EP4185870A1

EP4185870A1 - Determining hemodilution of bone marrow aspirates using biomarkers

Info

Publication number: EP4185870A1
Application number: EP21755647.1A
Authority: EP
Inventors: Alberto Jose HIDALGO ROBERT; Cherie Louise GREEN; Shadi TOGHI ESHGHI; Darya Yuryevna ORLOVA
Original assignee: Genentech Inc
Current assignee: Genentech Inc
Priority date: 2020-07-24
Filing date: 2021-07-23
Publication date: 2023-05-31
Also published as: WO2022020774A1; JP2023537260A; CN116194777A

Abstract

Methods, systems, and machine readable media for determining hemodilution of a bone marrow sample. In one example, a method includes analyzing the bone marrow sample to determine a quantity of cells in the bone marrow sample expressing one or more cell surface markers (e.g., CD13 and a complementary marker). The method further includes correlating the quantity of cells expressing the one or more cell surface markers (e.g., CD13 and the complementary marker) to a level of hemodilution of the bone marrow sample.

Description

DETERMINING HEMODILUTION OF BONE MARROW ASPIRATES USING

BIOMARKERS

Inventors:

Alberto Jose Hidalgo Robert, Cherie Louise Green, Shadi Toghi Eshghi, Darya Yuryevna

Orlova

CROSS-REFERENCE TO RELATED APPLICATION [0001] The present application claims priority to U.S. Provisional Application No. 63/056,504, filed July 24, 2020, which is incorporated by reference herein in its entirety.

FIELD

[0002] Provided herein are methods, systems, and kits for determining or measuring hemodilution in bone marrow aspirates or bone marrow samples from a subject. More specifically, this description provides methods, systems, and kits for determining or measuring hemodilution in bone marrow samples by measuring or determining the amount [or level] of one or more proteins or cell surface markers.

BACKGROUND

[0003] Bone marrow aspiration is the removal (drawing out) of a small amount of liquid bone marrow from bone via a needle. Bone marrow aspiration is essential to diagnosis, testing, and research, including diagnosis of cancer or monitoring the response to treatment in patients having hematologic malignancies. Oftentimes, multiple samples of bone marrow are aspirated in one visit for various purposes. For example, a first aspiration may be performed to provide a bone marrow sample for morphological assessment, while a second aspiration may be performed to provide a bone marrow sample for flow cytometry. Sequential draws of bone marrow, however, may gradually increase the amount of blood contamination in these bone marrow samples, thereby hemodiluting these bone marrow samples. This type of hemodilution may reduce the reliability of data obtained from analysis performed using the bone marrow samples. In some cases, bone marrow samples with a high amount of hemodilution may need to be excluded from analysis. Accordingly, methods, systems, and kits that accurately measure the amount of hemodilution in a bone marrow sample are desirable. SUMMARY

[0004] The embodiments described herein provide various methods, systems, and computer program products for determining hemodilution levels in bone marrow samples. [0005] In some embodiments, a method is provided for determining hemodilution of a bone marrow sample. The method includes analyzing the bone marrow sample to determine a quantity of cells in the bone marrow sample expressing CD 13 and a complementary marker. The method further includes correlating the quantity of cells expressing the CD 13 and the complementary marker to a level of hemodilution of the bone marrow sample.

[0006] In some embodiments, a non-transitory computer-readable medium storing computer instructions for determining hemodilution of a bone marrow sample is provided. One or more processors receive data obtained from the bone marrow sample. The one or more processors analyze the data to determine a quantity of cells in the bone marrow sample expressing CD13 and a complementary marker. The one or more processors correlate the quantity of cells expressing the CD 13 and the complementary marker to a level of hemodilution of the bone marrow sample.

[0007] In some embodiments, a method is provided for monitoring progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject. The method includes determining a quantity of cells in the bone marrow sample expressing CD 13 and a complementary marker. The method includes correlating the quantity of cells expressing CD 13 and a complementary marker to a level of hemodilution of the bone marrow sample. The method further includes assessing whether the level of hemodilution passes a pre-set hemodilution criteria and, responsive to the level of hemodilution passing the pre-set hemodilution criteria, performing an assessment or analysis of the bone marrow sample.

