recipient. In the future consideration of depleting such porcine tissue-resident immune cells pre-transplantation might reduce activation of the recipient immune response (Sykes, et al., 2022; Kim, et al., 2019; Collins, et al., 2001). [00329] Single-cell RNA-seq of the porcine kidneys identified a SLC34A1-positive proliferating cell population activated in the porcine xenografts in human studies for the first time. Proliferating or cycling cells have been observed in damaged kidney in both humans and rodents (Toback, et al., 1992; Humphreys, et al., 2008; Cochrane, et al., 2005; Wen, et al., 2023), and in some rejection cases of human kidney allotransplantations (Wu, et al., 2018). Further studies revealed that the activation of proliferative cells occurring in damaged kidneys may represent a cellular hallmark of kidney tissue repair to potentially restore nephron structure and function (Humphreys, et al., 2008; Benigni, et al., 2010; Witzgall, et al., 1994). Studies in rodents found that acute ischemic damage can activate the proliferation program of proximal tubule cells and may induce maladaptive repair of damaged kidney (Gerhardt, et al., 2021; Ferenbach, et al., 2015). The results herein demonstrated that the proliferative cells were dramatically more enriched in xenografts than in the untransplanted control kidneys. Moreover, time-resolved RNA-seq analysis of the xenograft biopsies revealed the sequential onset of expression of kidney damage genes and cell cycle genes. Together, these results indicate that the rapid activation of SLC34A1-positive proliferating cells was induced upon xenotransplantation. A longer-duration study could reveal whether the rapid activation of cell proliferation program would help restore tissue homeostasis or trigger maladaptive repair, which may eventually damage porcine kidney function (Ferenbach, et al., 2015; Franquesa, et al., 2015; Nieuwenhuijs-Moeke, et al., 2020). 196 314164997v1 Attorney Docket No: 243735.000437 [00330] In conclusion, the results herein provide comprehensive insights into the time-resolved and single-cell transcriptomic dynamics of xenograft-recipient interactions in the early stages of xenotransplantation. Beyond that, the results highlighted the power of single-cell and time- resolved transcriptomic approaches for discovering new biological insights such as the rare proliferating cell populations and the temporal regulation between xenograft and the recipient immune system. Longer periods of follow up studies could further characterize how these transcriptomic signals manifest at the cellular and tissue level. The presented discoveries will inform future porcine genetic engineering and xenotransplantation development. Example 13. Description of the experiments: clinical events, treatments, and multi-omics assays performed in the 61-day period following xenotransplantation. [00331] Clinical events, treatments and multi-omics assays performed in the 61-day period following a pig-to-human Gal-KO thymokidney xenotransplant from recipient blood and xenotransplanted tissue are outlined in Figure 44. The relevant clinical phenotypes include: post-operative day (POD) 0-28: IgM glomerular deposition, C3/C3b complement activation, mesangialysis; POD 33: rising creatinine, IgG spike, +C5b9, and diagnosis of AbMR; POD 34- 43: treatment of AbMR with PLEX, steroids and C3 inhibition; POD 44-48: donor specific antibody rebound during treatment pause; POD 49: diagnosis of ongoing AbMR and new cell mediated rejection (CMR); POD 49-58: treatment of AbMR and CMR; POD 61: resolution of AMR/CMR. [00332] Experiments included high-resolution tissue special transcriptomics (ST) using 478 and 5,100 (5.1k) customized mRNA panels, snRNA-seq of tissue, in addition to bulk RNA-seq analysis, totaling up to 10 tissue biopsy timepoints across modalities. Recipient primary peripheral blood mononuclear cells (PBMCs) were characterized by bulk and scRNA-seq, in addition to B-cell Receptor (BCR)/T Cell Repertoire (TCR) sequencing. Furthermore, sera from 63 timepoints were subjected to proteomic analysis (Proteograph). [00333] ST using Xenium (10x Genomics) workflows enabled the integration of histological features, such as glomeruli, with large-scale single cell transcriptomic profiling (Figure 45A). Different pig/human cell partition strategies were utilized across modalities. For the 478-mRNA panel, a cutoff of 0.5 was applied for the fraction of genes originating from the human genome to assign a cell as human (Figures 45B, 49A). The data displayed a bimodal distribution of pig and 197 314164997v1 Attorney Docket No: 243735.000437 human gene expression (Figures 49B-C). For the 5.1k-mRNA panel, clustering was used for species partitioning as the data included the non-transplanted contralateral pig kidney in addition to the decedent’s native human kidney, which was taken at nephrectomy prior to the xenotransplant procedure. This enabled the identification of a human cluster where cells originating from the native human kidney and human cells found in the xenograft grouped together (Figures 49D-E). This cluster was further dissected to identify pig/human doublets (Figure 49F). Finally, the performance of the method herein was estimated by comparing the results across the two ST modalities, which yielded similar results across timepoints (Figures 49G-H). Moreover, in the 5.1k mRNA ST panel dataset, the contralateral pig kidney had no cells assigned to the human group in the partitioning (although 1 cell out of 42,503 in the sample was predicted as a pig/human doublet), indicating very high specificity for the human cell identification process. As ~99% of cells from the decedent’s native kidney clustered as human cells, this also suggested very high sensitivity for human cell detection (Figure 49I). Distribution of human and pig probe expression was consistent with the partitioning results (Figures 45C, 50A). Xenograft biopsy snRNA-seq data was partitioned between pig and human in a similar clustering-based method, and displayed a clear bimodal distribution in the human gene expression fraction (Figures 50B-D). The expression of ST mRNA probes was assessed for human and pig orthologues, and consistent expression patterns were noted across pig and human cells, particularly for immune markers such as CD4, CD3 and CD68 (Figure 58). [00334] To facilitate expert pathologist interpretation of cellular composition and spatial- locations within key renal cortex structures, including glomeruli, tubules, ducts, loops, and podocytes identified using cell-type specific mRNA markers, ST datasets were overlaid onto hematoxylin and eosin (H&E) slide images, generated from the same tissue samples on which the ST was performed (Figures 45D-F). Clustering and dimensionality reduction were used to assign cell types to detected cells across data modalities, including the 478 ST panel (Figures 45G-H) and 5.1k ST panel (Figures 45I-J), tissue snRNA-seq data (Figures 45K-L), and recipient PBMCs scRNA-seq data (Figure 45M). Extensive depiction of the identified cell type, marker genes, and quantitative distribution across timepoints are shown in Figures 59-69. In addition, bulk RNA-seq and flow cytometry were performed on recipient PBMCs to validate changes in cell-type composition observed on scRNA-seq data (Figure 44). Proteomes were generated for all daily timepoints and ‘pig-alone’ and ‘decedent alone’ (pre-transplant) sera 198 314164997v1 Attorney Docket No: 243735.000437 samples using a proteomics profiling approach (Proteograph XT) and analyzed by LC-MS/MS (liquid chromatography, tandem mass spectrometry). Example 14. Recipient plasma, B and NK cell activation in response to the xenotransplantation. [00335] Acute antibody-mediated rejection (AbMR) of the xenograft was confirmed by conventional histology after a for-cause biopsy was performed on POD 33 based on a change in kidney function. From POD 21, human immune cells, consisting predominantly of macrophages, natural killer (NK) cells and plasmacytoid dendritic cells (pDCs) infiltrated the xenograft (Figures 46A-F, 61-62). Patterns in the number of infiltrating human immune cells in the xenograft tissue by ST across time were consistent with the AbMR event (Figures 46A, 44). Less than 2% of human cells were found immediately post-transplantation, but they increased up to more than 5% at POD 21 (Figure 46A), before peaking at POD 33 (at biopsy-confirmed AbMR) where human cells represented >20% of all cells observed in the xenograft biopsy (Figures 46A, 49G-H). The number of human cells then reduced to 3-7% of all cells in the xenograft between POD 45-61 after the successful treatment of AbMR, which consisted of thymoglobulin, steroid, pegcetacoplan (a C3 inhibitor), and multiple plasmapheresis treatments. The 5.1k ST panel analysis described a wide variety of human immune cell types and cell states invading the xenograft tissue, with specific marker gene expression defining each population (Figures 51A-H, 61). [00336] B cells and plasmablast cell counts peaked in the decedent’s blood before POD 7, after which they decreased to lower levels (Figures 46B-C). However, plasmablast levels rose again (to ~0.3% of all PBMCs) at POD 17-25 (Figures 46B-C). Corresponding B cell levels in the tissue peaked later in the time course at POD 45 (Figure 46C). In bulk RNA-seq, plasmablast specific markers showed high expression in the POD 17-25 (Figure 46B). Classical dendritic cells 2 (cDC2) population in the tissue spiked at POD 33 (Figure 51H), while classical dendritic cells 1 and 2 (cDC1 and cDC2) spiked in the blood between POD 17-33 (Figures 51I-J). Human NK cells infiltrated the transplanted tissue, and increased at POD 21, before declining after POD 45 (Figure 46D). Similar patterns were observed in the 478 ST panel (Figure 51K). This NK increase was also evident in the PBMC from scRNA-seq data, peaking at ~30% in POD 10 and POD 21, followed by another spike around POD 33-37, decreasing afterwards to lower levels 199 314164997v1 Attorney Docket No: 243735.000437 (Figure 46D). The decrease after POD 49, especially observed in bulk RNA-seq (Figure 46D), coincides with the rATG (rabbit anti-thymocyte globulin) induction treatment (Figure 44). The distribution patterns of blood NK and B cells were confirmed by flow cytometry (Figure 46E). The pDC population in PBMCs and tissue (both in the 478 and 5.1k panels) followed a similar pattern to that of NK cells, albeit with earlier proliferation in blood and to tissue infiltration (Figures 46F, 51K). Their levels in blood and tissue were also lower immediately post AbMR event (POD 33). Interestingly, pDCs activation markers (Birmachu, et al., 2007; Guiducci, et al., 2008; Di Domizio, et al., 2009) and CXCL9-11 showed highest expression in the pDC-cDC1 population at POD 21 in tissue (Figure 51L), and only partially in the blood (Figure 51M). Some of the additional immune cell populations showed similar distributions in the tissue and blood (Figures 51H-K). Levels of blood B and plasma cell markers were confirmed by bulk RNA-seq expression of immunoglobulin genes (Figure 52A). These data indicate an innate NK and B cell-mediated response in the blood, followed by infiltration of these cells into the xenograft, the number and activity of which peaked at biopsy confirmed AbMR (POD 33), and declined after successful AbMR treatment. [00337] The relevance of B and plasma cells in this setting was confirmed by BCR-seq, which showed matching peaks of clonal diversity at POD 2, POD 19, POD 25, and POD 40 consistent with distribution patterns of NK, dendritic, and plasmablast cells in the blood (Figure 46G). Small (<0.1%), medium (0.1-1%), and large (>1%) clonal expansions were evident at all timepoints, with small- and medium-sized clones making up the majority of unique BCR clones. High IgA/G BCR isotype usage was observed between POD 2 and POD 28, immediately followed by increasing IgM/D isotype usage starting at POD 31 until the end of the time course (Figure 46H). A moderate increase in BCR CDR3 length from 51nt to 54nt was observed (Figure 52B) following POD 15. BCR mutation analysis demonstrated high average rates of somatic hypermutation in IgG (7.9%), IgA (6.4%), IgE (6.1%), IgM (3.9%), and IgD (3.5%) (Figure 52C) with highest mutation rates observed on POD 2 and POD 3 in IgG clones. Clonotype overlap analysis identified high shared clonotype abundance between POD 17-25 (Figure 52D), consistent with top clone tracking analysis identifying extensive polyclonal expansions between POD 17-25, preceded and succeeded by large expansions of individual clones (Figure 52E). These data indicate a rapid re-activation of existing memory B cells and 200 314164997v1 Attorney Docket No: 243735.000437 differentiation into IgA/G plasmablasts, the activity of which peaked at biopsy confirmed AbMR (POD 33) and declined after successful AbMR treatment. Example 15. Macrophage dynamics in the xenograft. [00338] Macrophages constituted the largest human immune cell population within the xenograft across both ST panels (Figures 45G, 45I, 46I, 59C, 61C). Within this compartment, distinct macrophage and monocyte subpopulations were identified, displaying dynamic changes over time. Among these, CXCR1⁺ CXCR2⁺ monocytes were most abundant at POD 0, with reduced levels observed at subsequent time points (Figure 51H). The predominant macrophage population (Monocyte-MP), expressing CD163, CD14, and MRC1, also displayed elevated CXCL12 levels. This subset represented the largest immune cell fraction at most timepoints (excluding POD 0) but was surpassed at POD 33 by a distinct CXCL9⁺ macrophage subset (Figure 51B). This population, defined by high expression of CXCL9, CXCL10, CXCL11, as well as STAT1 and LILRB1 (Figure 51G), became the most abundant human immune cell type within the xenograft at POD 33, comprising >6% of total cells (Figures 46I, 61C). The only other population expressing similar levels of CXCL9-10-11 in the ST data were interferon+ cells and pDCs (Figure 51G). Another distinct subset, inflammatory classical monocytes, characterized by high expression of CD300E, LILRA5, FCN1, and LILRB2, exhibited a marked expansion at POD 33, reaching up to 1.5% of total cells (Figures 51F-H). [00339] Gene enrichment analysis revealed that CXCL9⁺ macrophages were strongly associated with pathways involved in interferon response, neutrophil activation, and cytokine signaling (Figure 46J). Additionally, another immune cell subset spiked at POD 33, which was termed “interferon+” cells due to their prominent expression of interferon-stimulated genes, including MX1, IFIT1, and IFIT2 (Figure 51G). This population exhibited an enrichment of pathways related to type I interferon responses (Figure 46J). Notably, they appeared to be a heterogeneous mixture of macrophages and T cells, as suggested by their co-expression of CD3E, MRC1, and CD163, and appeared to cluster together due to shared immune activation signatures (Figure 61D). [00340] The 478 ST mRNA panel data corroborated the presence of specific macrophage subpopulations exhibiting high CXCL9, CXCL10, and CXCL11 expression (Figures 52F-G). Notably, these genes peaked at POD 33 (Figure 52H), and the CXCL9⁺ macrophage subset was 201 314164997v1 Attorney Docket No: 243735.000437 almost exclusive to this timepoint, where it was the predominant immune population (Figure 52I). Interestingly, snRNA-seq data identified two macrophage populations with transcriptional profiles similar to CXCL9⁺ macrophages and interferon⁺ cells. One population was enriched at POD 10 and POD 14, while the other peaked at POD 33 (Figures 52J-K, 64), albeit at a lower total cell percentage than in ST data (similar to all human cell groups found in the snRNA-seq dataset). Finally, a global analysis of all human immune cells confirmed a pronounced increase in CXCL9, CXCL10, and CXCL11 expression, peaking around POD 21-33 before returning to near-baseline levels, as observed in both the 5.1k ST data (Figure 46K) and the tissue bulk RNA-seq data (Figure 52L). Example 16. Recipient T cells proliferate and activate around the rejection event. [00341] Analysis of PBMC scRNA-seq and ST panel datasets identified distinct T cell subtypes (Figures 47A-H, 53A-I, 59, 61, 68). After an initial decrease following rATG induction therapy (POD 0-4), there was an overall progressive increase in total T-cells in the PBMCs (Figure 47I), beginning on POD 14, accelerating after POD 33, and peaking at POD 42. That same pattern is shared across the main PBMC T cells subpopulations (Figures 47A-D). These levels fell considerably following rATG treatment on POD 49 to almost undetectable levels by POD 56. In the tissue, effector and naive CD8⁺ T cells, CD4⁺ T cells, and Tregs infiltrated the xenograft, expressing well-defined markers (Figures 53A-C). Their levels were generally similar then in the blood, reaching a maximum between POD 33-45 (Figures 47E-H), and decreasing thereafter. These changes were consistent with clinical histopathological evaluation. The 478 panel ST data identified less detailed subtypes, but CD8+ T and CD4+ T populations followed overall similar patterns of abundance (Figure 53F) and marker expression (Figure 53G). The blood data had other T-cell subsets not found in the graft with generally similar patterns of abundance across the time course (Figure 53H). In addition, the tissue snRNA-seq data shows that T cells were largely absent prior to POD 33 but had an overall lower total percentage than the ST data (Figure 53I). The blood trends were confirmed with expression dynamics of CD8+ T, CD4+ T and Treg markers in PBMC bulk RNA-seq, showing a sharp decrease of expression immediately after POD 49 (Figures 47J-L, 53J). [00342] Flow cytometric analyses also reflected the distribution patterns observed in the PBMC scRNA-seq, as seen in the overall level of T cells (Figure 47M), in addition to a peak of 202 314164997v1 Attorney Docket No: 243735.000437 activated CD8+ and CD4+ T cells around POD 33 (Figures 47N-O), and a similar trend for Tregs (Figure 47P). In addition, it also highlighted an increase in central memory and effector memory CD4+ T and CD8+ T cells after POD 21, with a subsequent reduction after POD 49 (Figure 53K). CD38, a marker associated with transplant rejection (Mancebo, et al., 2016; Doberer, et al., 2021) was highly expressed in CD8+ T cells around the AbMR event (POD 33), both in PBMC and human infiltrating cells in the xenograft, as well as in NK cells (Figure 47Q). These populations also displayed increased expression of TNFRSF9, which shows elevated expression in T and NK cells in the context of kidney allograft AbMR, and is associated with effector cell function and immune activity (Fröhlich, et al., 2020; Parkes, et al., 2017; Parkes, et al., 2018). The presence of CD103 (ITGAE) (Mueller, et al., 2016) and other tissue-resident markers (Mueller, et al., 2016; Victorino, et al., 2015; Kumar, et al., 2017; Narni-Mancinelli, et al., 2024) in graft-infiltrating T and NK cells suggests that some of these cells adopt a partial tissue- resident phenotype (Figures 47Q, 54). [00343] Recipient PBMC TCR-seq analysis identified a restricted set of clonal expansions embedded within an overall clonal diversification trend in T-cell proliferation following POD 14 and peaking at POD 49 (Figure 47R). Across the time course, drops in clonal diversity were observed at POD 28, 40, and 45, consistent with Belatacept therapy. Although the TCR repertoire is predominated by an increasing frequency of small-sized clones (<0.1%), differential V/J gene usage analysis identified a dramatic shift in representation of family genes TRBV2/J1 beginning as early as POD 28 and suggesting an expansion of specific clonotypes associated with the AbMR event (Figure 47S). TCR beta tracking analysis (Figure 47T) confirmed the presence of a single large clone (CASSDGEVNTEAFF (SEQ ID NO: 1), TRBV2*01, TRBJ1- 1*01) from POD 28-52. Remarkably, an iReceptor public database search of this and other top clones returned a >50% hit rate against the database (Table 8), providing additional context for determining the functionality and specificity of TCR beta receptors (Corrie, et al., 2018). Many of the clones overlapping with the database were previously found to be related to CMV (cytomegalovirus) and EBV (Epstein-Barr virus), while others, including the top clone, were also found in pediatric recipients following TCRαβ/CD19-depleted hematopoietic cells transplantation (Zvyagin, et al., 2017). These data suggest a T-cell-mediated response during the recovery of the host immune system, followed by infiltration into the transplanted tissue during 203 314164997v1 Attorney Docket No: 243735.000437 the AbMR POD 33 timepoint. Human T-cell activity and frequency peaked in the blood and xenograft between POD 33-49. Table 8. Top occurring TCR beta public and private clonotypes. Top TCR beta CDR3 AA V-gene J-gene Number of iReceptor database sequence timepoints detected detected 204 314164997v1 Attorney Docket No: 243735.000437 (SEQ ID NO: 19) CSAREVEPSQETQYF TRBV20-1*08x- TRBJ2-5*01 19 No treatment. [00344] The bulk transcriptional signal of the xenograft tissue over time revealed a cluster of genes that displayed high expression at POD 0 (immediately post-transplantation), but lower expression at all subsequent timepoints. These were enriched in inflammation signals and were consistent with immediate, but short-lived, ischemia/reperfusion injury (Figure 55A). This was confirmed by the expression patterns of markers FOS, FOSB, and CXCL2 in ST, which were most enriched in POD 0 (Figure 48A). Interestingly, ST revealed a clear transcriptional shift at POD 21 towards genes associated with fibrosis, inflammation, hypoxia, and injury (Figure 55B). This included SPP1, a marker of kidney injury (Yu, et al., 2023) which defines a specific population of SPP1+ cells in the 478 ST dataset, indicating damaged or inflamed loops, ducts, and tubules, and which expression increases in acute renal allograft rejection (Lin, et al., 2023; Alchi, et al., 2005). It was also found that COLEC11 defined a COLEC11+ population in the 5.1k panel ST data. COLEC11 activates the lectin pathway of the complement system and is associated with kidney damage, such as ischemia/reperfusion injury and complement associated injury rejection (Nauser, et al., 2017; Farrar, et al., 2016; Nauser, et al., 2018). These SPP1+ and COLEC11+ populations have very similar proportions, spiking at POD 21, with relatively high levels at POD 33 and decreasing thereafter (Figures 48B-C). Their gene markers (Figures 48D- E) include HIF1A, CXCL14+ or ICAM1, which are related to inflammation, immune recruitment and kidney injury (Rosenberger, et al., 2002; De Greef, et al., 2003; Hill, et al., 1995). High levels of THSB1 and STMN1 were also expressed. [00345] Similarity in expression of marker genes in both clusters suggests that they are the same population, detected in the two different ST panels. They colocalized with fibroblasts and endothelial cells across samples, and with human immune cells for POD 33 (Figures 48F-H, 55C-D). Porcine immune cells found in the graft cells were mostly resident macrophages (high expression of CD163, MRC1, C1QA, TLR2, and CCR5) (Figures 55E-F), while porcine T cell 205 314164997v1 Attorney Docket No: 243735.000437 presence was mostly limited to POD 0 based on marker gene expression (Figure 55G). Differential expression analysis of porcine immune cells in the 5.1k ST panel data revealed increased expression at POD 21 and POD 33 for genes of interest such as HIF1A, CD163, MKI67, STAT3, MX1, IFIT1, and S100A6 (Figure 55H). These pig immune cells colocalized with endothelial cells (EC), fibroblasts (FB), and human cells in POD 33 (Figures 55C-D). At POD 21, pig SPP1+ cells, also expressing CXCL2, were neighboring pig-resident macrophages expressing LYZ, S100A6, S100A11, and human immune cells (Figure 48F). In POD 33, a spatial region with increased density of infiltrating human immune cells was observed in the 478 panel ST data, expressing high levels of CXCL9, 10, 11. These colocalized with SPP1+ pig cells and with regions exhibiting interstitial fibrosis and tubular atrophy (IFTA) (Figure 48G). Similar regions were observed at POD 33 in the 5.1k panel ST data, highlighting colocalization of pig MX1, human cells, COLEC11+ cells and human CXCL9, CXCL10 and CXCL11 expression (Figure 48H). [00346] Additional evidence of kidney damage included increased expression from COLEC11, CDLN1, MX1, IL33, and MX2 in podocytes, and endothelial cells, fibroblasts, and Henle loop in the tissue (Figures 55I-J). In addition, these location with high density of infiltrating human cells (Figures 48G-H) overlap spatially with a transcriptional signature for AbMR using B-HOT (Banff Human Organ Transplant) panel, a bulk RNA panel of 770 human mRNAs related to allotransplant tissue injury, immunity and rejection forms, that was also evident in the pig to human decedent kidney xenotransplant setting in the prior 3-day study (Loupy, et al., 2023). Signals overlapping with those found as differentially expressed in that study (Loupy, et al., 2023) were largely driven by multiple macrophage subpopulations in the 478 ST panel (Figures 56A-D). In human cells originating from the 5k ST panel, B-HOT-derived AbMR signature was predominant in CXCL9+ macrophages, NK cells, and interferon+ cells (Figure 70A) and showed higher levels at POD 21 and POD 33 (Figures 56E, 70B). In addition, many probes targeting pig orthologues of B-HOT panel genes had increased expression in pig cells (5k ST panel) at POD 21 and POD 33 (Figure 56F). Spatially, AbMR signature score at POD 33 colocalized with pig MX1 expression and to a lower extent pig SERPINE1 (Figure 56G). Overall, these data illustrate a significant and multifaceted immune/inflammatory molecular response by human and pig cells before AbMR histological manifestation at POD 21, and at the AbMR biopsy positive event at POD 33 that was resolved by interventional treatment. The 206 314164997v1 Attorney Docket No: 243735.000437 immune response also involved resident pig immune macrophages as well as tubular and interstitial cells in damaged SPP1+ and COLEC11+ cellular states, with evidence of interaction with endothelial cells, fibroblasts, and the infiltrating human immune cells. Example 18. Blood proteomics reveals recipient complement activation. [00347] Mapping sera peptides from the decedent blood to human and pig proteome reference databases revealed a total of 93,712 peptides, of which 41,946 could be assigned as human- specific, and 4,192 pig-specific, measured across 63 timepoints. This corresponded to 6,228 and 1,002 proteins from the human decedent and pig, respectively. Given the role of complement pathways in the regulation of transplant immunity, longitudinal abundance analyses of complement proteins were performed in LC-MS/MS data (Figures 57, 71-72). Approximately 90% of complement proteins were detectable, and activation of complement via the lectin and alternative pathways was observed. Namely, an increase in MBL (Figure 71) and MASPs (Figure 72) in the lectin pathway, as well as C3a and C3b in the alternative pathway, was measured. This peaked at around POD 20 and was followed by a decrease to their lowest levels at POD 40. Notably, C3a, a critical chemotactic mediator, had a nadir in the blood over 6-7 days after pegcetacoplan (Empaveli) AbMR treatment initiation on POD 34. Interpretation of the complement proteomics is more challenging after the rejection event, as several sessions of plasmapheresis were administered. [00348] Until recently, the effect of all genetic modifications in pig donors and evaluation of immunosuppressive strategies in the xenotransplantation setting have been tested exclusively in non-human primates (NHP). The human decedent model therefore presents a unique opportunity to characterize immune responses to xenografts in Homo sapiens, whilst allowing for frequent sampling of blood and tissue. In this study, a high-resolution molecular profile of a 61-day pig kidney-to-human decedent xenotransplant procedure was presented, which captured a strong immune response involving multiple arms of the immune system. Components of this response were detectable as early as POD 10 and POD 21 in peripheral blood and tissue, respectively, culminating in a biopsy-proven AbMR episode at POD 33 which was successfully treated allowing for the preservation of xenograft function. [00349] A major component of this study design was the utilization of large-scale multi-omic, longitudinal and spatial profiling that allow for genome-wide discrimination of both pig- and 207 314164997v1 Attorney Docket No: 243735.000437 human-specific gene products in the xenograft and at the periphery (blood). More specifically, spatial and single-cell transcriptomics approaches were used to identify precise pig and human cell subtypes and subpopulations involved in host-recipient interactions. This included the development of two custom pig/human xenotransplant spatial gene expression panels which were refined over time (Schmauch, et al., 2024), in addition to pig kidney cell type-specific markers (Wang, et al., 2022). Also, a nanoparticle-based proteomic characterization pipeline was utilized which facilitated ultradeep longitudinal profiling of human and pig proteins in the blood. The dense longitudinal molecular profiling yielded unprecedented insights into xenograft and human host cellular interactions over the course of the study. [00350] Host innate immune activity was observed as early as POD 10, which was accompanied by major increases in NK and pDC cells, as well as peaks in B-cell clonal diversity in the recipient peripheral blood. This was followed by infiltration of these cells into tissues as early as POD 21, in parallel with increased expression of porcine SPP1 and COLEC11. Then, at POD 33, tissue remodeling stabilized, while human NK, macrophages, and T cells peaked in the xenograft. Clonal expansion of a single dominant T cell clonotype was noted around POD 33. The involvement of known AbMR genes/proteins in infiltrating human immune cells were observed at the same time as rise in serum creatinine, followed by subsequent resolution of these factors after successful treatment of the AbMR episode. The gradual increase of human CXCL9, CXCL10, and CXCL11 (Th1 immune response) in the pig kidney peaked near the AbMR episode and was followed by a rapid reduction after anti-rejection treatment. This pattern mostly originated from a population of activated macrophages, which were enriched for interferon response and cytokine signaling, and were the predominant cell population at POD 33. Another human immune cell population (interferon+) expressing high levels of interferon-associated genes was also prevalent at POD 33. CXCL9- and CXCL10-expressing immune cell hotspots may act as chemotactic niches, guiding T cell infiltration and differentiation. Similar to other tissues, localized cytokine gradients likely shape their activation and function (Reina-Campos, et al., 2025). In addition, tissue pDCs and cDC1 cells exhibited the highest expression of CXCL9, CXCL10, and CXCL11 at POD 21, alongside activation markers (Birmachu, et al., 2007; Guiducci, et al., 2008; Di Domizio, et al., 2009) such as IRF7 and TLR9, indicating both heightened activation and a potential early role in recruiting CXCR3⁺ T cells to the graft. This activation phenotype was only partially replicated in blood, with the notable absent expression of 208 314164997v1 Attorney Docket No: 243735.000437 CXCL9-11, suggesting further activation of these cells in the graft at POD 21-33. In kidney allograft rejection, pDCs have been shown to acquire phagocytic capacity, present antigens to CD4⁺ and CD8⁺ T cells, and drive immune activation (Ruben, et al., 2018; Reich, et al., 2018). Their transcriptional profile and timing in the model herein suggest that they may here contribute to similar immune responses before the AbMR event. [00351] Interestingly, xenograft studies performed in the heart (Schmauch, et al., 2024) and the kidney (Pan, et al., 2024; Cheung, et al., 2024) reported the presence of human macrophages infiltrating the graft at an early timepoint (within the first 3 days), with minimal or no detectable T and B cells during this initial period. These findings are consistent with observations herein, of human macrophage presence immediately post-transplantation, and early NK and pDC appearance whereas T cells primarily infiltrated the graft after POD 21. The results herein underscore the importance of evaluating immune infiltration and host immune response at timepoints extending beyond the initial 3-day period examined in the aforementioned studies. The ST data herein displayed a distinct bimodal distribution, and the alignment methodology was validated through control samples. [00352] The tissue remodeling observed at POD 21 and POD 33 was accompanied by specific expression of molecular markers, including SPP1 (Yu, et al., 2023; Lin, et al., 2023; Alchi, et al., 2005; Jin, et al., 2013; Sinha, et al., 2023). Also associated was STMN1, a marker of cell proliferation, shown to be overexpressed in the early cellular response to pig-to-human kidney xenotransplantation, which could be part of a tissue repair process following transplant- associated injury (Pan, et al., 2024). Also, expression of COLEC11 was observed, which encodes collectin-11, a marker of complement-mediated kidney injury (Nauser, et al., 2017; Nauser, et al., 2018), which has been associated with ischemia-reperfusion injury (Farrar, et al., 2016). Recent evidence has shown associations of collectin-11 with tubulointerstitial fibrosis (Wu, et al., 2018). THSB1 expression has been associated with kidney fibrotic remodeling after injury (Daniel, et al., 2007). Also, increased expression of other stress and injury response genes was identified, such as HIF1A (Rosenberger, et al., 2002) and IL33 (Chen, et al., 2016). The identified population of interest (SPP1+ and COLEC11+) also expressed high levels of S100A6, a known marker of tubular injury (Cheng, et al., 2005). S100A6 is also found to be markedly expressed in POD 21 and POD 33 in many cell-types of the xenograft. These data suggest tubular injury, inflammation and remodeling temporally associated with AbMR, and spatially 209 314164997v1 Attorney Docket No: 243735.000437 with the infiltration of activated human immune cells. In addition, the increased expression of MX1 and MX2, as well as spatial colocalization of MX1 with infiltrating immune cells, suggest a response of pig kidney cells to human interferon activation described herein. Resident pig macrophage activation, shown by their increased expression of CD163 (Etzerodt, et al., 2013), S100A6 (Xia, et al., 2018), MKI67 (Lee, et al., 2011), STAT3 (Lee, et al., 2024), IFIT1 (McDermott, et al., 2012), and MX1 (Rascio, et al., 2020) (specifically at POD 21 and POD 33), suggests potential signaling from inflamed or damaged tissue, and raises the possibility of interactions between infiltrating human and resident pig immune cells. [00353] Infiltrating T and NK cells co-expressed CD38 and TNFRSF9, indicative of an effector phenotype, while upregulating CD103 (ITGAE) and other tissue-resident markers. This expression pattern paralleled findings in peripheral blood, suggesting that activated cells migrated from circulation into the graft. Together, these combined activation and residency traits highlight a multifaceted immune response, wherein T and NK cells adapt to local microenvironmental cues while driving rejection. Moreover, there was increased clonal diversity of circulating B cells, increased proportion of IgA-switched B cells, and identification of a dominant TRBV2-based T-cell receptor clonotype in the circulation, after day 28 post- xenotransplant. Using the publicly available iReceptor database (Corrie, et al., 2018), the dominant TRBV2/TRBJ1 clonal signature identified in this study was queried for similar observances in the literature. The database returned an exact clonal sequence match from a single donor associated with a previous study on tracking T-cell immune reconstitution after hematopoietic transplant (Zvyagin, et al., 2017). Furthermore, in addition to the TCR beta chain, TCR alpha chain clonotype