[0008] In some embodiments, a method is provided for monitoring progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject. The method includes extracting targeted mRNA molecules associated with CD 13 and the complementary marker from the bone marrow sample. The method includes sequencing the targeted mRNA molecules extracted from the bone marrow sample. The method incudes determining an expression level of CD 13 and the complementary marker in the bone marrow sample using sequence data obtained from the targeted mRNA molecules. The method includes assessing whether the expression level of CD13 and the complementary marker passes a pre-set complementary marker expression criteria. And the method includes, responsive to the expression level of CD 13 and the complementary marker passing the pre set complementary marker expression criteria, performing an assessment or analysis of the bone marrow sample.

[0009] In some embodiments, a method is provided for monitoring effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing a bone marrow sample obtained from a subject being treated with the therapeutic regimen. The method includes determining a quantity of cells in the bone marrow sample expressing CD13 and a complementary marker. The method includes correlating the quantity of cells to a level of hemodilution of the bone marrow sample. The method includes assessing whether the level of hemodilution passes a pre-set hemodilution criteria. And the method includes, responsive to the level of hemodilution passing the pre-set hemodilution criteria, performing an assessment or analysis of the bone marrow sample to at least one of monitor or verify a therapeutic effectiveness of the therapeutic regimen.

[0010] In some embodiments, a method is provided for monitoring effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing a bone marrow sample obtained from a subject being treated with the therapeutic regimen. The method includes extracting targeted mRNA molecules associated with CD 13 and a complementary marker from the bone marrow sample. The method includes sequencing the targeted mRNA molecules extracted from the bone marrow sample. The method includes determining an expression level of CD 13 and the complementary marker in the bone marrow sample using sequence data obtained from the targeted mRNA molecules. The method includes assessing whether the expression level of CD13 and the complementary marker passes a pre-set complementary marker expression criteria. And the method includes, responsive to the expression level of CD13 and the complementary marker passing the pre-set complementary marker expression criteria, performing an assessment or analysis of the bone marrow sample to at least one of monitor or verify a therapeutic effectiveness of the therapeutic regimen. [0011] In some embodiments, a method is provided for determining hemodilution of a bone marrow sample. The method includes analyzing the bone marrow sample to identify a cell distribution for cells in the bone marrow sample expressing one or more cell surface markers. The method includes calculating a statistical distancing score for the cell distribution. And the method includes correlating the statistical distancing score to a level of hemodilution of the bone marrow sample.

[0012] In some embodiments, a non-transitory computer-readable medium storing computer instructions for determining hemodilution of a bone marrow sample is provided. One or more processors receive data obtained from the bone marrow sample. One or more processors analyze the data to identify a cell distribution for cells in the bone marrow sample expressing one or more cell surface markers. One or more processors calculate a statistical distancing score for the cell distribution. One or more processors correlate the statistical distancing score to a level of hemodilution of the bone marrow sample.

[0013] In some embodiments, a method is provided for monitoring progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject. The method includes calculating a statistical distancing score for a cell distribution of cells in the bone marrow sample expressing one or more cell surface markers. The method includes correlating the statistical distancing score to a level of hemodilution of the bone marrow sample. The method includes assessing whether the level of hemodilution passes a pre-set hemodilution criteria. And the method includes ascertaining a progression of the disease or disorder in the subject with the bone marrow sample if the level of hemodilution passes the pre-set hemodilution criteria.

[0014] In some embodiments, a method is provided for monitoring effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing a bone marrow sample obtained from a subject being treated with the therapeutic regimen. The method includes calculating a statistical distancing score for a cell distribution of cells in the bone marrow sample expressing one or more cell surface markers. The method includes correlating the statistical distancing score to a level of hemodilution of the bone marrow sample. The method includes assessing whether the level of hemodilution passes a pre-set hemodilution criteria. And the method includes verifying an improvement of the disease or disorder in the subject if the level of hemodilution passes the pre-set hemodilution criteria.

[0015] In some embodiments, a method is provided for determining hemodilution of a bone marrow sample. The method includes analyzing the bone marrow sample to identify a cell distribution for a cell subpopulation that expresses at least one cell surface marker of interest. The method includes calculating a statistical distancing score for the cell distribution with respect to a control cell distribution for the cell subpopulation in a control bone marrow sample with no hemodilution. And the method includes correlating the statistical distancing score to a level of hemodilution of the bone marrow sample.

[0016] In one or more embodiments, a kit is provided to carry out one or more of the methods described above or elsewhere herein.

[0017] Some embodiments of the present disclosure include a system including one or more data processors. In some embodiments, the system includes a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform part or all of one or more methods and/or part or all of one or more processes disclosed herein. Some embodiments of the present disclosure include a computer-program product tangibly embodied in a non- transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform part or all of one or more methods and/or part or all of one or more processes disclosed herein.

[0018] The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention as claimed has been specifically disclosed by embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The application file contains at least one drawing executed in color. Copies of this patent or patent application with color drawings will be provided by the Office upon request and payment of the necessary fee.

[0020] The present disclosure is described in conjunction with the appended figures: [0021] Figure 1 is a block diagram of an analysis system in accordance with an example embodiment.

[0022] Figure 2 is a flowchart illustrating a method for determining a level of hemodilution in bone marrow samples in accordance with various embodiments.

[0023] Figure 3 is a flowchart illustrating a method for obtaining data for use in determining a level of hemodilution of a bone marrow sample using flow cytometry in accordance with various embodiments.

[0024] Figure 4 is a flowchart illustrating a method for determining a quantity of cells expressing CD 13 and a complementary marker using immunohistochemistry with a fluorometer in accordance various embodiments.

[0025] Figure 5 is a flowchart illustrating a method for determining a quantity of cells expressing CD 13 and a complementary marker using immunohistochemistry with cell counting in accordance with various embodiments.

[0026] Figure 6 is a flowchart illustrating a method for determining expression levels of markers in single cells for hemodilution assessment using single-cell sequencing in accordance with various embodiments.

[0027] Figure 7 is a flowchart illustrating a method for determining expression levels of markers in a plurality of cells for hemodilution assessment using bulk-cell sequencing in accordance with various embodiments.

[0028] Figure 8 is a flowchart illustrating a method for determining hemodilution in a bone marrow sample using statistical distancing in accordance with various embodiments.

[0029] Figure 10 is a flowchart illustrating a method for identifying a set of expression profiles of interest using computationally mixed data in accordance with various embodiments. [0030] Figure 11 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13+CDllc- and hemodilution level based on simulated samples generated via computational mixing in accordance with various embodiments. [0031] Figure 12 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13+CDllc- and hemodilution level based on samples generated via experimental mixing of bone marrow and blood in accordance with various embodiments.

[0032] Figure 13 is a plot series that demonstrates that the expression profile CD13+CDllc- has a strong correlation with hemodilution level in experimentally mixed samples in accordance with various embodiments.

[0033] Figure 14 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13+CD15+ and hemodilution level based on simulated samples generated via computational mixing in accordance with various embodiments.

[0034] Figure 15 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13+CD15+ and hemodilution level based on samples generated via experimental mixing of bone marrow and blood in accordance with various embodiments.

[0035] Figure 16 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13+CD16+ and hemodilution level based on simulated samples generated via computational mixing in accordance with various embodiments.

[0036] Figure 17 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13+CD16+ and hemodilution level based on samples generated via experimental mixing of bone marrow and blood in accordance with various embodiments.

[0037] Figure 18 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13+HLA-DR- and hemodilution level based on simulated samples generated via computational mixing in accordance with various embodiments.

[0038] Figure 19 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13+HLA-DR- and hemodilution level based on samples generated via experimental mixing of bone marrow and blood in accordance with various embodiments.

[0039] Figure 20 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13-HLA-DR- and hemodilution level based on simulated samples generated via computational mixing in accordance with various embodiments. [0040] Figure 21 is a plot showing the relationship between the cell subpopulation matching the expression profile CD13-HLA-DR- and hemodilution level based on samples generated via experimental mixing of bone marrow and blood in accordance with various embodiments.

[0041] Figure 22 is a plot 2200 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD71 and hemodilution level in accordance with various embodiments.

[0042] Figure 23 is a plot 2300 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD33 and hemodilution level in accordance with various embodiments.

[0043] Figure 24 is a plot 2400 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD33 and CD 117 and hemodilution level in accordance with various embodiments.

[0044] Figure 25 is a plot 2500 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD56 and CD 13 and hemodilution level in accordance with various embodiments.

[0045] Figure 26 is a plot 2600 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD 19 and hemodilution level in accordance with various embodiments.

[0046] Fig. 27 is a plot series 2700 illustrating an example gating strategy performed in AutoGate to identify cell population of interests in accordance with various embodiments. [0047] In the appended figures, similar components and/or features can have the same reference label. Further, various components of the same type can be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label. DETAILED DESCRIPTION

I. Overview

[0048] This disclosure describes various exemplary embodiments of methods, kits, and systems for assessing the amount of hemodilution in bone marrow samples. The disclosure, however, is not limited to these exemplary embodiments and applications or to the manner in which the exemplary embodiments and applications operate or are described herein. Moreover, the figures may show simplified or partial views, and the dimensions of elements in the figures may be exaggerated or otherwise not in proportion.