information was simultaneously generated and similarly, a strong TRAV13/TRAJ4 clonal signature was identified. Querying the database for the alpha chain signature returned another perfect match associated with a T cell repertoire study with adoptive transfer of allogenic regulatory T cells (Theil, et al., 2017). These findings lend support for the conclusion of a potential public clonotype TCR response following transplantation. [00354] Although the precise trigger for this AbMR event is not known, these data provide a multi-omic view of the AbMR event that occurred despite immunosuppression. Additional studies are required to decipher the molecular target(s) of the immune response, which may yield more directed immunosuppression strategies or donor organ genetic modifications. The AbMR event only partially responded to the first cycle of plasmapheresis, steroids, and complement 210 314164997v1 Attorney Docket No: 243735.000437 inhibition with anti-C3 and C5 treatments. Complete resolution of the AbMR episode was achieved only after further plasmapheresis and T-cell depletion with rATG. The infiltration of effector T and NK cells, as well as activated antigen-presenting cells, extracted from the xenograft on POD 33, support a role for a cellular component of the rejection episode. The isolated intimal arteritis seen on POD 49 histology could have been easily missed (presaged by the multi-omic studies on POD 33) and the cell mediated contribution of the rejection episode unappreciated. Additionally, an increase in the proportion of Tregs was first observed in the tissue peaking at POD 33-45, synchronous to the increase in peripheral blood, mimicking the TEM CD8+ T cells pattern. Expansion of Tregs is known to be induced by activated CD4+ or CD8+ T cells (Martin-Moreno, et al., 2018), and the presence of Tregs in transplanted organs is associated with graft tolerance and survival (Mohr Gregoriussen, et al., 2017). Again, the study herein demonstrates that longitudinal multi-omic profiling of xenograft tissues has utility in identifying early signals for rejection (earlier than identified by standard histopathology), as well as the unique ability to assess the functional state of both pig and human-derived cell-types as well as their interactions. [00355] While this study identified a number of key cellular responses post-xenograft, additional questions remain, including whether changes in the TCR/BCR clonotype frequencies observed in the periphery are also associated with infiltration into the graft. Additionally, while tissue sampling was relatively frequent (in total, ten biopsies over the time course), and sampling intervals were largely chosen as to not introduce additional perturbations to the graft, daily sampling of biomolecules from blood show that analyte variation occurs at a variety of timescales (Figures 46-47). As such, future decedent studies may warrant additional tissue sampling to encapsulate the extent of these dynamic changes. A key advantage of the decedent model is the ability to alter experimental conditions to explore additional questions. Along with this, additional omics strategies may uncover graft and host interactions at different levels of regulation; for example, it is unclear whether xenografted cells remain stable or undergo epigenetic state changes post-transplantation that may alter overall function. [00356] This study is one of the most densely phenotyped human studies conducted to date across a defined study period. The aim of this project was focused on discovery and the analytes identified in this study could be further developed into targeted assays (e.g., digital PCR, immunohistochemistry, etc.) that could be easily deployable in a clinical laboratory setting. 211 314164997v1 Attorney Docket No: 243735.000437 Previous atlas studies have revealed technology-dependent biases in capturing kidney cell types, with scRNA-seq excelling at detecting leukocytes and snRNA-seq specializing in epithelial cells (Abedini, et al., 2024; Kim, et al., 2023). Reconstructing cell populations in complex or injured kidney biopsies is even more challenging when relying on a single technique (Kim, et al., 2023). By contrast, ST assays excel at identifying diverse immune populations and provide a reliable ground truth for cell-type composition when integrated with histological staining (Abedini, et al., 2024; Janesick, et al., 2023; Zhang, et al., 2024). Consistent with this, the xenograft snRNA-seq data showed a lower percentage of human immune cells in the graft compared to the ST data herein. Leveraging the complementary strengths of these advanced techniques enabled the study herein to achieve a high-resolution characterization of cell types in the xenograft model. [00357] In conclusion, the study herein provided an in-depth genomic and pathway-level immune cell characterization allowing early immune and tissue cell responses to the xenograft to be assessed in a human decedent within an ICU setting and maps the early immune response and donor xenograft damage before physiological kidney function decline. The work herein confirms the absence of initial xenograft injury in the first days post-transplantation, characterizes the AbMR episode before its clinical and histological presentation, and confirms its resolution. This study provides an extensive data resource for understanding the dynamics and importance of inflammatory and tissue repair archetypes, interactions with resident pig immune cells, and infiltrating human immune cells comprising both the innate and adaptive immune systems of the recipient. Example 19. Prognostic and diagnostic assays based on gene signatures. [00358] The use of gene signatures for both prognostic and diagnostic applications in the setting of organ transplantation, particularly xenotransplantation, are described herein. These signatures allow evaluation and prediction of biological processes such as tissue injury, inflammation, fibrosis, immune activation, rejection, and overall graft health. By analyzing expression patterns across a set of genes, it becomes possible to detect early signs of dysfunction and to guide clinical decision-making and therapeutic interventions. [00359] The gene signatures were derived from experimental data collected and described herein during short-term (2–3 days, Pan, et al., 2024; Schmauch, et al., 2024) and long-term (up to 61 days) xenotransplantation experiments. Despite differences in timing and organ systems 212 314164997v1 Attorney Docket No: 243735.000437 (heart and kidney), there was an important overlap among the markers identified, suggesting that all the gene signatures described herein are applicable across multiple transplanted organs (for example, but not limited to, heart and kidney) and at different post-transplant timepoints. [00360] These signatures will be used for early post-transplant assessment (e.g., starting as early as 3 days or before), longitudinal monitoring over time (e.g., at regular intervals, over longer timeframes), and real-time diagnostics in response to clinical signs of graft dysfunction. [00361] Samples for analysis will include: blood (e.g., PBMCs, plasma, serum), tissue biopsies (e.g., from the transplanted organ), urine (e.g., for kidney grafts), and/or other proxy fluids or tissues as appropriate (e.g., pericardial fluid for heart transplants). [00362] While the experiments herein focused on RNA expression, the markers could be assayed at the RNA level, downstream at the protein level, and/or upstream at the expression regulation level (epigenetic). Potential assay modalities include: bulk RNA-sequencing, targeted RNA panels, or qPCR assays; antibody-based methods (e.g., flow cytometry, immunohistochemistry, or ELISA); epigenetic assays (e.g., methylation, chromatin accessibility [ATAC-seq]); single-cell technologies (e.g., scRNA-seq, CITE-seq); spatial transcriptomics (e.g., with targeted panels containing genes of interest or with whole transcriptome methods followed by signature enrichment analyses); and/or multiplexed approaches combining RNA and protein measurements. [00363] The gene set described herein also includes cell-type markers that were identified in experiments described herein. Many of these cell types show major shifts in abundance in response to transplantation and are associated with the processes described herein. Thus, expression of these markers could also serve as a proxy for the abundance or activation state of these cell types in clinical samples, with major prognostics or diagnostic value. This approach in disclosed herein. [00364] The signatures include markers of both porcine and human origin, reflecting the dual biology of xenotransplantation (e.g., donor and host). This provides an opportunity to track both graft-intrinsic and host-driven processes in parallel. Finally, these signatures could be used individually or in combination to: stratify patients according to risk of rejection or tissue injury, guide immunosuppressive therapy, predict long-term graft outcomes, and/or monitor therapeutic responses or disease progression. 213 314164997v1 Attorney Docket No: 243735.000437 [00365] Experiments described herein of pig to human xenotransplant in 2 decedents, studied for 2-3 days, revealed the following gene signals. Human genes include: LYZ, CD163, NKG7, GNLY, KLRD1, TYROBP, IFNG, CXCL9, CXCL10, CXCL11, COL13A1, TM4SF18, ICAM2, CDH13, RAPGEF5, ROBO4, PLA1A, ANKRD22, TNFSF8, SLAMF8, CDH5, PECAM1, and RAMP3. These genes were shown to have increased expression in human cells in the graft following transplantation, aligning with enhanced immune activity, inflammation, and rejection signals. Pig genes include: CXCL11, CXCL10, COL13A1, TM4SF18, ICAM2, CDH13, RAPGEF5, ROBO4, PLA1A, ANKRD22, TNFSF8, SLAMF8, CDH5, PECAM1, RAMP3, SPP1, GPNMB, S100A6, STMN1, SLC34A1, CD3E, PCLAF, HMGB2, HMGN2, SMC4, USP1, CKS2, and NASP. These genes were found to have increased expression in the xenograft tissue, as a response to inflammation and the rejection signals, including many damage markers. [00366] Experiments described herein of pig to human xenotransplant in 1 decedent, studied for 61 days, revealed the following gene signals. Human genes include: CXCL9, TRBV2, TRBJ1, IGG, IGA, IFNG, CXCL10, CXCL11, STAT1, LILRB1, CD163, CD14, MRC1, CXCR1, CXCR2, CD300E, LILRA5, FCN1, LILRB2, MX1, IFIT1, IFIT2, CD3E, IGHG, IGHA, IGHD, IGHM, FOXP3, CD38, TNFRSF9, and ITGAE. These genes were found to be associated with the rejection event in temporality and are major markers of invading immune cell activity in the graft during the experiment. Further human genes include: CXCL10, CXCL11, CXCL9, GBP1, GZMB, ICAM1, IDO1, IRF1, KLF4, LILRB2, PECAM1, PRF1, TAP1, and WARS. These genes are known AbMR-associated genes that were found to be temporally associated with the rejection event. [00367] Additionally included are cell type markers of cell populations which were found to display proportion dynamics that were associated with the rejection event and other clinical events displayed by the xenograft. Cell markers of human cells invading the tissue include, for mast cells: IL1RL1, KIT, GATA2, SLC18A2, LIF, IL18R1, MS4A2, SIGLEC6, HDC, HPGD, and MAOB; for interferon+ cells: MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, CCL8, IRF7, and IFIH1; for neutrophils (activated): ANXA3, CEACAM8, DEFA1, MMP8, PADI4, PGD, RNASE3, CAMP, CSF3R; for plasma cells: XBP1, MZB1, TENT5C, DERL3, CLPTM1L, SLAMF7, PIM2, PDIA4, and POU2AF1; for cDC2 cells: FCER1A, CLEC10A, CD1C, CD1E, CD1D, ENHO, CCR2, FCGR2B, CX3CR1, and RTN1; for pDCs: PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, 214 314164997v1 Attorney Docket No: 243735.000437 TLR7, and TLR9; for plasmablast cells: XBP1, MZB1, TNFRSF17, CD27, TENT5C, TNFRSF13B, POU2AF1, CPNE5, PRDM1, NUGGC, and SDC1; for cDC1 cells: CLEC9A, DNASE1L3, C1orf54, IDO1, CLNK, CADM1, FLT3, ENPP1, XCR1, and NDRG2; for B cells: MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, LINC01781, BLK, IGHM, and IGHD; for Monocytes-MP cells: MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, PLTP, GPR34, LGMN, MAN1A1, MAF, and CXCL12; for CXCL9+ macrophages: STAT1, GBP1, GBP5, WARS, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A; for CXCR1+ CXCR2+ monocytes: CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, AQP9, ANXA3, MCTP2, CXCR2, CD177, IL18RAP, and MMP9; for inflammatory classical monocytes: CD300E, LILRA5, CFP, FCN1, LILRB2, LILRA6, and CLEC12A; for Prolif2 (proliferative MP) cells: STMN1, TOP2A, TYMS, H2AFX, and BIRC5; for pDC-cDC1: IRF8, IRF4, IL3RA, MZB1, WDFY4, GZMB, IDO1, SPIB, RUBCNL, CLEC4C, SULF2, BCL11A, RUNX2, LILRA4, SMPD3, and CCDC50; for CXCL9+ MP cells: CXCL9, CXCL10, CXCL11, STAT1, GBP1, GBP5, LILRB1, FCGR1A, CLEC7A, and CLEC10A; for T cells: CD3G, THEMIS, CD5, and CD6; for CD4T cells: CD4, CD40LG, CCR2, CCR6, and DPP4; for Treg cells: CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA; for CD8T cells: CD8B, and CD8A; for CD8T naïve cells: LEF1, TCF7, SELL, CD27, CD55, and CCR7; for CD8 TEM cells: KLRG1, GZMK, CST7, and GZMH; for cytotoxic cells: GZMA, PRF1, and FASLG; for NK cells: KLRC1, GZMB, TRDC, and KLRF1; and for proliferating cells: TOP2A, TYMS, and MKI67. [00368] Cell markers of human blood cells include, for T cells: CD3G, CD3E, THEMIS, CD5, and CD6; for naïve cells: LEF1, TCF7, SELL, NELL2, CD55, and CCR7; for CD4+ cells: CD4, and TSHZ2; for CD8+ cells: CD8A, and CD8B; for Treg cells: CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA; for CD8 TEM cells: KLRG1, GZMK, CST7, and GZMH; for cytotoxic cells: GZMA, PRF1, and FASLG; for CD4 TEM cells: ADAM19; for NK cells: KLRC1, GZMB, TRDC, and KLRF1; for proliferating cells: TOP2A, TYMS, and MKI67; for BLK+ cells: BLK, DNM3, ZNF462, and MCF2L2; and for MAIT cells: SLC4A10, KLRB1, KLRG1, PLCB1, ME1, and ADAM12. [00369] Resident/circulating markers for human NK cells include resident markers: ITGA1 (CD49a), CD44, CD160, TNFSF10, CCR2, CD69, TCF7, EMB, KIT, LTB, and BHLHE40; and circulating markers: S1PR5, KLF2, SELL (CD62L), CX3CR1, and KLRG1. Resident/circulating 215 314164997v1 Attorney Docket No: 243735.000437 markers for human T cells include resident markers: ITGAE (CD103), ITGA1 (CD49a), CXCR6, CXCR3, ZNF683 (HOBIT), PRDM1 (BLIMP-1), CD69, CD101, and CRTAM; and circulating markers: S1PR1, SELL (CD62L), CCR7, KLF2, TBX21, EOMES, and CX3CR1. [00370] Regarding pig genes, overall markers associated with the rejection event, inflammation, pig immune activity, hypoxia, fibrosis and damage in the tissue include: BGN, C1QA, C3, C3b, C4A, C5b9, CCL19, CD163, CDH18, CDLN1, CCR5, CLDN1, COLEC11, COL1A1, COL1A2, CXCL14, CXCL2, DCN, FOS, FOSB, HIF1A, ICAM1, IFIT1, IL1R1, IL33, JUNB, LYZ, MKI67, MRC1, MX1, MX2, NFKBIZ, NREP, S100A11, S100A6, SERPINE1, SOX9, SPP1, STAT3, STMN1, THBS1, and TLR2. [00371] Genes associated with the rejection event in pig immune cells: HIF1A, LIMA1, EMP1, EFNA1, DTX4, LYVE1, YWHAG, CD163, CCL2, VCAM1, MKI67, CDK1, CD19, IFIT1, CD1D, PSMD1, FUS, CAM1, STAT1, GBP1, TOP2A, S100A6, MCM6, IGFBP2NL, CTNNB1, YWHAZ, SMCA2, SMCA1, SERPINF1, SERPINH1, TOP2A, AP2M1, RBP7, NDUFB9, MX1, CD1E, FTL1, MK2, MSN, WARS, SLURP1, ANXA2, ANXA1, SMP, TAU, PLC, ACSL4, IL4, and RGS5. [00372] Genes associated with the pig SPP1+ / COLEC11+ population: SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP. Known genes associated with AbMR found to be increased around the rejection event in pig cells: ICAM1, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1. [00373] Experiments described herein of pig heart to human decedent xenotransplantation, studied for 3 days, revealed the following gene signals. Human genes include genes found to have patterns of interest in the human PBMC data that are mostly associated with immune activity, such as: CD69, IL7R, CCR7, TCL1A, CD70, FOS, IL6, IL8, IL10, IL13, CX3CR1, STAT4, and ID2. Genes found to have patterns of interest in human cells found to be infiltrating the graft include: CCR7, CD69, IL7R, CX3CR1, STAT4, and ID2. [00374] Additionally are included cell type markers of populations that show proportion changes across time during the experiment most of which have strong evidence of involvement in the graft outcome. Cell markers of human cells include, for T cells / NK cells: CD247, CD96, ITK, CD7, BCL11B, and APBA2; for B cells: EBF1, BANK1, MS4A1, BLK, CD79A, PAX5, 216 314164997v1 Attorney Docket No: 243735.000437 and PLEKHG1; for Monocyte-MP cells: CD163, VCAN, LYZ, FMN1, MAFB, CD14, and FCN1; for dividing cells: MKI67, CENPF, ASPM, RRM2, and TPX2; for granulocytes: IL18RAP, MGAM, and ADGRG3; for HSC-CD34⁺ cells: ERG, CHRM3, MED12L, MEIS1, ZNF521, and CD109; for erythroblasts: CA1, SLC4A1, ALAS2, HBA1, HBA2, HBB, and HBQ1; for megakaryocytes: PF4, PPBP, GP9, GNG11, ITGA2B, and TUBB1; for CD8 T cells: CD8A, CD8B, KLRG1, TRGC2, PZP, and A2M; for CD4 T cells: CD4, ANK3, IL7R, CD28, CAMK4, and CCR7; for Treg cells: IL2RA, CTLA4, RTKN2, and F5; for FOS⁺ T cells: FOS, FOSB, JUN, CFAP20, and LMNA; for GZMK⁺ T cells: GZMK, MYB, SESTD1, SIRPG, and CD84; for NK cells: AREG, GNLY, TYROBP, IGFBP7, BNC2, FCER1G, and NCAM1; for dividing T cells: DIAPH3, RRM2, TOP2A, KIF15, and POLQ; for active-B cells: SOX5, ITGAX, ITGB2, GPR137B, TUBB6, TOX, SYT1, ENC1, CD86, and CD19; for TCL1A⁺ B cells: TCL1A, CARMIL1, THRB, IGHD, and COL19A1; for CD70⁺ B cells: CD70, ALPL, PXDC1, TMEM163, COL4A3, and KIF13A; for infla-B cells: TNFAIP3, IL4R, NFKBIA, and NFKBID; for PDGF⁺ B cells: SIPA1L1, DNMBP, and ZHX2; for plasma cells: PRDM1, NUGGC, GPRIN3, INPP4A, ERN1, and TNFRSF17; and for HSF1⁺ B cells: HSPA1A, HSPA1B, DNAJB1, and DNAJB4. [00375] Overall pig genes of interest, found to be associated with the worst outcome of decedent 1 include: FOS, CD80, IL15, RIPK2, HMOX1, IL1RAP, TIMP1, IL25, IL17RB, CCL2, IL4R, STAT3, HIF1A, IL33, BGN, DCN, S100A2, CCDC3, CCN3, MYL4, THBS1, ENO1, CXCL14, and PHLDA2. [00376] Pig genes of interest, associated with vascular cells/dividing cells, and overall damage markers/region include: HIF1A, IL17B, IL33, IL1RL1, IL1R1, CXCL14, PHLDA2, ICAM1, SELP, SELE, ADIRF, MYH11, CDC3, TP5311, CKB, MYLK, PIL6, CSPG4, CPE, C4A, GJA5, KCNMB1, OLFML2A, S100A2, THBS1, TIMP1, SERPINE1, ENO1, PIP5, and