[0049] The embodiments described herein provide, for example, methods and systems of assessing the quality of a bone marrow sample based on the quantity of cells expressing CD 13 and a complementary marker that comprises one or more cell surface markers. For example, the embodiments described herein provide methods and systems for analyzing a bone marrow sample to determine the quantity of cells expressing CD 13 and the complementary marker and correlating the quantity of cells with a level of hemodilution. This level of hemodilution may be used to assess the quality of the bone marrow sample. For example, a level of hemodilution above a selected threshold (e.g., about 50%, about 40%, about 30%, about 20%, etc.) may be considered a low-quality bone marrow sample. A level of hemodilution above the selected threshold may be considered a high-quality (or sufficiently high-quality) bone marrow sample that can be used to perform further analysis (e.g., assessing effectiveness of therapeutic). [0050] In some embodiments, a report is generated based on the level of hemodilution. The report may, for example, identify the level of hemodilution, include an assessment of the quality of the bone marrow sample based on the level of hemodilution, or both. In some examples, the report may identify one or more recommended actions to be taken by a human operator based on the assessed quality of the bone marrow sample. For example, when the level of hemodilution is above some selected threshold (e.g., about 50%, about 40%, about 30%, about 20%, etc.), the report may include an alert indicating that the level is above the selected threshold and may identify one or more recommended actions. The one or more recommended actions may include, for example, discarding the bone marrow sample, entering user input that makes one or more software adjustments and/or measurement adjustments for further analysis performed using the bone marrow sample, one or more other types of actions, or a combination thereof.

II. Exemplary Definitions and Considerations

[0051] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0052] Unless defined otherwise, all terms of art, notations and other technical and scientific terms or terminology used herein are intended to have the same meaning as is commonly understood by one of ordinary skill in the art to which the claimed subject matter pertains. In some cases, terms with commonly understood meanings are defined herein for clarity and/or for ready reference, and the inclusion of such definitions herein should not necessarily be construed to represent a substantial difference over what is generally understood in the art. Generally, nomenclatures utilized in connection with, and techniques of, chemistry, biochemistry, molecular biology, pharmacology and toxicology are described herein are those well-known and commonly used in the art.

[0053] As used herein, the singular forms “a” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It is also to be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It is further to be understood that the terms “includes” “including” “comprises” and/or “comprising” when used herein, specify the presence of stated features, integers, steps, operations, elements, components, and/or units but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, units, and/or groups thereof.

[0054] Throughout this disclosure, various aspects are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub ranges as well as individual numerical values within that range. For example, where a range of values is provided, it is understood that each intervening value, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed in the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed in the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure. This applies regardless of the breadth of the range.

[0055] The term “about” as used herein refers to the usual error range for the respective value readily known. Reference to “about” a value or parameter herein includes (and describes) embodiments that are directed to that value or parameter per se. For example, description referring to “about X” includes description of “X”. In some embodiments, “about” may refer to ±15%, ±10%, ±5%, or ±1% as understood by a person of skill in the art.

[0056] In addition, as the terms "in communication with" or “communicatively coupled with” or similar words are used herein, one element may be capable of communicating directly, indirectly, or both with another element via one or more wired communications links, one or more wireless communications links, one or more optical communications links, or a combination thereof. In addition, where reference is made to a list of elements (e.g., elements a, b, c), such reference is intended to include any one of the listed elements by itself, any combination of less than all of the listed elements, and/or a combination of all of the listed elements.

[0057] As used herein, "substantially" means sufficient to work for the intended purpose. The term "substantially" thus allows for minor, insignificant variations from an absolute or perfect state, dimension, measurement, result, or the like such as would be expected by a person of ordinary skill in the field but that do not appreciably affect overall performance. When used with respect to numerical values or parameters or characteristics that can be expressed as numerical values, "substantially" means within ten percent.

[0058] As used herein, the term "ones" means more than one.

[0059] As used herein, the term “plurality” or “group” can be 2, 3, 4, 5, 6, 7, 8, 9, 10, or more.

[0060] As used herein, the term “set” means one or more.

[0061] As used herein, the phrase “at least one of,” when used with a list of items, means different combinations of one or more of the listed items may be used and only one of the items in the list may be needed. The item may be a particular object, thing, step, operation, process, or category. In other words, “at least one of’ means any combination of items or number of items may be used from the list, but not all of the items in the list may be required. For example, without limitation, “at least one of item A, item B, or item C” or “at least one of item A, item B, and item C” may mean item A; item A and item B; item B; item A, item B, and item C; item B and item C; or item A and C. In some cases, “at least one of item A, item B, or item C” or “at least one of item A, item B, and item C” may mean, but is not limited to, two of item A, one of item B, and ten of item C; four of item B and seven of item C; or some other suitable combination.

[0062] As used herein, the terms “bone marrow sample,” “bone marrow aspirate,” and “bone marrow sample aspirate” are used interchangeably and refer to an amount of liquid bone marrow that has been aspirated from (drawn from) the bone of a subject.

[0063] As used herein, the terms “whole blood” and “peripheral blood” may be used interchangeably and refer to blood in which the red blood cells have not been separated from the white blood cells.

[0064] An “individual”, “subject,” or “patient” is a mammal. Mammals include, but are not limited to, domesticated animals (e.g., cows, sheep, cats, dogs, and horses), primates (e.g., humans and non-human primates such as monkeys), rabbits, and rodents (e.g., mice and rats). In certain aspects, the individual or subject is a human.

[0065] As used herein, “hemodilution” refers to an increased concentration of peripheral blood cells in a bone marrow sample.

[0066] The term “CD 13”, as used herein, refers to any native CD 13 protein from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats), unless otherwise indicated. The term “CD13” encompasses “full-length”, unprocessed CD13 as well as any form of CD13 that results from processing in the cell. The term “CD13” also encompasses naturally occurring variants of CD13, e.g., splice variants or allelic variants. CD 13 is located in the small-intestinal and renal microvillar membrane, and also in other plasma membranes. In the small intestine CD 13 plays a role in the final digestion of peptides generated from hydrolysis of proteins by gastric and pancreatic protease. CD 13 is also known as alanine aminopeptidase (AAP) N, alanyl aminopeptidase, aminopeptidase M, microsomal aminopeptidase, myeloid plasma membrane glycoprotein CD 13, gpl50, or membrane alanyl aminopeptidase. CD 13 is encoded by ANPEP gene (also known as APN, CD13, or PEPN gene) alanyl aminopeptidase. ( See https ://www .uniprot-org/uniprot/P 15144 for more information about human CD 13 and common isoforms.)

[0067] The term “CD 11c”, as used herein, refers to any native CD 11c protein from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats), unless otherwise indicated. The term “CDllc” encompasses “full-length”, unprocessed CD1 lc as well as any form of CD1 lc that results from processing in the cell. The term “CD1 lc” also encompasses naturally occurring variants of CD1 lc, e.g., splice variants or allelic variants. CDllc is a receptor for fibrinogen. It recognizes the sequence G-P-R in fibrinogen. It mediates cell-cell interaction during inflammatory responses and is used in monocyte adhesion and chemotaxis. CDllc is also known as Integrin, alpha X (complement component 3 receptor 4 subunit), or ITGAX. CD1 lc is encoded by CDllc gene (also known as Integrin, alpha X (complement component 3 receptor 4 subunit gene (ITGAX)). ( See http s ://w w w . uniprot.org/uniprot/P20702 for more information about human CDllc and common isoforms.)

[0068] The term “CD 15”, as used herein, refers to a tetrasaccharide carbohydrate, which is usually attached to O-glycans on the surface of cells from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats), unless otherwise indicated. CD15 is a tetrasaccharide composed of a sialic acid, fucose and an N- acetyllactosamine. Its systematic name is 5-acetylneuraminyl-(2-3)-galactosyl-(l-4)- (fucopyranosyl-(l-3))-N-acetylglucosamine (Neu5Aca2-3Gaipi-4[Fucal-3]GlcNAcP). CD15 is also known as Sialyl-Lewis^x, SLE, stage-specific embryonic antigen 1 (“SSEA-1”), or cluster of differentiation 15s (“CD15s”). CD15 is also one of the blood group antigens and is displayed on the terminus of glycolipids that are present on the cell surface. The CD 15 determinant, E-selectin ligand carbohydrate structure, is constitutively expressed on granulocytes and monocytes and mediates inflammatory extravasation of these cells. The term “ CD15” encompasses “full-length”, unprocessed CD15 as well as any form of CD15 that results from processing in the cell. The term “CD 15” also encompasses naturally occurring variants of CD15, e.g., splice variants or allelic variants.