CCL19. [00377] Genes known to be associated with PCXD, and that showed patterns of interest in the analysis include: CPM, PTX3, SLC5A6, CXCL14, PHLDA2, PPP1R14B, TNC, FAF1, IL1R1, HMOX1, F13A1, FERMT1, STOX1, LMAN2L, SLF1, TRIM44, TMEFF2, SLC40A1, GASK1B, CTSS, NTRK2, MS4A7, HBB, RND3, RHOJ, and AKR1B1. [00378] Pig genes that are members of specific pathways/annotations (gene sets) and were found to be differentially expressed in the xenotransplant experiment (specifically in D1 vs. control) include, for interleukins (CM): TAB2, IL17RB, PELI1, MEF2A, FBXW11, PSMB7, 217 314164997v1 Attorney Docket No: 243735.000437 IL13RA1, FOS, STAT5B, SOCS3, CDC42, MSN, PSMD14, CD80, N4BP1, PSMD5, BCL6, CAPZA1, PTK2B, IL1RAP, IL15, PSMC2, MMP2, RAP1B, PTPN12, USP14, PSMB2, COL1A2, VIM, ANXA1, FN1, CCL2, PSMA6, HMOX1, IL4R, PSMA4, PSMD6, PSMD12, YWHAZ, PSMC1, RIPK2, RPS6KA3, STAT3, CRLF1, TIMP1, CREB1, PSMC6, IL25, HIF1A, LYN, DUSP4, CEBPD, PSMA3, NFKBIA, GSTO1, and ANXA2; related to noncanonical NF-κB signaling: FBXW11, PSMB7, UBA3, PSMD14, PSMD5, PSMC2, PSMD7, PSMB2, PSMA6, PSMA4, PSMD6, PSMD12, PSMC1, CHUK, PSMB3, PSMC6, and PSMA3; related to DNA replication (FB): KIF23, RFC5, BUB3, CENPF, KIF18A, ZWINT, PPP2R5E, RFC4, MCM10, PSMD11, KIF2C, CDCA8, MAD1L1, CENPH, MCM4, BUB1, CENPN, CLIP1, NUF2, MCM3, SPDL1, SMC1A, PSMD5, ZWILCH, DNA2, PSMC1, POLE4, E2F3, PSMD12, NUDC, BUB1B, PSMA5, NUP107, FBXO5, PAFAH1B1, MCM2, PSMD6, PSMB7, PRIM2, ORC1, CDC6, KNTC1, SKA1, MAD2L1, PSMD14, KIF20A, PSMD1, GINS1, NDC80, PSMB2, SEC13, CENPT, PPP2R1A, PPP2CA, SPC25, RNASEH2C, PSME3, PSMA3, CDC7, PSMC6, BIRC5, RFC1, POLE, PSMA6, ORC3, NUP37, MCM6, SPC24, NUP85, CENPL, RCC2, CDKN1A, and ERCC6L; related to EMT (epithelial-mesenchymal transition): TIMP1, CALU, PLOD2, BGN, SERPINE1, LOX, SFRP1, GJA1, PDLIM4, RHOB, PMP22, IGFBP2, LUM, TPM4, TNC, ITGB1, TAGLN, FN1, CCN2, COL6A3, MMP2, COL1A2, COL4A2, ECM1, LGALS1, IGFBP4, EMP3, VIM, ITGB3, ITGAV, PPIB, CRLF1, COL12A1, MGP, TFPI2, ID2, IL6, GPX7, COL5A2, SERPINE2, TGFBR3, DCN, and THBS1; related to Myc Targets V1: PSMD14, NPM1, ABCE1, MYC, LDHA, PRDX4, NAP1L1, EIF4A1, KPNA2, ERH, DEK, ILF2, RACK1, EIF4E, DDX21, SNRPD1, DUT, PSMD1, EIF3B, RAN, HDGF, HSP90AB1, MCM4, EEF1B2, NME1, SNRPD2, RANBP1, SERBP1, PPM1G, ACP1, SNRPG, HDAC2, PSMA6, GNL3, DHX15, ETF1, IARS1, KARS1, CCT4, NCBP1, AIMP2, PGK1, PSMC6, FBL, CTPS1, TRA2B, and RSL1D1; and related to TNF-alpha signaling via NF-κB: CEBPD, SERPINE1, KLF10, BHLHE40, FJX1, PDE4B, RHOB, SOCS3, SGK1, PHLDA2, PLAU, FOSL2, SPSB1, EDN1, TNC, MYC, ACKR3, IL6ST, MAP3K8, GCH1, SERPINB8, B4GALT5, ETS2, CEBPB, GPR183, CDKN1A, EFNA1, FOSL1, NINJ1, FOS, ID2, TGIF1, IL6, and PFKFB3. [00379] Pig markers of population of interest, that displayed enrichment in specific pathways believed to be related to the response of the organ to transplantation, and found to be present in high proportion in D1 include, LEC3 marker genes: COL4A2, AGO2, ADAMTS9, SOCS5, 218 314164997v1 Attorney Docket No: 243735.000437 RAI14, PLXNA2, BIRC2, PLAT, CCNL1, CEMIP, SMAD3, PARVB, HIF1A, MARCHF3, SERPINE1, SEMA3A, CEMIP2, PALM2AKAP2, CHSY3, WDR43, MACF1, ABHD17C, VMP1, ARHGAP23, EMP1, NFKBIA, YBX3, TNIP1, PHF11, LONRF3, USP10, NOL11, HK1, LTBP1, EMP2, RIN3, ACKR2, TXNRD1, REL, NAV2, and TEAD4; and DIV VEC2 markers: LDB2, PLAT, CARMIL1, NFKBIZ, LRRC1, RIN2, SH3BP5, ADAMTS9, LIPC, RAI14, CALCRL, LTBP1, PTPRB, ANKRD28, SLCO2A1, IL1R1, NAV1, PLVAP, PLXNA2, PTPRE, NXN, IL33, GRB10, ELK3, SEMA3F, BNC2, FLT1, PLCB1, PRICKLE1, CMIP, WWTR1, EPB41L4A, PELI2, FRMD4B, PECAM1, TEK, HIF1A, ENTPD1, HRH1, and SMURF2. [00380] Below are the methods used in Examples 1-6 described above. [00381] Patient data and biospecimen collection: The 10-gene-edit pig heart xenografts received by both decedents showed no ostensible evidence of acute cellular rejection or AbMR on conventional histology, or on flow or complement-dependent cytotoxicity crossmatches (Moazami, et al., 2023). The hemodynamics and left ventricular stroke volume of D1 began to deteriorate at 30-36 h after reperfusion, whereas the cardiac function of D2 remained stable throughout the study. D1 developed an increased need for vasopressors, which led to evidence of visceral hypo-perfusion, including rising lactate and liver enzymes, indicating ischemic injury to end organs. D1 explant tissue demonstrated progressive myocyte injury and cell death, which were not present in day 1 and 2 endomyocardial biopsies. Standard histology and immunohistology did not show features of hyperacute rejection in either daily biopsies or explant (Moazami, et al., 2023). [00382] The two human decedent recipients of the pig heart xenografts were one male and one female, both of European ancestry. The first xenotransplant was performed in an 82-kg, 72-year- old male recipient with a body mass index of 25.9 and a history of coronary artery bypass graft surgery and heart failure. A heart from a 70-kg male pig that was 309 days old at procurement was used. The total ischemia time was 4 hours and 21 minutes, of which 3 hours and 11 minutes was in cold static storage. The second xenotransplantation recipient was a 57-kg, 64-year-old female with a body mass index of 22. She had two previous kidney and pancreas allotransplants and was maintained on oral mycophenolate and prednisone. A heart from a 69-kg male pig, 329 days old at procurement, was used and the total ischemia time was 3 hours and 30 minutes, of 219 314164997v1 Attorney Docket No: 243735.000437 which 2 hours and 40 minutes was in cold static storage. There were restrictions on the transport of pigs, so the xenograft had to be flown in, impacting cold ischemia time; perfusion-based preservation of the pig heart xenograft was not possible. The immunosuppression regimen for both decedents did not include the co-stimulatory blockade drugs considered essential to support long-term survival of a xenograft (Cooper, et al., 2007; Schroder, et al., 2019). Both decedents received a single dose of 1,000 mg methylprednisolone and rATG (thymoglobulin), 1.5 mg kg−1 preoperatively, whereas the second decedent received an additional dose of rATG 24.5 hours post-transplant. Both decedents received 1,200 mg eculizumab on postoperative day (POD) 0 and 900 mg on POD 1. Maintenance immunosuppression consisted of administering 1,000 mg of methylprednisolone daily, 1,000 mg of intravenous mycophenolate mofetil twice daily beginning POD 0, which continued until the heart was explanted at 66 hours post-reperfusion. Consent and other regulatory protocols, operative procedures, available phenotypes, and physiological and clinical outcomes are described elsewhere (Moazami, et al., 2023). Key biospecimens, physiological measurements, blood chemistries and other laboratory values are described in Table 9. CO/CI measurements commence approximately 24 h post-reperfusion. Cardiac function data was enhanced by Swan Ganz catheter data, offering quantitative insights into cardiac performance beyond the basic CO/CI metrics. Blood samples were collected at pre-transplant (0 h) and then at 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 65, 66 and 66.5 hours (terminal sample) for D1 and 0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60 and 66 hours (terminal sample) for D2. Table 9. Clinical parameters and blood laboratory measurements of the two decedents during transplantation. Sample D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D1 D2 D2 D2 D2 D2 D2 D2 D2 D2 D2 D2 D2 66 h 66 220 314164997v1 Attorney Docket No: 243735.000437 CVP 10 15 18 17 14 15 19 19 20 19 16 18 14 20 20 20 19 20 20 21 18 17 19 20 (central 3.5 6 1.8 8 107 8 2.5 0 0.0 5 0 28. 3 221 314164997v1 Attorney Docket No: 243735.000437 82 98 85 70 57 42 35 32 30 28 27 21 12810793 85 73 66 53 44 38 CL 36 U 56 42 2.6 123 9 834 46. 1 482 35 41 1.1 595 4.3 222 314164997v1 Attorney Docket No: 243735.000437 IL6 8.3 43. 33. 30. 35. 49. 71. 1434311031812508.3 63 18. 12. 13 15. NA NA 4.9 4.2 5.6 6 (pg/mL) 5 7 5 8 9 2 .1 .1 6 2 6.5 4 5 6 <3 54. 7 <1. 7 across the two pig-to-human decedent cardiac xenotransplant procedures, spanning PBMCs and fresh-frozen tissue samples by bulk RNA-seq, targeted RNA profiling, scRNA-seq and snRNA- seq, and spatial transcriptomics as described herein. Bulk RNA-seq and scRNA-seq were performed on blood samples collected at the time points above, except for the final three bulk RNA-seq time points from D1, which occurred at 60, 64 and 65 hours. [00384] Bulk RNA-seq: Bulk RNA-seq was performed to assess the concordance with scRNA- seq, as the latter is more prone to batch effects, with fewer numbers of starting input cells, leading to wider variance in cell-type proportions. Analyses were performed using venous blood collected into PaxGene tubes across all time points and processed as a single batch. For data analysis, FASTQ files were aligned to GRCh38 GENCODE Human release v.43 using STAR v.2.7-10a. STAR run parameters were adapted from the ENCODE RNA-seq pipeline for gene count quantification (ENCODE Project Consortium, 2012). Counts were preprocessed by removing genes with fewer than an average of four counts across all samples. For multi-omics integration, gene DEA was performed on each contrast using DESeq2 (Love, et al., 2014). Standalone analysis was conducted at the decedent level using the time after transplantation (in hours, with pre-transplantation set as 0) as a continuous variable, with DESeq2 (Love, et al., 2014). This was used to track expression shifts over the post-transplantation period. [00385] PBMC scRNAseq: Fresh PBMCs at each time point were collected by gradient centrifugation, followed by lysing any remaining red blood cells and cryopreservation according to the 10x Genomics protocol (protocol no. CG00039). Upon collection, the cryopreserved cells were thawed in four batches, then loaded for standard 10x Genomics scRNA-seq (v.3.1 Chemistry,10x Genomics). Initial scRNA-seq data processing was performed using the 223 314164997v1 Attorney Docket No: 243735.000437 CellRanger v.7.0.1. pipeline using the vendor-recommended workflow. After CellRanger preprocessing, count matrices were processed using scanpy (Wolf, et al., 2018) v.1.9.3, with Python v.3.10.10. In brief, quality control (QC) filters were applied as follows: maximum 50,000 counts; maximum 7,000 genes; maximum 20% mitochondrial gene expression; and minimum 500 genes. Several levels of correction were applied, including regression of the number of unique molecular identifiers (UMIs), genes and cell cycle. Harmony (Korsunsky, et al., 2019) (harmonypy v.0.0.6) was used for batch correction, at the individual level (to integrate the two decedents) for main cell-type embedding and at the time-point level to correct for technical batch-effect and subtype analysis. The counts were normalized and logarithmized. For visualization and clustering, highly variable genes were selected using scanpy's dedicated function and the integrated matrix was scaled. PCA and UMAP (McInnes, et al., 2018) algorithms were then applied (including UMAP k-nn graph creation). From the k-nn graph, clusters were discovered using the Leiden (Traag, et al., 2019) algorithm (leidenalg v.0.9.1). Cell types were identified based on marker genes and gene set enrichment. Pseudobulk counts were sourced using the dc.get_pseudobulk function in Decoupler (Badia-I-Mompel, et al., 2022) v.1.4.0. In essence, raw counts from cells of the same sample/cell type combination were added, allowing DEA with sample-level significance. DESeq2 (Love, et al., 2014) v.1.38.0 was run independently for each cell type in both decedents, using R v.4.2.3. DEA was conducted for each decedent and cell type independently using time after transplantation (in hours, with pre- transplantation set as 0) as a continuous variable. This metric served to investigate track expression shifts over the post-transplantation period. [00386] Tissue snRNA-seq/spatial transcriptomics: For snRNA-seq, 4 × 50 µm OCT curls were used as starting material, from which nuclei were isolated using a Chromium Nuclei Isolation kit (10x Genomics). The isolated nuclei were resuspended in 50 µl wash and resuspension buffer, counted using an automated cell counter, immediately loaded into gel bead-in-emulsion as a single replicate and run according to the Chromium Single Cell 3’ kit (10x Genomics). For spatial transcriptomics, 10-µm OCT sections in duplicate were used (serial sections processed to single-nuclei curls). Fixation, H&E staining, and imaging for the Visium processing was performed using the Visium v.1 protocol (CG000239) (10x Genomics). For both snRNA-seq and spatial transcriptomics (Visium, 10x Genomics), first flash-frozen samples were embedded in 224 314164997v1 Attorney Docket No: 243735.000437 OCT compound to preserve the structure of the pig heart xenograft tissue and to provide support during cryo-sectioning. Samples were kept at -80 °C before sectioning. [00387] For snRNA-seq, the raw data were processed using CellRanger with mixed genomes (‘Barnyard’ experiment, with GRCh38 and Sscrofa11 genomes). This enabled the correct labeling and assignment of pig and human nuclei, in addition to cross-species multiplet removal (Figure 19). To ensure the absence of multiple alignments, CellRanger was also run against the human genome (GRCh38) and pig genome (Sscrofa11) separately. Velocyto v.0.17.17 (La Manno, et al., 2018) was run to obtain nuclei-level splicing fractions. Datasets were further partitioned by species. Also, a publicly available pig heart dataset was acquired (Andrijevic, et al., 2022), exploring pig heart single-nuclei transcriptomes during control, ischemic and reperfusion conditions, under the Gene Expression Omnibus accession no. GSE183448, which was acquired by 10x chromium snRNA-seq as above. The raw fastq files were retrieved through the Sequence Read Archive and were processed with CellRanger using the same pig genome and velocyto (La Manno, et al., 2018). [00388] Separation of pig and human nuclei from the xenograft data, and their independent analysis, allowed integration of the xenograft pig nuclei with the public dataset (Andrijevic, et al., 2022) nuclei. For the pig nuclei, the following filters were applied: maximum UMI was 30,000 for xenograft and 40,000 for public data (Andrijevic, et al., 2022), the minimum number of genes was 500, while the maximum was 5,000 for the xenograft and 6,000 for the public dataset (Andrijevic, et al., 2022), the maximum mitochondrial content was 2% and the minimum number of counts was 500. Maximum counts and gene thresholds are different for the xenograft and public dataset (Andrijevic, et al., 2022) due to small difference in distribution of number of genes. Other parameters are kept the same for both datasets to ensure comparability. For the human nuclei, lenient filtering was applied due to their low number (100 minimum genes, 200 minimum counts, and 20% maximum mitochondrial content). Downstream, clusters identified as clear doublets, containing all marker genes for several cell types, were removed. For the pig nuclei, both datasets and samples were integrated using harmony (Korsunsky, et al., 2019) at the sample level. Cell types were identified using marker genes and gene set enrichments, as mentioned herein. For both human and pig nuclei, counts were (separately) normalized, logarithmized, and highly variable genes were selected, with the data corrected through regression by number of UMIs and mitochondrial content. The same approach used for the 225 314164997v1 Attorney Docket No: 243735.000437 PBMC scRNA-seq was also utilized (PCA, UMAP, and Leiden). Scrublet (Wolock, et al., 2019) v.0.2.3 was run on the pig nuclei, applying a filter of 0.1 of maximum doublet score to remove doublets. For the pig nuclei, a pseudobulk count matrix was created the same way as for the PBMC scRNA-seq, resulting in one transcriptome per sample per cell type. DESeq2 (Love, et al., 2014) was run for each of the eight conditions (D1, D2, 1 h PMI, 7 h PMI, ECMO, and OrganEx), independently and each were compared to controls (0 h PMI, no ischemia). For subclustering analysis nuclei belonging to the cell type of interest were selected and the downstream processing was performed again, starting from the raw counts matrix. A resolution of 0.5 was selected for Leiden subclustering. Subcluster-specific marker genes were obtained with a Wilcoxon rank-sum DEA, which output was used in GSEA (using the GSEApy (Fang, et al., 2023) v.1.0.5 prerank function) obtained pathways enriched in each subcluster. For cell-cell communication analysis, the whole integrated snRNA-seq data were used, with the count matrix for all samples and the nuclei main cell-type annotation was passed to CellChat (Jin, et al., 2021) (v.2.0.0). The CellChat tutorial was followed to infer the cellular communication network for each condition and to compare the strength and number of interactions between the conditions. [00389] For spatial transcriptomics, the six samples (2×D1-LV, 2×D2-RV, and 2×D1-RV) were processed using CellRanger and the mixed genome. They were further processed by scanpy (Wolf, et al., 2018). Reads aligned to the human genome were discarded. Barcoded spots were filtered based on a maximum of 30,000 counts and 5,000 genes, a minimum of 100 counts and genes and a mitochondrial filter set at 40%. Data were processed as with the single-cell protocols, encompassing normalization, logarithmization, selection of highly variable genes, standard regression based on total counts per spot, scaling, PCA, UMAP dimension reduction, and Leiden clustering. For deconvolution, two methods were used to infer the cell-type composition of the spatial transcriptomics spots, using the annotated snRNA-seq data (from the matched samples) as the expression references. Cell2location (Kleshchevnikov, et al., 2022) v.0.1.3 was run according to the documentation’s tutorial, using the sample origin as the batch key, a detection_alpha value of 150 and a number of expected cells per location of five (N_cells_per_location). DestVI (Lopez, et al., 2022) (DestVI-utils v.0.1.0 and scvi-tools v.1.0.4) was run following the documentation’s tutorial, to obtain predicted cell-type proportions. [00390] All enrichments were run using GSEApy (Fang, et al., 2023), the prerank module was used for GSEA, and the enrichr module was used for over representation analysis. 226 314164997v1 Attorney Docket No: 243735.000437 [00391] Proteomics: Proteomic profiling of plasma samples was performed by 16-Plex tandem mass tag (TMT)-based mass spectrometry (MS). For a high-throughput TMT-based MS study, a pool of all samples was used as a reference across different runs. Protein quantification and QC were performed as previously described (Andrijevic, et al., 2022). In brief, in each MS run, pep- tide spectrum matches (PSMs) without quantification were removed before sum normalization with the reference channel. Only PSMs with unique protein groups were selected that had an SPS match of >65 and a co-isolation interference of <50. PSMs with a sum of 200 from all 16 channels were removed. The resulting PSMs were combined into protein groups, resulting in over 1,000 protein groups being identified. [00392] Metabolomics and lipidomics: Sample preparation plasma: Metabolites and complex lipids were extracted using a biphasic separation with cold methyl tert-butyl ether (MTBE), methanol, and water in a deep-well plate format. In brief, 1 ml ice-cold MTBE and 260 µl methanol was added to 40 µl plasma spiked with 40 µl deuterated lipid internal standards (cat. no.5040156, Sciex). Samples were agitated at 4 °C for 30 minutes. After addition of 250 µl ice- cold water, samples were vortexed for 1 minute and centrifuged at 3,800g for 5 minutes at 4 °C. The upper organic phase contained lipids, the lower aqueous phase contained metabolites, while proteins were precipitated at the bottom of the well. For QC, three reference samples (40 µl volumes) and a control lacking any sample, were processed in parallel per plate. For metabolite preparation, proteins were further precipitated by adding 500 µl of 1:1:1 acetone:acetonitrile:methanol spiked-in with 15 labeled metabolite internal standards to 300 µl of the aqueous phase and 200 µl of the lipid phase and incubated overnight at −20 °C. Following centrifugation at 3,800g for 10 minutes at 4 °C, the metabolic extracts were dried down to completion and resuspended in 200 µl 50:50 methanol:water for liquid chromatography (LC)– MS analysis. One outlier sample from D2 was removed due to poor data quality. [00393] Sample preparation for targeted lipidomics. Lipid extracts were analyzed using a Sciex platform that comprised a 5500 QTRAP system equipped with a SelexION differential mobility spectrometry (DMS) interface (Sciex) and a high flow LC-30AD solvent delivery unit (Shimazdu). Lipid molecular species were identified and quantified using multiple reaction monitoring (MRM) and positive–negative ionization switching. Two acquisition methods were employed covering 13 lipid classes; method 1 had SelexION voltages turned on, whereas method 2 had SelexION voltages turned off. High data quality was ensured by (1) tuning the DMS 227 314164997v1 Attorney Docket No: 243735.000437 compensation voltages using a set of lipid standards (cat. no.5040141, Sciex) after either each cleaning procedure, more than 24 h of standing idle or 3 days of consecutive use; (2) performing a quick system suitability test (cat. no.5040407, Sciex) before each batch to ensure an acceptable limit of detection for each lipid class; and (3) triplicate injection of lipids extracted from a reference plasma sample (cat. no.4386703, Sciex) at the beginning of each batch. [00394] Data acquisition for plasma metabolites: Metabolite extracts were analyzed using a broad-spectrum untargeted LC-MS platform as previously described (Contrepois, et al., 2015) whereas complex lipids were quantified using a targeted MS-based approach (Contrepois, et al., 2018). [00395] Untargeted metabolomics by LC-MS: Metabolic extracts were analyzed in quadruplicate using HILIC and reverse-phase LC (RPLC) separation in both positive and negative ionization modes. Data were acquired on a Thermo Q Exactive HF mass spectrometer for HILIC (Thermo Fisher) and a Thermo Q Exactive mass spectrometer for RPLC (Thermo Fisher). Both instruments were equipped with a HESI-II probe and operated in full MS-scan mode. MS/MS data were acquired on QC samples consisting of an equimolar mixture of all samples in the study (global reference pool). HILIC experiments were performed using a ZIC- HILIC column 2.1 × 100 mm, 3.5 µm, 200 Å (Millipore) and mobile-phase solvents consisting of 10 mM ammonium acetate in 50:50 acetonitrile:water (A) and 10 mM ammonium acetate in 95:5 acetonitrile:water (B). RPLC experiments were performed using a Zorbax SBaq column 2.1 × 50 mm, 1.7 µm, 100 Å (Agilent) and mobile-phase solvents consisting of 0.06% acetic acid in water (A) and 0.06% acetic acid in methanol (B). Data quality was ensured by (1) injecting 6- and 12-pool samples to equilibrate the LC–MS system before running the sequence for RPLC and HILIC, respectively; (2) injecting a pool sample every ten injections to control for signal deviation with time; and (3) checking mass accuracy, retention time and peak shape of internal standards in each sample. [00396] Data acquisition for lipidomics: Lipidomics data were reported by Shotgun Lipidomic Assistant (v.1.21) (Su, et al., 2021) software, which calculates concentrations for each detected lipid as the average intensity of the analyte MRM/average intensity of the most structurally similar internal standard (IS) MRM multiplied by its concentration. Lipids detected in fewer than two-thirds of the samples were discarded and missing values were imputed by drawing from a 228 314164997v1 Attorney Docket No: 243735.000437 random distribution of low values class-wise in the corresponding sample. Abundances were reported in nmol g−1. [00397] Data processing for plasma metabolomics: Data from each mode were independently analyzed using Progenesis QI software (v.2.3) (Nonlinear Dynamics). Metabolic features from blanks and those that did not show sufficient linearity upon dilution in QC (r < 0.6) were discarded. Only metabolic features present in more than two-thirds of the samples were retained. Missing values were imputed as above. Intensity drift was corrected. Data from each mode were merged and features annotated as follows: peak annotation was first performed by matching experimental m/z, retention time, and MS/MS spectra to an in-house library of analytical-grade standards. Remaining peaks were identified by matching experimental m/z and fragmentation spectra to publicly available databases, including HMDB, MoNA, MassBank, METLIN, and NIST using the R package ‘metID’ (v.0.2.0). The Metabolomics Standards Initiative level of confidence was used to grade metabolite annotation confidence (levels 1–3). Level 1 represents formal identifications where the biological signal matches accurate mass, retention time, and fragmentation spectra of an authentic standard run on the same platform. For level 2 identification, the biological signal matches accurate mass and fragmentation spectra available in one of the public databases listed above. Level 3 represents putative identifications that are the most likely name based on previous knowledge. Metabolite abundances were reported as spectral counts. [00398] Cytokine panels: C-reactive protein (CRP), D-dimer, ferritin, lactate, lactate dehydrogenase, interferon-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-13 and IL-17 and soluble IL-2R and TNF, were measured in both xenograft transplant decedents as previously described (Moazami, et al., 2023) and using the same time points as above, for the Multiplex Human Cytokine/Chemokine/Growth Factor Panel A 48 Plex kit (Millipore Sigma) run on the Luminex 200 System (Millipore Sigma). A total of 25 µl plasma was used for each time point and assayed in duplicate, following the standard manufacturer’s protocol. Millipex Analyst v.5.1 software was used for standard curve generation and data analysis. [00399] DEA for proteomics, metabolomics and lipidomics: Data quality was first examined using a distribution plot. PCA plots were generated to identify potential batch effects. For normalization, the values were scaled to have the same median value using normalizeMedianValues function from the limma package (v.3.54.2), followed by log2 229 314164997v1 Attorney Docket No: 243735.000437 transformation (Ritchie, et al., 2015). Only features with less than 50% missing values for the downstream analysis were considered. For DEA, first time points were grouped into early (6 and 12 h), mid (18 to 36 h) and late stages (after 36 h). DEA was performed for D1 and D2 separately. Then DEA was performed using a linear model (~individual + stage) followed by empirical Bayes moderation with the limma package. [00400] Integrative analysis methodology: Processed bulk RNA-seq (PBMCs) and cytokine, metabolomic, lipidomic and proteomic data from plasma were normalized and scaled to z-scores. Integrative analysis was performed using fuzzy c-means clustering with the mfuzz (v.2.62) package to identify multi-omics analytes that had similar temporal expression patterns across the heart transplant time course. This was performed for each xenotransplant time course independently as well as for combined data after normalizing and standardizing the bulk RNA- seq, proteomic, lipidomic and metabolomic datasets into a unified integrative data matrix (Cannon, et al., 1986). The downstream multi-omics clusters were manually evaluated for trends that correlated with clinical events in the time course and relevant clusters were selected for further analysis. To test for pathways enriched in the individual clusters, pathway analysis was performed using Metaboanalyst v.5.0 (using the joint protein/metabolite analysis function). [00401] Histological evaluation: The xenograft heart samples were dissected and fixed in 10% neutral buffered formalin (Fisher Scientific) for 48 h and transferred to 70% ethanol. The xenograft tissues were processed in a Leica ASP300 (Leica Biosystems) following a 1 h, 13-step program, and paraffin-embedded using standard procedures. The 5-µm tissue sections were collected onto Plus slides (Fisher Scientific), air dried and stored at room temperature before use. Each sample was histochemically stained with H&E and Masson’s trichrome (Carson). H&E stains and EM were assessed by an expert clinical transplant pathologist. [00402] Transmission EM: Explanted heart tissues were fixed and stored in 2.5% glutaraldehyde and 2% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4) at 4 °C. The samples were washed three times with 0.1 M phosphate buffer (10 minutes each time), post-fixed with 1% osmium tetroxide for 1.5 h at room temperature and dehydrated in a serial of ethanol solutions (30%, 50%, 70%, 85%, 95%, and 100% for 15 min each). The heart tissue was then washed in propylene oxide twice for 5 min each, infiltrated with propylene oxide and embedded in EMbed 812 (Electron Microscopy Sciences). Semi-thin sections were cut at 500 nm and stained with 1% toluidine blue to find the best orientation of the heart tissue. Ultrathin sections 230 314164997v1 Attorney Docket No: 243735.000437 of 70 nm were cut, mounted on 200-mesh copper grids, stained with uranyl acetate and lead citrate by standard methods, and imaged with a JEOL 1400 Flash transmission electron microscope (Japan) using a Rio16 camera (Gatan). [00403] Flow cytometry: PBMC samples were thawed and rested for 1 h at 37 °C in RPMI supplemented with 10% FBS. Then, cells were treated with Human TruStain FcX (BioLegend) and NovaBlock (Thermo Fisher Scientific) for 10 min at room temperature. Subsequently, surface antibody staining was performed at room temperature for 20 minutes in the dark for the following antibodies: CD4 (1:300 dilution, clone SK3, BioLegend, 344851); CD8 (1:1,000 dilu- tion, clone SK1, BioLegend, 344762); CD14 (1:1,000 dilution, clone M5E2, BD, 612902); CD16 (1:1,000 dilution, clone 3G8, BD, 566171); CD19 (1:100 dilution, clone SJ25C1, BD, 612939); CD27 (1:100 dilution, clone O323, BioLegend, 302833); CD38 (1:300 dilution, clone HIT2, BioLegend, 303549); CD56 (1:50 dilution, clone 5.1H11, BioLegend, 362539); HLA-DR (1:1,000 dilution, clone G46-6, BD, 612980); and LiveDead Blue (Thermo Fisher, L23105). PBMCs were then permeabilized with the eBioscience Foxp3/ Transcription Factor Staining Buffer Set (Thermo Fisher) for 20 minutes at room temperature in the dark, followed by 1 h intracellular staining at room temperature in the dark for the following antibodies: CD3 (1:500 dilution, clone SK7, BioLegend, 344851). Cells were resuspended in 1% paraformaldehyde (Electron Microscopy Sciences). All samples were acquired on a five-laser Aurora cytometer (Cytek Biosciences) and data were analyzed using FlowJo (v.10.10.0, BD Biosciences). [00404] Data availability: Count matrices for the generated data (PBMC scRNA-seq, PBMC bulk RNA-seq, tissue snRNA-seq, tissue spatial transcriptomics, proteomics, lipidomics and metabolomics data) are available on Zenodo (Schmauch, Datasets, 2024). [00405] Code availability: Custom scripts and code are deposited on Zenodo (Schmauch, Scripts, 2024). [00406] Below are the methods used in Examples 7-12 described above. [00407] Data and code availability: All de-identified single-cell RNA-seq and bulk RNA-seq sample data from this study have been deposited to the GEO portal. De-identified data of sequenced samples and all original code used for analysis in this study has been deposited in a Github repository: boxialaboratory/Pig-to-Human-Kidney-Xenotransplantation. 231 314164997v1 Attorney Docket No: 243735.000437 [00408] Experiment models and subject details: Both recipients of the xenografts were declared brain-dead according to standard criteria at site of hospitalization, and were described in the clinical report (Montgomery, Stern, et al., 2022). Pigs genetically modified for the alpha-1,3- galactosyltransferase gene knock-out genotype were supplied by Revivicor. [00409] Sample collection: Ethics statements, regulatory details, organ selection and procurement, and xenotransplantation procedures were previously described for the xenografts analyzed in this study (Montgomery, Stern, et al., 2022). Briefly, the transplanted and contralateral control kidneys went through the same procedure for storage and transportation. When one kidney was transplanted to the recipient, the contralateral control kidney was kept in cold preservation solution for storage and acted as a backup. Kidney tissues from xenograft and the untransplanted kidney in both cases were collected in parallel at the end point of the study (54 hours). Longitudinal core biopsy samples were collected in xenotransplantation case 2 at 0h from untransplanted control kidney (with two technical replicate samples), and then at 12h, 24h, and 48h from the xenograft (with one biopsy sample). PBMC samples were collected from the recipient in case 2 at the following timepoints: pre-transplant (0h) and then at 6h, 12h, 24h, 48h, and 53h (right before termination) post-xenotransplantation. [00410] Sample preparation for bulk and single-cell RNA-seq: Freshly collected kidney tissues were minced rapidly in a cell culture dish followed by washing with DPBS buffer. Minced tissue was resuspended in 5mL of 37°C pre-warmed 0.25% Trypsin plus TURBO DNase (10U/mL final concentration) for 20 min with mechanical dissociation using serological pipette every 5 min. Following digestion, dissociation mix was filtered through a 100µm strainer. The remaining large tissues on the strainer were further ground and washed to collect additional cells. Cell debris and dead cells were removed using Miltenyi Biotech Dead Cell Removal Kit. Cells were counted using Countess II Automated Cell Counter before loading for single-cell RNA-seq using 10X Genomics Chromium Single Cell 3' Kit (v3.1 Chemistry). [00411] For bulk RNA-seq, kidney core biopsy samples were homogenized using a pestle homogenizer and total RNA was extracted from lysate using a QIAGEN RNeasy Plus kit. Total RNA quality and quantity were examined using the RNA 6000 Nano Kit and Agilent 2100 Bioanalyzer. Bulk RNA-seq library was constructed following the Illumina Ribo-Zero Plus rRNA Depletion Kit protocol (protocol number: 20037135). 