[0069] The term “CD 16”, as used herein, refers to any native CD 16 protein from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats), unless otherwise indicated. The term “CD16” encompasses “full-length”, unprocessed CD16 as well as any form of CD16 that results from processing in the cell. The term “CD16” also encompasses naturally occurring variants of CD16, e.g., splice variants or allelic variants. CD 16, also known as FcyRIII, is a cluster of differentiation molecule found on the surface of natural killer cells, neutrophils, monocytes, and macrophages. CD16 is the type III Fey receptor. In humans, CD 16 exists in two different forms: FcyRIIIa (CD 16a) and FcyRIIIb (CD 16b), which have 96% sequence similarity in the extracellular immunoglobulin binding regions. CD 16a is encoded by FCGR3A gene (also known as CDMA, FCG3, FCGR3, IGFR3), and CD 16b is encoded by FCGR3B gene (also known as CD16B, FCG3, FCGR3, IGFR3). ( See https://www.uniprot.org/uniprot/P08637 and http s ://w w w . uniprot.or /uniprot/Q75015 for more information about human CD 16a and CD 16b and common isoforms.)

[0070] The term “HLA-DR” (or “HLADR”), as used herein, refers to any native HLA-DR protein from any vertebrate source, including mammals such as primates (e.g., humans) and rodents (e.g., mice and rats), unless otherwise indicated. The term “HLA-DR” encompasses “full-length”, unprocessed HLA-DR as well as any form of HLA-DR that results from processing in the cell. The term “HLA-DR” also encompasses naturally occurring variants of HLA-DR, e.g., splice variants or allelic variants. HLA-DR is also known as the Human Leukocyte Antigen - DR isotype. HLA-DR helps to present peptide antigens, potentially foreign in origin, to the immune system for eliciting or suppressing T-(helper)-cell responses that eventually lead to the production of antibodies against the same peptide antigen. HLA- DR is an ab heterodimer, cell surface receptor, each subunit of which contains two extracellular domains, a membrane- spanning domain and a cytoplasmic tail. Both a and b chains are anchored in the membrane. The N-terminal domain of the mature protein forms an alpha-helix that constitutes the exposed part of the binding groove, the C-terminal cytoplasmic region interacts with the other chain forming a beta- sheet under the binding groove spanning to the cell membrane. HLA-DR has five subunits, HLA-DRa, HLD-DRbl, HLA-DRb3, HLA- DRb4, and HLA-DRb5, which are encoded by HLA-DRA , HLA-DRB1, HLA-DRB3, HLA- DRB4, and HLA-DRB5, respectively. (See https:// w w w .unipiOt.o g/unip ro t / PO 1903 , https://www.uniprot.org/uniprot/PO 1911, https ://www uniprot.org/uniprot/P79483 , http s ://w w w . uniprot.org/uniprot/ 13762, and https://www.uniprot.org/uniprot/Q30154 for more information about human HLA-DRa, HLD-DRpi, HLA-DRP3, HLA-DRP4, HLA- DRP5 and common isoforms.)

[0071] As used herein, an “expression level” of a cell surface marker, such as a protein, refers to a measurable detection of that cell surface marker with respect to an individual cell, a tissue sample, a liquid sample, or some other type of sample. When used with respect to flow cytometry, the expression level of, for example, a protein corresponds to a presence or absence of that protein on or embedded in the cell depending on whether or not a fluorescence corresponding to that protein is detected for the cell and/or whether that fluorescence has a low (or dim) level of brightness, a middle level of brightness, or a high level of brightness. When used with respect to immunohistochemistry (IHC) or mRNA sequencing, the expression level of a protein may refer to the presence or absence of that protein on or embedded in the cell as described above or with respect to the overall level of expression of that protein in the sample that is analyzed.

[0072] As used herein, a positive sign (+) in association with a cell surface marker (e.g., CD13+, CD15+, CD16+, HLA-DR+) refers to a positive expression of that cell surface marker on a cell (e.g., a cell contained in a blood sample or bone marrow sample). A positive expression, as used herein, may refer to an expression level that is detectable, e.g., an expression level that is above a detection threshold based on a detection method that is used, such as flow cytometry, IHC, or gene expression analysis through RNA sequencing. For example, a CD13+ cell is a cell that expresses CD13 or, has CD13 on its surface.

[0073] As used herein, a negative sign (-) in association with a cell surface marker (e.g., CD13-, CDllc-, HLA-DR-) refers to a negative expression of that cell surface marker on a cell (e.g., a cell contained in a blood sample or bone marrow sample). A negative expression, as used herein, may refer to an expression level that is not detectable, e.g., an expression level that is below a detection threshold based on a detection method that is used, such as flow cytometry, IHC, or gene expression analysis through RNA sequencing. For example, a CD13- cell is a cell that does not express CD 13 or does not have CD 13 on its surface.

[0074] As used herein, an “expression profile” is defined as an expression level of interest for each of two or more cell surface markers (e.g., protein, carbohydrate) such that when the respective expression levels of interest for the two or more cell surface markers are detected together on a cell, they enable characterization of that cell in a manner that distinguishes the cell as being of a particular type (e.g., a bone marrow cell, a peripheral blood cell). In these examples, the two or more cell surface markers include CD 13 and at least one other complementary marker.

[0075] As used herein, a “complementary marker” refers to a set of (one or more) cell surface markers for which an expression level of interest for that one or more cell surface markers on cells in a bone marrow sample, when looked at in combination with an expression level of interest for CD 13 on those same cells in the bone marrow sample, enables a characterization of those cells in a manner that enables mathematically correlating a quantity of cells that express CD 13 and the complementary marker to a level of hemodilution. For example, identifying the particular cells in a bone marrow sample that have a first expression level of interest (e.g., + or -) for CD13 in combination with a second expression level of interest (e.g., + or -) for the complementary marker enables characterizing those particular cells as either bone marrow cells or peripheral blood cells. In other words, the quantity of cells in the bone marrow sample that have the first expression level of interest for CD 13 and the second expression level of interest for the complementary marker may be linearly or otherwise correlated via a mathematical function (e.g., logarithmic, exponential, etc.) to a level of hemodilution. As one example, an increasing quantity of these cells may indicate an increasing level of hemodilution or, alternatively, a decreasing quantity of these cells may indicate an increasing level of hemodilution.

[0076] As used herein, “DNA (deoxyribonucleic acid)” is a chain of nucleotides consisting of four types of nucleotides; A (adenine), T (thymine), C (cytosine), and G (guanine), and that RNA (ribonucleic acid) is comprised of 4 types of nucleotides; A, U (uracil), G, and C. Certain pairs of nucleotides specifically bind to one another in a complementary fashion (called complementary base pairing). That is, adenine (A) pairs with thymine (T) (in the case of RNA, however, adenine (A) pairs with uracil (U)), and cytosine (C) pairs with guanine (G). When a first nucleic acid strand binds to a second nucleic acid strand made up of nucleotides that are complementary to those in the first strand, the two strands bind to form a double strand.

[0077] As used herein, “nucleic acid sequencing data,” “nucleic acid sequencing information,” “nucleic acid sequence,” “genomic sequence,” “genetic sequence,” or “fragment sequence,” or “nucleic acid sequencing read” denotes any information or data that is indicative of the order of the nucleotide bases (e.g., adenine, guanine, cytosine, and thymine/uracil) in a molecule (e.g., whole genome, whole transcriptome, exome, oligonucleotide, polynucleotide, fragment, etc.) of DNA or RNA. It should be understood that the present teachings contemplate sequence information obtained using all available varieties of techniques, platforms or technologies, including, but not limited to: capillary electrophoresis, microarrays, ligation-based systems, polymerase-based systems, hybridization-based systems, direct or indirect nucleotide identification systems, pyro sequencing, ion- or pH-based detection systems, electronic signature-based systems, etc.

[0078] A “polynucleotide”, “nucleic acid”, or “oligonucleotide” refers to a linear polymer of nucleosides (including deoxyribonucleosides, ribonucleosides, or analogs thereof) joined by intemucleosidic linkages. Typically, a polynucleotide comprises at least three nucleosides. Usually oligonucleotides range in size from a few monomeric units, e.g. 3-4, to several hundreds of monomeric units. Whenever a polynucleotide such as an oligonucleotide is represented by a sequence of letters, such as “ATGCCTG,” it will be understood that the nucleotides are in 5 '->3' order from left to right and that “A” denotes deoxyadenosine, “C” denotes deoxycytidine, “G” denotes deoxyguanosine, and “T” denotes thymidine, unless otherwise noted. The letters A, C, G, and T may be used to refer to the bases themselves, to nucleosides, or to nucleotides comprising the bases, as is standard in the art.