232 314164997v1 Attorney Docket No: 243735.000437 [00412] Longitudinal single-cell RNA-seq of the recipient’s PBMCs were prepared at the same time after collecting all samples across timepoints. Fresh PBMCs of each timepoint were first collected through gradient centrifugation and any remaining RBCs were removed using the ACK lysing buffer followed by washing by the DPBS buffer. The prepared PBMCs for each time point were cryopreserved according to the 10X Genomics protocol (protocol number: CG00039). Upon collecting all PBMC samples, the frozen cells were thawed at the same time, then loaded for single-cell RNA-seq using 10X Genomics Chromium Single Cell 3' Kit (v3.1 Chemistry). [00413] Data pre-processing and quality control of scRNA-seq: Raw single-cell sequencing data were processed with the CellRanger pipeline of 10X Genomics to generate single-cell raw gene expression matrices. Kidney single-cell sequencing data were separately mapped to both human genome (GRCh38) and porcine genome (Sscrofa11) to distinguish species origin of cells, whereas recipient PBMC single cell data were mapped to human genome (GRCh38) only. Single-cell raw expression matrices were then processed and analyzed using Seurat v4.3.0 (Butler, et al., 2018). Cells with less than 500 expressed genes, 1000 UMI, or more than 8% of transcripts from mitochondrial genes were removed from the kidney single cells, resulting in a total of 23,868 cells. A library of homologous genes between human and pig, obtained from the HGD Database (Duan, et al., 2023), was used as a reference to subset the filtered gene expression data for downstream analyses. Within the scope of gene expression of human-porcine one-to-one homologous genes, single cells with greater proportion of transcripts mapped to the porcine genome than the human genome are identified to be porcine cells, and vice versa to be human cells. PBMC single-cell RNA-seq datasets were filtered by removing extra-low-quality cells with less than 200 expressed genes or higher than 20% of mitochondrial genes, resulting in a total of 31,321 cells across 6 time points. [00414] Data normalization, integration, and cell type annotation: Each filtered dataset obtained from the kidneys and PBMC samples were normalized using SCTransform v2 from Seurat followed by integration using most commonly deemed variable features within each dataset (Stuart, et al., 2019). Dimensionality reduction analyses were performed on integrated kidney and PBMC datasets separately using Principal Component Analysis (PCA: npcs = 30) and Uniform Manifold Approximation and Projection (UMAP: dims = 1:30). Cell clusters were generated using the original Louvain algorithm with a resolution of 0.7 for integrated kidney 233 314164997v1 Attorney Docket No: 243735.000437 dataset and 0.5 for the integrated PBMC dataset. Individual clusters were annotated based on the expression of cell type-specific markers. [00415] Differential expression analysis: Differentially expressed genes between the control and xenograft samples were assessed using the Seurat FindMarkers function, whose transcripts were detected in at least 20% of cells, with a log-fold-change threshold of 0.25, and each identified DEG were tested for statistical significance using the Wilcoxon rank sum test. Differentially expressed genes with log2 fold change > 0.5, and p-value < 0.01 were filtered as genes of interest for further analysis. [00416] Cell cycle analyses: Cell cycle scoring was applied to assess the cycling status of single cells. CellCycleScoring function of Seurat computes a score for each cell cycle phase (G1, G2/M and S) and clustering cells into the three phases of cell cycles by summing the expression levels of the phase-specific marker genes. The cell cycle scores and phase assignments were annotated for each cell, and followed by secondary validation of cell cycle marker gene expressions. [00417] Temporal gene set coexpression clustering analysis: To minimize the effect of noise on temporal gene expression values in PBMC data, the corrected UMI counts matrix adjusted for sequencing depth from the SCT assay was used, normalized by gene using centered log ratio transformation by applying the Seurat NormalizeData function, then scaled using Seurat ScaleData function for each cell type independently. For each cell type, a set of temporally variable genes was identified by using Seurat FindVariableFeatures and mitochondrial genes (MT), ribosomal genes (RP), or genes whose normalized expression was detected in < 20% of all cells within the given cell type were filtered. The 6 average Pearson residual values of each filtered temporally variable genes at the 6 time points generated by the AverageExpression function was used to calculate euclidean distance matrices between filtered temporally variable genes for each cell type. [00418] Temporally enriched gene set identification: In each cell type, co-expressed gene sets with strong evidence of temporal enrichment were identified. To do so, filtering was performed for gene sets whose average scaled expression level at a given time point was larger than the average scaled expression level across all previous time points by 0.5. Further, gene sets were removed that were composed of less than 20 genes or were supported by less than 20 cells in the corresponding cell type at the time point of gene set enrichment. These parameters 234 314164997v1 Attorney Docket No: 243735.000437 identified 13 cell types with at least one gene set showing temporal enrichment at 12 hours post- xenotransplantation (pXTx), and 8 cell types at 48-53 hours pXTx. Enriched gene sets identified from erythrocytes at both time points were excluded from visualization. [00419] Gene set enrichment analysis (GSEA) for functional annotations: All Gene Ontology (GO) term (Ashburner, et al., 2000) and Kyoto Encyclopedia of Genes and Genomes (KEGG) term (Kanehisa, et al., 2000) enrichment analyses were performed using clusterProfiler (v4.8.2) (Wu, et al., 2021). GO terms generated from the least number of overlapping terms were visualized for GO term analyses of PBMC data. All p-values in the GSEA were adjusted for multiple tests using the Benjamin-Hochberg method (Benjamini, et al., 1995). [00420] Analyzing longitudinal bulk RNA-seq of kidney biopsy samples: The bulk RNA-seq data across 0, 12, 24, and 48 hours pXTx was analyzed to understand the temporal changes in gene expression between the control and xenograft samples. Two technical replicates were conducted at 0 hours post-transplantation from the control kidneys. Gene expression at time 0 was calculated by the average value between the two replicates. The log2 fold change in gene expression was calculated for each gene across the different time points relative to the expression value at time 0. To calculate the human to pig raw count reads ratio (H/P Ratio), the absolute raw reads mapped to the human genome for every gene at a specific time point was divided by the absolute raw reads mapped to the porcine genome. [00421] Below are the methods used in Examples 13-18 described above. [00422] Clinical subject: The decedent was a 57-year-old male who died from complications related to a biopsy performed on grade 4 glioblastoma. He was declared dead based on neurologic criteria, and was ruled out as an organ donor due to the glioblastoma. [00423] Clinical samples and data: Figure 44 outlines the different omics assays performed at each timepoint, along with immunosuppressive medications, clinical microbiological testing, and significant clinical events. [00424] Pig xenograft organ selection and procurement: The genetically modified pig GalSafe™ thymokidney was supplied by Revivicor. The pig thymus was implanted under the renal capsule of the same pig approximately five months prior to organ procurement, as previously described (Lambrigts, et al., 1996; Yamada, et al., 1999). 235 314164997v1 Attorney Docket No: 243735.000437 [00425] Spatial RNA expression profiling: For the 478 probes panel: Spatial RNA expression profiling within transplanted pig kidney tissue was performed using a custom 478-gene panel on a Xenium In Situ Analyzer (10x Genomics). A total of seven biopsies acquired over the full time-course were assessed (Figure 44). Briefly, unstained formalin-fixed paraffin-embedded (FFPE) sections of 5 μm thickness were affixed to Xenium slides (10x Genomics, PN 1000460). Slides were dried overnight at 37°C and subsequently stored in a desiccator at room temperature (RT) for approximately 4 days. Tissue processing/labeling and mRNA detection were carried out by following the Xenium In Situ manufacturer protocols CG000578 (Rev. C), CG000580 (Rev. C), CG000582 (Rev. E), and CG000584 (Rev. C) (10x Genomics). Xenium probes for 478 mRNAs, encompassing 129 human and 349 pig cell-specific markers and additional genes of interest (outlined in Tables 10-11), were designed according to the manufacturer’s instructions (10x Genomics). The custom probe set was then used to label and detect mRNAs in kidney tissues. Seven xenograft samples were processed and analyzed across two runs, according to the protocols described above. Regions of interest were selected on the Xenium Analyzer to include the totality of kidney tissue within each section. A second serial tissue section was stained with hematoxylin and eosin (H&E) and imaged using bright-field microscopy. For analysis, nuclei identification and cell segmentation were performed using Xenium Analyzer software to generate a cell/gene count matrix. This dataset was subsequently analyzed in python using Squidpy (Palla, et al., 2022) and Scanpy (Wolf, et al., 2018). All samples were loaded using the scanpy.read_10x_h5 function on the cell_feature_matrix.h5. Transcripts with a nucleus distance parameter greater than 0.1 were excluded from analysis in order to minimize transcript-cell misassignment and cells with less than 20 assigned transcripts were excluded. To separate pig and infiltrating human cells, a human_expression_fraction metric was subsequently calculated for each cell, defined as the fraction of raw counts originating from human transcripts. A cell was considered to be of human origin when more than half of the transcripts originated from human probes. The count matrix was normalized and logarithmized. For cell typing, human and pig cells were separated into two independent matrices, where pig genes were removed from the pig matrix and human genes from the human matrix. PCA and UMAP (Uniform Manifold Approximation and Projection) (McInnes, et al., arXiv, 2018) algorithms were then applied (including UMAP-knn graph creation). From the knn graph, clusters were discovered using the Leiden (Traag, et al., 2019) algorithm, resulting in human and pig cell embeddings. Cell type 236 314164997v1 Attorney Docket No: 243735.000437 labels were applied based on gene marker expression (Figures 59-60). Cell-type colocalization analysis was performed independently for each sample using the gr.nhood_enrichment function of Squidpy. The resulting enrichment counts were then scaled across samples. For subclustering analysis, a subset of the count matrix (normalized and logarithmized) containing only cells of the population of interest was created. Subsequently, PCA, UMAP, and leiden were reapplied, resulting in subclusters. For the analysis overlap with Loupy, et al. (Loupy, et al., 2023) results (Figures 56A-D), differentially expressed genes (DEGs) from that study (comparing pre- and post-transplant xenografts) were analyzed for overlap with cells in the dataset herein. Among the Xenium 480 panel genes, the antibody-mediated rejection (AbMR) signature, interferon-gamma response, and monocyte/macrophage activation genes were included in the analysis. To define AbMR-positive cells, five genes (HLA-DRA, HLA-DRB1, CXCL9, HLA-DPA1, HLA-DPB1) from the Loupy, et al. AbMR signature and present in the assay herein were selected. The average expression per cell was calculated, and cells were classified as “AbMR” or “Other” based on a 0.9 log-normalized expression threshold. This binary classification was then spatially mapped to Day 33 tissue and analyzed across time points. Table 10.478 ST Xenium panel design. List of the 478 genes constituting the panel, with the genome name (GRCh38 for human, Sscrofa11 for pig), their symbol, and Ensembl IDs. Species Gene_name Gene_id Species Gene_name Gene_id GRCh38 A2M ENSG00000175899 Sscrofa11 ENSSSCG00 ENSSSCG00000013788 4 1 4 4 9 0 0 9 7 0 237 314164997v1 Attorney Docket No: 243735.000437 GRCh38 CAMK4 ENSG00000152495 Sscrofa11 FCGRT ENSSSCG00000003187 GRCh38 CCR5 ENSG00000160791 Sscrofa11 FCN2 ENSSSCG00000023333 97 2 7 5 0 7 3 6 7 7 6 3 3 6 8 8 1 7 9 1 0 1 3 0 2 3 6 9 3 4 5 5 1 9 5 9 4 0 2 3 0 238 314164997v1 Attorney Docket No: 243735.000437 GRCh38 HLA-DPA1 ENSG00000231389 Sscrofa11 IGFBP7 ENSSSCG00000008913 GRCh38 HLA-DPB1 ENSG00000223865 Sscrofa11 IGHM ENSSSCG00000037775 94 5 4 0 2 1 6 5 6 3 2 5 5 7 4 3 3 5 9 5 0 2 5 4 2 1 8 2 2 6 8 2 6 7 2 8 1 6 0 6 2 239 314164997v1 Attorney Docket No: 243735.000437 GRCh38 NARF ENSG00000141562 Sscrofa11 MARCHF1 ENSSSCG00000039175 GRCh38 NFKB1 ENSG00000109320 Sscrofa11 MECOM ENSSSCG00000011743 49 5 3 2 7 8 9 7 6 7 6 7 7 8 2 1 5 8 4 1 6 0 6 6 1 6 0 1 7 1 8 6 6 4 2 8 5 1 7 7 240 314164997v1 Attorney Docket No: 243735.000437 Sscrofa11 AKR7A2 ENSSSCG00000003491 Sscrofa11 PI16 ENSSSCG00000001570 Sscrofa11 ALB ENSSSCG00000008948 Sscrofa11 PIGR ENSSSCG00000015657 95 5 2 9 7 9 1 4 8 9 1 7 9 4 3 6 1 6 1 1 7 4 3 7 2 3 0 4 2 4 3 6 8 9 0 0 8 8 6 7 8 241 314164997v1 Attorney Docket No: 243735.000437 Sscrofa11 CD79A ENSSSCG00000033684 Sscrofa11 SLC11A1 ENSSSCG00000039798 Sscrofa11 CD84 ENSSSCG00000006381 Sscrofa11 SLC12A1 ENSSSCG00000004659 43 9 6 5 2 8 3 7 2 5 1 3 6 3 3 7 1 7 2 3 2 4 0 1 2 0 9 2 2 7 0 3 2 1 5 4 4 8 7 5 9 242 314164997v1 Attorney Docket No: 243735.000437 Sscrofa11 EGFL7 ENSSSCG00000005828 Sscrofa11 TRAF1 ENSSSCG00000005511 Sscrofa11 EGFLAM ENSSSCG00000016848 Sscrofa11 TRDN ENSSSCG00000027613 15 7 9 7 9 4 2 3 0 2 3 4 T abe . 8 S enum pane desgn. eta s on gene annotaton. Pig genes OVERALL SUB- genes genes genes genes genes genes genes genes RL 1 3 243 314164997v1 Attorney Docket No: 243735.000437 Heart_Xenog L3 DOCK2 BANK1 TBXAS SLC11 MARC ITGAL ENSSS KCNK1 raft_paper 1 A1 HF1 CG0000 3 3 P R 2 1 11 1 244 314164997v1 Attorney Docket No: 243735.000437 Gene_set Allograft_reje NLRP3 TLR2 ction 1 2 A R1 245 314164997v1 Attorney Docket No: 243735.000437 KIDNEY IC_A SLC26 SLC4A ATP6V A7 1 0A4 246 314164997v1 Attorney Docket No: 243735.000437 LIVER HLA Class II CD74 HLA- HLA- DRA DOB 7 247 314164997v1 Attorney Docket No: 243735.000437 Human_activi Immune_activ IL2 IL15RA IL21R FASLG IL2RA CD69 FOSL1 ty ity ; ercyes; , c wann e s; , ro ass; , moo musce ce s/ rera ce s; ep ron_ , Nephron Proximal Tubule; PT, Proximal Tubule; PT_prolif, Proximal Tubule Proliferative; PT_VIM+, Proximal Tubule VIM+; TAL, Thick Ascending Limb; NK-T, Natural Killer cells or T cells; T, T lymphocytes; IC_A, Intercalated Cells type A; ICB, Intercalated Cells type A; HEP, Hepatocytes; CC, Cholangiocytes; EC, Endothelial cells; HSC, Hepatic Stellate Cells; LP, Lymphoid cells; B, B lymphocytes; TNK, T lymphocytes and Natural Killer Cells; NK, Natural Killer Cells; CD4T, CD4+ T cells; CD8T, CD8+ T cells; FOS+T, T cells expressing FOS; Treg, Regulatory T cells; GZMK+T, T cells expressing GZMK. **References used include: Schmauch, et al., 2024; Pan, et al., 2024; Gene Set Enrichment Analysis (GSEA), 2004-2023; Payen, et al., 2021; Aizarani, et al., 2019. [00426] For the 5k panel: Kidney xenograft core biopsies were collected at multiple time points, including post-reperfusion (POD 0), POD 21, POD 33, POD 45, and a wedge biopsy of the explant (POD 61). Additionally, a biopsy from the pig’s contralateral kidney and decedent’s native kidney were included. All samples underwent spatial transcriptomic analysis using the Xenium Prime 5K Human Pan Tissue & Pathways Panel, supplemented with a custom set of 100 added targets. These probes were designed to distinguish pig kidney cell populations, incorporating markers from a previously defined 478-gene panel along with additional pig- specific genes identified through snRNA-seq analysis (described herein). Details of this panel can be found in Tables 12-13. Two additional APPV targets were included as part of the 100 targets for the positive and negative sense strands using p7 (2 probes), NS3 (4 probes) and NS5 genes (4 probes) for a total of 10 independent probes.5uM sections from each FFPE block were used for H&E staining and processing on the Xenium platform following the 10x Genomics Xenium In Situ for FFPE – Deparaffinization & Decrosslinking protocol and the Xenium Prime In Situ Gene Expression with optional Cell Segmentation Staining protocol (10x Genomics). Data processing was performed the same as described for the 478-panel (described herein) with the modification that cells with fewer than 70 transcripts were excluded for the larger panel. Moreover, no nuclear distance filter was applied on counts in this dataset, as the cell segmentation process was improved in this version of the method. 248 314164997v1 Attorney Docket No: 243735.000437 Table 12. Details of the 5k pre-designed human genes. Gene Ensembl Gene Ensembl Gene Ensembl Gene Ensembl Name ID Name ID Name ID Name ID ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ABCG2 0118777 DVL2 0004975 LRRTM1 0162951 ROR1 0185483 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 250 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ACTBL2 0169067 EDNRB 0136160 MAF 0178573 RRS1 0179041 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 251 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ADAR 0160710 EID1 0255302 MAP1B 0131711 RXFP1 0171509 ENSG0000 ENSG0000 MAP1LC3 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 252 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ADORA3 0282608 ELK1 0126767 MAPK15 0181085 SBNO1 0139697 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 253 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 AHCY 0101444 ENPP3 0154269 MCHR2 0152034 SDC3 0162512 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 254 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ALDOA 0149925 EPS8L3 0198758 MEIOB 0162039 SENP1 0079387 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 255 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ANGPTL3 0132855 ESRRA 0173153 MICB 0204516 SF3B1 0115524 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 256 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 AOAH 0136250 F8 0185010 MMP13 0137745 SHBG 0129214 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 257 314164997v1 Attorney Docket No: 243735.000437 APOBEC3 ENSG0000 ENSG0000 ENSG0000 ENSG0000 C 0244509 FANCD2 0144554 MRAP2 0135324 SIRPB1 0101307 APOBEC3 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 258 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ARHGEF7 0102606 FCGR1A 0150337 MTAP 0099810 SLC17A8 0179520 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 259 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ASTN2 0148219 FEZF2 0153266 MYB 0118513 SLC26A2 0155850 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 260 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 ATP5F1C 0165629 FIP1L1 0145216 MYO5A 0197535 SLC5A3 0198743 ATP6V1B ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 261 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 BAALC 0164929 FOLR1 0110195 NBPF3 0142794 SLITRK2 0185985 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 262 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 BCCIP 0107949 FPR1 0171051 NDUFA3 0170906 SMOC2 0112562 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 263 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 BFSP1 0125864 FYN 0010810 NEURL4 0215041 SNURF 0273173 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 264 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 BNC1 0169594 GABRQ 0268089 NIPSNAP1 0184117 SOST 0167941 ENSG0000 ENSG0000 NIPSNAP3 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 265 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 C1QBP 0108561 GBP1 0117228 NOC3L 0173145 SPAG8 0137098 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 266 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CACNA1H 0196557 GIGYF1 0146830 NPC1L1 0015520 SPON2 0159674 CACNA2D ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 267 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CAND1 0111530 GLS2 0135423 NR4A1 0123358 SSB 0138385 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 268 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CAT 0121691 GP5 0178732 NTN1 0065320 STC2 0113739 CATSPER ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 269 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CCDC113 0103021 GPR4 0177464 NXF1 0162231 STUB1 0103266 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 270 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CCL13 0181374 GRIN2C 0161509 OPALIN 0197430 SYNPO2 0172403 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 271 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CCND1 0110092 GTF2I 0263001 P2RX4 0135124 TASOR2 0108021 ENSG0000 ENSG0000 ENSG0000 TBC1D22 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 272 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CD151 0177697 HAS2 0170961 PALMD 0099260 TCF3 0071564 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 273 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CD300C 0167850 HDC 0140287 PBK 0168078 TES 0135269 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 274 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CD79A 0105369 HIPK3 0110422 PCNA 0132646 TGM2 0198959 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 275 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CDCA3 0111665 HOOK3 0168172 PDIA2 0185615 TIGIT 0181847 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 276 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CDK6 0105810 HRAS 0174775 PFKP 0067057 TLR9 0239732 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 277 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CENPA 0115163 HSPB8 0152137 PHLPP1 0081913 TMEM45A 0181458 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 278 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 TNFRSF13 ENSG0000 CGB3 0104827 ICAM4 0105371 PITPNB 0180957 C 0159958 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 279 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CHRNA6 0147434 IFNGR1 0027697 PLD1 0075651 TNPO3 0064419 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 280 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CLEC14A 0176435 IKBKG 0269335 PMAIP1 0141682 TPH1 0129167 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 281 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CMA1 0092009 IL18BP 0137496 POLR2C 0102978 TRIB3 0101255 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 282 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CNTROB 0170037 IL31 0204671 PPARA 0186951 TRPM6 0119121 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 283 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CORO1B 0172725 INHBA 0122641 PPP4R4 0119698 TSPAN9 0011105 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 284 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CREG2 0175874 IRF2BP1 0170604 PRKCG 0126583 TWIST1 0122691 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 285 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CSF3R 0119535 ITGAV 0138448 PROSER1 0120685 UBE4B 0130939 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 286 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CTNNA1 0044115 JPH1 0104369 PSIP1 0164985 UPF1 0005007 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 287 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CX3CR1 0168329 KCNJ1 0151704 PTGER3 0050628 VAPB 0124164 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 288 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 CYP19A1 0137869 KCNN4 0104783 PTPN22 0134242 VSIG4 0155659 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 289 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 DACT2 0164488 KHDRBS1 0121774 QRFPR 0186867 WDR77 0116455 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 290 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 DDX1 0079785 KLHL13 0003096 RALGPS1 0136828 XIRP1 0168334 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 291 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 DGAT1 0185000 KPRP 0203786 RBBP5 0117222 YWHAZ 0164924 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 292 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 DLEC1 0008226 LATS2 0150457 RCOR1 0089902 ZCCHC2 0141664 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 293 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 DNAH8 0124721 LIG3 0005156 RFX1 0132005 ZMYND10 0004838 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 294 314164997v1 Attorney Docket No: 243735.000437 ENSG0000 ENSG0000 ENSG0000 ENSG0000 DPF3 0205683 LMX1B 0136944 RIOX1 0170468 ZNF8 0278129 ENSG0000 ENSG0000 ENSG0000 ENSG0000 0 0 0 0 0 0 0 0 0 0 0 0 Annotation genes genes genes genes genes genes Dama ed cells CLDN1 CDH18 SOX9 COLEC11 295 314164997v1 Attorney Docket No: 243735.000437 Duct AQP2+ ACOXL LOC102160 043 04- [00427] Counts were normalized and log-transformed, and highly variable genes were identified and removed using Scanpy’s highly_variable_genes function with parameters (min_disp = 0.100, min_mean = 0.0050, max_mean = 6). The integrated matrix was scaled, followed by PCA and UMAP embedding. A k-nearest neighbor (k-nn) graph was constructed, and clustering was performed using the Leiden algorithm (resolution = 1.5), yielding a low-dimensional representation of all cells across samples (Figure 49D). Clusters mapping exclusively to the native human kidney samples were removed, as they represent human kidney parenchymal cells. All other human clusters were included in the final dataset as they contained human cells detected during the time-course. After reprocessing the remaining cells (from log-normalized counts), a human cell cluster was identified (Figure 49D) that was characterized by high human gene expression. Additionally, >99.9% of cells from the contralateral pig-only sample were located outside this cluster (Figure 49H), and 99% of remaining human kidney-derived cells were found within this cluster (Figure 49I). The human cluster was then further reprocessed, enabling the identification and removal of suspected pig-human doublets (Figures 49F-I), 296 314164997v1 Attorney Docket No: 243735.000437 resulting in the final selection of human cells. The human and pig populations were subsequently analyzed independently, retaining all probes for both cell populations. Reprocessing was conducted from the normalized log-transformed counts and included highly variable gene selection, standard scaling, PCA, k-nn, UMAP, Leiden clustering, and Harmony batch correction at the sample level. Cell-type level doublets were identified and removed based on distinct expression patterns, and cell types were assigned based on canonical marker gene expression (Figures 61–63). Subclustering was performed on specific human cell populations for visualization and population marker discovery (Figures 51A-D, 53A-C, 61E-G). For subpopulation marker gene enrichment in CXCL9⁺ macrophages and interferon⁺ cells, differentially expressed genes were identified using the Wilcoxon rank-sum test, selecting only those with positive log fold change (LFC) and false discovery rate (FDR) < 0.05. This resulted in 265 genes for the CXCL9⁺ macrophage population and 52 genes for the interferon⁺ population. Gene set enrichment analysis (GSEA) was performed using the gseapy (Fang, et al., 2023) enrichr function, with enrichment assessed against the GO_Biological_Process_2021 gene set, using all human genes from the 5K panel as the background (Figure 46J). For B-HOT scores (Figures 56G, 65A), each B-HOT gene set was used as the gene list in Scanpy’s score_genes function. The annotation gene sets and gene list defining the B-HOT panel were obtained from Mengel et al. (Mengel, et al., 2020). [00428] T- and B-cell repertoire sequencing: Total RNA was extracted from whole blood samples across 27 timepoints (see Figure 44) collected in PAXgene Blood RNA Tubes (BD Biosciences) using the PAXgene Blood RNA extraction kit (Qiagen). Purity was assessed using the ThermoFisher NanoDrop One. Purified RNA was quantified using the Qubit RNA Broad Range Assay Kit (ThermoFisher Scientific). The RNA Integrity Number (RIN) equivalent was determined using the RNA ScreenTape on the 4150 TapeStation (Agilent). TCR libraries were generated using the Takara SMARTER Human TCR a/b Profiling Kit v2 and BCR libraries were generated using the Takara SMART-Seq Human BCR (with UMIs) kit (Takara). Libraries were pooled and sequenced using the iSeq 100 and a 50 x 8 x 8 x 50 cycle run to allow for index balancing. Index balanced full-length VDJ, UMI, isotype (IgG, IgM, IgA, IgD, IgE) and TCR A/B pooled libraries were sequenced on NextSeq 2000 (600 cycle) sequencer using the P2 flow cell (Illumina) with 301 x 8 x 8 x 301 paired end cycles and all samples yielding sufficient high- 297 314164997v1 Attorney Docket No: 243735.000437 quality se MiXCR v Table 14. sr i a P da e R Q t s a F w a R POD 13,21 -02 4,450 POD 11,75 -03 4,962 POD 10,77 -05 9,044 POD 12,24 -07 8,545 POD 10,46 -09 2,225 POD 10,64 -11 4,638 POD 11,52 -13 4,865 % % % % % % % % % % % % % % % % % POD 10,8245.1953.430.77 0.00 80.371.70 1.20 0.10 3.43 41.990.00 0.00 0.00 1.65 75.970.08 1.56 1,961 -15 2,984 % % % % % % % % % % % % % % % % % POD 9,02350.4247.631.44 0.00 82.092.17 1.62 0.07 2.81 46.740.00 0.00 0.00 1.71 71.460.08 1.63 2,447 -17 ,179 % % % % % % % % % % % % % % % % % POD 8,82754.3144.110.78 0.00 84.042.60 1.73 0.10 3.44 49.630.00 0.00 0.00 2.22 69.670.10 2.12 2,626 -19 ,364 % % % % % % % % % % % % % % % % % POD 8,46045.8653.010.51 0.00 82.411.85 1.63 0.11 2.62 42.300.00 0.00 0.00 2.42 66.240.10 2.32 2,039 -21 ,082 % % % % % % % % % % % % % % % % % POD 10,6042.5856.010.82 0.00 80.521.74 1.58 0.09 3.05 39.500.00 0.00 0.00 1.79 71.390.09 1.70 2,119 -23 6,152 % % % % % % % % % % % % % % % % % POD 10,0940.1558.650.36 0.00 79.281.40 1.24 0.08 2.70 37.620.00 0.00 0.00 1.58 73.320.09 1.49 2,551 -25 4,565 % % % % % % % % % % % % % % % % % POD 11,5216.5882.990.22 0.00 77.330.75 1.68 0.12 2.24 15.450.00 0.00 0.00 1.47 76.220.14 1.32 795 -27 4,680 % % % % % % % % % % % % % % % % % 298 314164997v1 Attorney Docket No: 243735.000437 POD 11,0215.1084.440.32 0.00 77.390.87 2.43 0.03 2.97 13.860.00 0.00 0.00 1.52 74.900.16 1.36 818 -28 9,531 % % % % % % % % % % % % % % % % % POD 10,4718.1481.240.25 0.00 77.960.90 2.33 0.03 1.83 16.850.00 0.00 0.00 1.25 77.120.14 1.11 888 4 5 828 2 1 3 6 3 3 9 2 299 314164997v1 Attorney Docket No: 243735.000437 Table ri POD 2,2 -02 2,0 6 POD 2,0 -03 2,0 8 POD 2,1 -05 3,1 5 POD 2,4 -07 5,0 5 POD 2,6 -09 9,3 1 POD 3,7 -11 3,9 1 POD 3,80 23.9 75.3 0.46 0.00 94.0 1.94 7.57 0.39 3.61 21.5 0.00 0.00 0.00 1.27 76.4 0.26 1.02 109 71 -13 2,11 0% 8% % % 5% % % % % 4% % % % % 5% % % 2 POD 3,14 10.0 89.3 0.51 0.00 97.2 1.32 13.7 0.17 2.56 8.57 0.00 0.00 0.00 1.67 79.2 0.39 1.27 149 105 -15 9,14 6% 2% % % 0% % 5% % % % % % % % 7% % % 7 POD 2,27 36.1 63.2 0.38 0.00 95.1 4.40 7.60 0.00 4.54 30.9 0.00 0.00 0.00 1.76 75.7 0.25 1.50 377 220 -17 1,14 1% 5% % % 1% % % % % 4% % % % % 8% % % 5 POD 2,89 40.1 58.9 0.64 0.00 96.4 5.13 10.0 0.00 5.61 34.6 0.00 0.00 0.00 0.97 87.3 0.26 0.71 189 97 -19 0,74 9% 3% % % 5% % 9% % % 1% % % % % 4% % % 2 POD 3,56 52.5 46.7 0.48 0.00 94.9 6.76 7.32 0.00 8.13 44.9 0.00 0.00 0.00 1.42 78.3 0.19 1.23 537 403 -21 6,58 9% 2% % % 1% % % % % 5% % % % % 6% % % 1 300 314164997v1 Attorney Docket No: 243735.000437 POD 3,62 69.5 29.5 0.56 0.00 92.0 9.04 7.98 0.07 8.57 58.9 0.00 0.00 0.00 1.76 75.9 0.17 1.59 610 400 -23 2,29 2% 7% % % 5% % % % % 3% % % % % 6% % % 7 1 1 4 83 80 3 05 45 64 19 00 7 301 314164997v1 Attorney Docket No: 243735.000437 Overlapped paired-end reads: 76.63% Alignments that do not cover VDJRegion: 0.63% gradient centrifugation, followed by red blood cell lysis and cryopreservation according to the 10x Genomics protocol (protocol number: CG00039). Upon collection, the cryopreserved cells were thawed in four batches and then loaded for standard 10X Genomics single-cell RNA-seq (v3.1 Chemistry, 10x Genomics).22 PBMC scRNA-seq samples were obtained, each for a different timepoint. Initial scRNA-seq data processing was performed using the CellRanger pipeline with vendor-recommended workflows. After CellRanger preprocessing, the count matrices were processed using scanpy. Briefly, QC filters were applied as follows: a maximum of 50,000 counts, a maximum of 7,000 genes, a maximum of 20% mitochondrial gene expression, and a minimum of 500 genes. In addition, Scrublet (Wolock, et al., 2019) was used to identify doublets (cells were filtered out when their doublet score was greater than 0.15). Several levels of correction were applied, including linear regression of the number of unique molecular identifiers (UMIs), genes, and cell cycle score. Harmony was used for batch correction at the sample level to correct for technical batch effects, for both the main cell type and subtype analyses. Counts were normalized and logarithmized. For visualization and clustering, highly variable genes were selected using Scanpy's dedicated function and the integrated matrix was scaled. PCA and UMAP algorithms were then applied (including UMAP-knn graph creation). Clusters were identified from the knn graph using the Leiden algorithm. Cell types were identified based on marker genes and gene set enrichment, and specific cell type subtypes were identified and validated by comparing marker gene expression to Azimuth markers (specifically the PBMC dataset (Hao, et al., 2021)). To identify subpopulations of B, dendritic, T and NK 302 314164997v1 Attorney Docket No: 243735.000437 cells, subclustering analysis was performed. A subset of the count matrix (normalized and logarithmized) containing only cells of the population of interest was created. Subsequently, highly variable gene selection, PCA, UMAP and leiden were once again performed on the subpopulation, resulting in subclusters, which annotated based on their marker genes expression. This was done independently for each of the B, plasma, and dendritic cells populations and for the T/NK cells population. [00430] PBMC bulk RNA-seq: Bulk RNA-seq on PBMCs was performed to assess concordance with the scRNA-seq results. The 27 RNA samples previously extracted from whole blood stored in PAXgene Blood RNA tubes and used for immune repertoire sequencing also underwent whole transcriptome sequencing. WTS libraries were generated using the Illumina Stranded Total RNA Prep, Ligation with Ribo-Zero Plus library preparation kit (Illumina). Libraries underwent quantification using the KAPA Library Quant (Roche) Primers & Universal qPCR Mix and fragment analysis using the Agilent Fragment Analyzer 5200 (Agilent). Libraries were pooled and sequenced on a NovaSeq 6000 S4 flowcell (Illumina) with 101 x 10 x 10 x 101 paired end and indexed cycles. For data analysis, FASTQ files were aligned to human GRCh38 using the nf-Core RNA-seq pipeline (Ewels, et al., 2020), resulting in a TPM (transcript per million) count matrix. To assess overall trends in cell-type levels, min-max scaled TPM of specific marker genes were averaged for each cell-type/subtype of interest. [00431] Tissue bulk RNAseq: Kidney core biopsies collected on POD 0, 10, 14, 21, 28, 33, 45, 49, 56, 61 post-xenotransplantation were preserved in the RNA shield buffer (Zymo Research, ZR1100-50) until the end of the study. RNeasy Plus kit (QIAGEN, 74134) was used for total RNA extraction. RNA qualities were evaluated using an RNA quality and quantity nano assay kit on a bioanalyzer. RNA libraries were constructed using the TruSeq stranded total RNA, with Ribo Zero Gold library prep kit. RNA libraries were sequenced with an Illumina NovaSeq6000 SP 100 Cycle Flow Cell v1.5. For data analysis, FASTQ files were aligned to the mixed genome of Sus scrofa and human GRCh38 using the nf-Core RNAseq pipeline (Ewels, et al., 2020). Pig counts were then extracted for analysis using the mFuzzy R package to retrieve longitudinal clusters. The POD 56 sample was removed for QC reasons. [00432] Tissue snRNA-seq: OCT-embedded samples (POD 0, POD 10, POD 14, POD 21, POD 33, POD 49, POD 56, POD 61) were processed using the Miltenyi gentleMACs Octo Dissociator. Isolated nuclei were resuspended in 50 μL of 1x PBS (Phosphate Buffered Saline) 303 314164997v1 Attorney Docket No: 243735.000437 and fixed using the Scale Biosciences fixation kit (2020001) following the low input fixation protocol. Each fixed sample was distributed across the 96 wells of the first barcoding plate in the Scale Biosciences RNA kit (950884), with a target loading amount of 10,000 cells per well. Samples were taken through reverse transcription and then spread across the second barcoding ligation step. Finally, nuclei were distributed across the last barcoding plate at a count of 1,600 cells per well. Amplified material was pooled from all wells, purified and analyzed for concentration using the NEBNext Library Quant kit for Illumina (E7630L) and Agilent High Sensitivity D5000 screentape (5067-5593). The final purified pooled library was sequenced on a NovaSeq6000 targeting 20,000 reads per cell. Samples collected at POD 21 and POD 56 were excluded due to QC failure. Count matrices were generated using the ScaleRna pipeline v1.6.1 with parameters (starFeature="GeneFull_Ex50pAS", starMulti="PropUnique") and aligned to a composite human-pig genome (GRCh38 and Sscrofa11.1). CellBender (Fleming, et al., 2019) v3 was applied to the unfiltered matrices to remove empty droplets and ambient RNA contamination. The initial step involved the species-level partition of human and pig nuclei. Cells underwent a preliminary filtering step (≥50 detected genes), followed by normalization and log transformation of counts. Highly variable genes were identified using Scanpy, and the integrated matrix was scaled prior to PCA and UMAP embedding. A k-nearest neighbor (k-nn) graph was computed, and clustering was performed using the Leiden algorithm (resolution = 1.5). This analysis yielded a global low-dimensional representation of all nuclei across samples, allowing the identification of a cluster exhibiting a high expression of human genes (Figure 50C). Nuclei within this cluster were classified as human-derived, whereas all remaining nuclei were classified as pig-derived. Subsequent analyses were conducted separately for human and pig populations. For each, raw count data (prior to normalization and log transformation) were retrieved, retaining only human genes for human nuclei and pig genes for pig nuclei. QC-based filtering was then applied independently to both datasets as follows. Human nuclei: gene number cutoffs (max: 4,000, min: 100), UMI counts cutoffs (min: 200, max: 10,000), mitochondrial expression cutoff (max: 6%). Pig nuclei: gene number cutoffs (max: 6,000, min: 500), UMI counts cutoffs (min: 500, max: 20,000), mitochondrial expression cutoff (max: 6%). [00433] For pig nuclei only, additional filtering steps were applied. Doublet removal was performed using Scrublet (doublet score threshold = 0.1), and batch correction across samples was conducted using Harmony to mitigate technical batch effects. These corrections were not 304 314164997v1 Attorney Docket No: 243735.000437 applied to human nuclei. Clustering and dimensionality reduction were performed separately for each species-specific dataset, following the same pipeline used for the integrated dataset (highly variable gene selection, scaling, PCA, k-nn graph, UMAP embedding, and Leiden clustering). For cell type annotation, clusters were manually assigned based on canonical marker gene expression (Figures 64 and 66). Within the human dataset, lymphocyte and NK cell clusters were further subclustered by selecting relevant cells and reprocessing from the normalized, log- transformed counts, allowing for higher-resolution characterization of immune cell subsets (Figure 65). [00434] Flow cytometry: Cryopreserved PBMC were rapidly thawed and blocked with a combination of TruStain FcX (BioLegend #433302) and CellBlox (Fisher #B001T03F01) for 10 min at RT, followed by surface staining for 20 min at RT in the dark, permeabilization with Foxp3 permeabilization kit (Fisher #00552100) for 20 min at RT in the dark, and intracellular staining for 60 min at RT in the dark. Details on the antibodies used for staining are provided in Table 16. Samples were resuspended in 1% paraformaldehyde in PBS (Electron Microscopy Sciences #15700) prior to acquisition on a Cytek Aurora 5-laser cytometer. Data were analyzed using FlowJo v.10.10 (BD Biosciences). Table 16. List of antibodies used for flow cytometry. Antibody Clone Fluorochrome Supplier Catalog Lot number 305 314164997v1 Attorney Docket No: 243735.000437 Foxp3 PCH101 PE-Cy5.5 Invitrogen 35-4776-42 2065635 well plates in a randomized manner and processed on a SP100 Automation Instrument using two nanoparticle suspensions (XT1 and XT2) from the Proteograph XT Assay Kit, as per manufacturer’s instructions (Seer, Inc.). The resulting dried samples were directly analyzed by LC-MS/MS, where 8 µL of the reconstituted peptides (at a concentration of 50 ng/μL) were loaded onto an Acclaim PepMap 100 C18 (0.3 mm ID x 5 mm) trap column connected to an Ultimate 3000 HPLC System (Dionex). Peptides were separated on a 50 cm μPAC HPLC column (Thermo Fisher Scientific) at a flow rate of 1 μL/min using a gradient of 5–25% solvent B (0.1% FA (Formic Acid), 100% ACN) in solvent A (0.1% FA, 100% water) over 22 min, resulting in a total run time of 37 min. MS analysis was performed on an Orbitrap Astral mass spectrometer (Thermo Fisher). Approximately, ~380-400 ng of material per NP suspension was injected in Data Independent Acquisition (DIA) mode using m/z 3 isolation windows from m/z 380–980. MS1 scans were acquired at 240k resolution and MS2 at 80k resolution. LC-MS raw files were analyzed using the Proteograph Analysis Suite (PAS) with the data processed using DIA-NN 1.8.1 and a PAS library-free search with the cloud scalable Match Between Run (MBR) option applied. The analyses compared the results from three search strategies, including a combined pig and human FASTA database search and a separate individual FASTA database search for each species: Sus scrofa FASTA v11.1 and Homo sapiens FASTA version 2023.11.09. All identifications were reported at a False Discovery Rate (FDR) of 1%. Nanoparticle-level PSM (peptide spectrum matches) identifications were exported from PAS into a report for downstream analysis. [00436] To estimate the dynamics of abundance changes for each protein group in each of the Proteograph XT analyses (XT1 or XT2), the precursor intensities of peptides were fitted to the following penalized thin plate splines statistical model (in R notation): log₂ Intensity ~ s(timepoint, bs=”ts”, k=45) + precursor_id + plate_id + sample_composition, where the terms plate_id and sample_composition account for the experimental batch effects and refer to the Proteograph XT plate, and the variation in serum composition due to the sample collection (which was estimated using the factor analysis of the entire dataset), respectively. This statistical model was fitted using the BRMS v.2.20.3 R package, assuming that precursor intensities 306 314164997v1 Attorney Docket No: 243735.000437 (including the missing ones) have a Hurdle Normal-Cauchy mixture distribution. 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Establishment of pluripotent cell lines from porcine preimplantation embryos. Theriogenology.1999, 52(2):195-212. doi: 10.1016/S0093-691X(99)00122-3. 202. Takahashi, K., Yamanaka, S.. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell.2006, 126(4):663-76. doi: 10.1016/j.cell.2006.07.024. 203. Evans, M., Kaufman, M. Establishment in culture of pluripotential cells from mouse embryos. Nature 292, 154–156 (1981). doi: 10.1038/292154a0 204. Thomson, et al., Embryonic Stem Cell Lines Derived from Human Blastocysts. Science, 282, 1145-1147 (1998). doi: 10.1126/science.282.5391.1145 324 314164997v1 Attorney Docket No: 243735.000437 205. Loupy, A. et al. The Banff 2019 Kidney Meeting Report (I): Updates on and clarification of criteria for T cell– and antibody-mediated rejection. Am. J. Transplant. 20, 2318–2331 (2020). * * * [00437] The present invention is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description. Such modifications are intended to fall within the scope of the appended claims. [00438] All patents, applications, publications, test methods, literature, and other materials cited herein are hereby incorporated by reference in their entirety as if physically present in this specification. 325 314164997v1

Claims

Attorney Docket No: 243735.000437 Claims 1. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 2. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs); and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 3. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 326 314164997v1 Attorney Docket No: 243735.000437 4. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 5. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes IRF7, TLR7, TLR9, MYD88, and STAT1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 6. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 327 314164997v1 Attorney Docket No: 243735.000437 7. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 8. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 9. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD3G, THEMIS, CD5, and CD6, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 10. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: 328 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of CD8B and/or CD8A, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the gene(s) determined in step (a) to a corresponding control. 11. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes KLRG1, GZMK, CST7, and GZMH, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 12. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 13. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CD4, CD40LG, CCR2, CCR6, and DPP4, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and 329 314164997v1 Attorney Docket No: 243735.000437 b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 14. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 15. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes ITGAE, CD38, TNFRSF9, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 16. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GZMA, PRF1, and FASLG, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 330 314164997v1 Attorney Docket No: 243735.000437 17. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes TOP2A, TYMS, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 18. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes GBP1, IRF1, TAP1, WARS1, and IDO1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 19. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 20. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: 331 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 21. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 22. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: 332 314164997v1 Attorney Docket No: 243735.000437 a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 23. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 333 314164997v1 Attorney Docket No: 243735.000437 24. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 25. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 26. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 334 314164997v1 Attorney Docket No: 243735.000437 27. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 28. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 29. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, 335 314164997v1 Attorney Docket No: 243735.000437 IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 30. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, 336 314164997v1 Attorney Docket No: 243735.000437 STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 31. A method of detecting a xenograft rejection in a subject, or monitoring a xenograft rejection in a subject, or predicting a likelihood of a xenograft rejection in a subject, wherein the subject has received a xenograft transplantation, said method comprising: a) determining in a sample collected from the subject expression levels of two or more genes selected from 1) human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67, and 2) porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, 337 314164997v1 Attorney Docket No: 243735.000437 VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1, wherein the sample is isolated peripheral blood mononuclear cells (PBMCs) and/or a xenograft tissue biopsy; and b) comparing the expression levels of the two or more genes determined in step (a) to a corresponding control. 32. The method of any one of claims 1-31, wherein the method further comprises step c) (i) determining that the subject has a xenograft rejection or is at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are increased as compared to the control by 1.5-fold or more; or (ii) determining that the subject does not have a xenograft rejection or is not at a risk of a xenograft rejection when the expression levels of at least one of the genes determined in step (a) are decreased, not increased or increased by less than 1.5-fold as compared to the control. 33. The method of any one of claims 1-4, 6, 10-17, 19-23, and 29-32, wherein the control is a predetermined value or a value determined from a sample taken from the subject before the xenograft transplantation. 34. The method of any one of claims 1 and 3-32, wherein the control is a predetermined value or a value determined from a sample taken from the xenograft before the xenograft transplantation. 35. The method of any one of claims 1-6, 18-20, and 24-28, wherein the method further comprises administering to the subject a treatment that targets plasma cells and/or a complement inhibitory agent, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. 36. The method of any one of claims 9-14, wherein the method further comprises administering to the subject a treatment that targets T cells when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. 338 314164997v1 Attorney Docket No: 243735.000437 37. The method of any one of claims 7-8, 15-17, 21-23, and 29-31, wherein the method further comprises administering to the subject a treatment that targets plasma cells, a treatment that targets T cells, a complement inhibitory agent, or a combination thereof, when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. 38. The method of claim 35 or 37, wherein the treatment that targets plasma cells is plasmapheresis. 39. The method of claim 36 or 37, wherein the treatment that targets T cells is rabbit anti- thymocyte globulin (rATG). 40. The method of claim 35 or 37, wherein the complement inhibitory agent is a C3 inhibitor. 41. The method of any one of previous claims, wherein the method further comprises administering an immunosuppressive agent to the subject when the subject is determined to have a xenograft rejection or is at a risk of a xenograft rejection. 42. The method of any one of previous claims, wherein the subject has received a kidney, heart, lung, liver, bone marrow, pancreas, or islet cell transplantation from a porcine donor. 43. The method of any one of previous claims, wherein the expression levels are determined based on RNA expression, protein expression, epigenetic regulation, or a combination thereof. 44. The method of any one of previous claims, wherein the expression levels are determined using RNA sequencing, targeted RNA panel, a quantitative PCR assay, an antibody- based method, an epigenetic assay, a single-cell technology, a spatial transcriptomics technology, or a multiplexed approach combining RNA and protein measurements. 339 314164997v1 Attorney Docket No: 243735.000437 45. The method of claim 44, wherein the antibody-based method is flow cytometry, immunohistochemistry, or enzyme-linked immunosorbent assay (ELISA). 46. The method of claim 44, wherein the single-cell technology is single-cell RNA sequencing (scRNA-seq) or cellular indexing of transcriptomes and epitopes (CITE-seq). 47. The method of any one of previous claims, wherein the sample is collected from the subject 3 days after the xenograft transplantation. 48. The method of any one of previous claims, wherein the method comprises obtaining two or more samples from the subject at different time points after the xenograft transplantation and repeating the method for each sample. 49. A kit comprising: 1) one or more sets of probe nucleic acids useful for detecting two or more genes selected from: i. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, and IGHD; ii. human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, and SDC1; iii. human genes KLRC1, GZMB, TRDC, KLRF1, and GNLY; iv. human genes LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, and CD14; v. human genes IRF7, TLR7, TLR9, MYD88, and STAT1; vi. human genes PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, and SMPD3; vii. human genes STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, and CLEC10A; viii. human genes MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, and IFIH1; ix. human genes CD3G, THEMIS, CD5, and CD6; x. human genes CD8B, and CD8A; 340 314164997v1 Attorney Docket No: 243735.000437 xi. human genes KLRG1, GZMK, CST7, and GZMH; xii. human genes LEF1, TCF7, SELL, CD27, CD55, and CCR7; xiii. human genes CD4, CD40LG, CCR2, CCR6, DPP4; xiv. human genes CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, and IL2RA; xv. human genes ITGAE, CD38, TNFRSF9, and MKI67; xvi. human genes GZMA, PRF1, and FASLG; xvii. human genes TOP2A, TYMS, and MKI67; xviii. human genes GBP1, IRF1, TAP1, WARS1, and IDO1; xix. human genes CLEC10A, CD1C, FCER1A, CD1E, CD1D, and CCR2; xx. human genes CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, CXCR2; xxi. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2; xxii. human genes XBP1, MZB1, TNFRSF17, CD27, TENT5C, POU2AF1, PRDM1, NUGGC, SDC1, KLRC1, GZMB, TRDC, KLRF1, GNLY, LYZ, MRC1, STAB1, SLCO2B1, F13A1, CD163, MSR1, SLC40A1, CD14, IRF7, TLR7, TLR9, MYD88, STAT1, PLD4, CLEC4C, LILRA4, TPM2, MZB1, SCT, IRF4, SMPD3, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, 341 314164997v1 Attorney Docket No: 243735.000437 FASLG, TOP2A, TYMS, MKI67, GBP1, IRF1, TAP1, WARS1, IDO1, CLEC10A, CD1C, FCER1A, CD1E, CD1D, CCR2, CSF3R, FCGR3B, FPR1, ALPL, MGAM, CXCR1, MXD1, and CXCR2; xxiii. human genes MS4A1, CD19, AIM2, BANK1, CD79A, TNFRSF13C, BLK, IGHM, IGHD, STAT1, GBP1, GBP5, WARS1, IRF1, CXCL10, CXCL9, CXCL11, TAP1, SLAMF7, APOL3, LILRB1, CLEC10A, MX1, STAT1, IFIT3, IFIT1, IFIT2, IFI44L, GBP1, OAS3, CXCL10, RSAD2, TNFSF10, OAS1, IFIH1, CD3G, THEMIS, CD5, CD6, CD8B, CD8A, KLRG1, GZMK, CST7, GZMH, LEF1, TCF7, SELL, CD27, CD55, CCR7, CD4, CD40LG, CCR2, CCR6, DPP4, CTLA4, FOXP3, IKZF2, RTKN2, IRF4, IL21, IL2RA, ITGAE, CD38, TNFRSF9, MKI67, GZMA, PRF1, FASLG, TOP2A, TYMS, and MKI67; xxiv. porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, and NREP; xxv. porcine genes C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, and HIF1A; xxvi. porcine genes PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, and LYVE1; xxvii. porcine genes HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1; and/or xxviii. porcine genes SPP1, COLEC11, S100A6, NFKBIZ, CLDN1, THBS1, HIF1A, STMN1, ICAM1, TNFRSF1B, CXCL14, IL1RL1, NREP, C3, CXCL2, C4A, LYZ, S100A6, S100A11, MX1, MX2, IL33, HIF1A, PDGFRB, PECAM1, STAT3, IL33, RGS5, CD163, C3, VCAM1, TNC, CD74, SERPINE1, CCR5, TEK, RUNX1, MX2, IFIT1, COL1A1, CXCL14, MRC1, CD68, CD14, MX1, LYVE1, HIF1A, LMNA, EMP1, EIF4A1, DHX9, LYVE1, YWHAQ, CD163, VCAM1, MKI67, IFIT1, ICAM1, STAT1, STAT3, TOP2A, S100A6, WARS1, S100A1, SERPINH1, TOP2A, MX1, MX2, CD14, HSPA9, and HSPD1; or any combination of the genes listed above, and 2) optionally, packaging and/or instructions for using the same. 342 314164997v1
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