[0079] As used herein, the term “cell” is used interchangeably with the term “biological cell.” Non-limiting examples of biological cells include eukaryotic cells, plant cells, animal cells, such as mammalian cells, reptilian cells, avian cells, fish cells or the like, prokaryotic cells, bacterial cells, fungal cells, protozoan cells, or the like, cells dissociated from a tissue, such as muscle, cartilage, fat, skin, liver, lung, neural tissue, and the like, immunological cells, such as T cells, B cells, natural killer cells, macrophages, and the like, embryos (e.g., zygotes), oocytes, ova, sperm cells, hybridomas, cultured cells, cells from a cell line, cancer cells, infected cells, transfected and/or transformed cells, reporter cells and the like. A mammalian cell can be, for example, from a human, mouse, rat, horse, goat, sheep, cow, primate or the like.

[0080] As used herein, a genome is the genetic material of a cell or organism, including animals, such as mammals, e.g., humans. In humans, the genome includes the total DNA, such as, for example, genes, noncoding DNA and mitochondrial DNA. The human genome typically contains 23 pairs of linear chromosomes: 22 pairs of autosomal chromosomes plus the sex-determining X and Y chromosomes. The 23 pairs of chromosomes include one copy from each parent. The DNA that makes up the chromosomes is referred to as chromosomal DNA and is present in the nucleus of human cells (nuclear DNA). Mitochondrial DNA is located in mitochondria as a circular chromosome, is inherited from only the female parent, and is often referred to as the mitochondrial genome as compared to the nuclear genome of DNA located in the nucleus.

[0081] The phrase “gene expression analysis” refers to any step or technique that can study the occurrence or activity of the formation of a gene product from its coding gene. It can be a useful indicator of biological activity wherein a changing gene expression pattern is reflected in a change of biological process. Gene expression analysis may include measurement of gene expression at the mRNA level or protein level. Gene expression analysis may include, but not be limited to, array-based methods (e.g., DNA microarrays, etc.), real-time/digital/quantitative PCR instrument methods and whole or targeted nucleic acid sequencing systems (e.g., NGS systems, Capillary Electrophoresis systems, etc.). Non-limiting examples of gene expression analysis may include northern blotting, PCR, reverse transcription-quantitative PCR (“RT- qPCR”), fluorescence in situ hybridization (“FISH”), Taq Man analysis, FRET detection, hybridization to an oligonucleotide array, hybridization to a cDNA array, hybridization to a polynucleotide array, hybridization to a liquid microarray, hybridization to a microelectric array, molecular beacons, clone hybridization, cDNA fragment fingerprinting, serial analysis of gene expression (“SAGE”), subtractive hybridization, differential display and/or differential screening, RNA-sequencing (“RNA-seq”), and any combination thereof.

[0082] The phrase “sequencing” refers to any technique known in the art that allows the identification of consecutive nucleotides of at least part of a nucleic acid. Non-limiting exemplary sequencing techniques include RNA-seq (also known as whole transcriptome sequencing), Illumina™ sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy termination sequencing, whole-genome sequencing, massively parallel signature sequencing (MPSS), sequencing by hybridization, pyrosequencing, capillary electrophoresis, gel electrophoresis, duplex sequencing, cycle sequencing, single-base extension sequencing, solid-phase sequencing, high-throughput sequencing, massively parallel signature sequencing, emulsion PCR, sequencing by reversible dye terminator, paired-end sequencing, near-term sequencing, exonuclease sequencing, sequencing by ligation, short-read sequencing, single- molecule sequencing, sequencing-by-synthesis, real-time sequencing, reverse-terminator sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD™ sequencing, MS-PET sequencing, mass spectrometry, and any combination thereof. [0083] The phrase “RNA-seq (RNA-sequencing)” refers to any step or technique that can examine the presence, quantity or sequences of RNA in a biological sample using sequencing such as next generation sequencing (NGS). RNA-seq can analyze the transcriptome of gene expression patterns encoded within the RNA.

[0084] The phrase “next generation sequencing” (NGS) refers to sequencing technologies having increased throughput as compared to traditional Sanger- and capillary electrophoresis- based approaches, for example with the ability to generate hundreds of thousands of relatively small sequence reads at a time. Some examples of next generation sequencing techniques include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization. More specifically, the MISEQ, HISEQ and NEXTSEQ Systems of Illumina and the Personal Genome Machine (PGM) and SOLiD Sequencing System of Life Technologies Corp, provide massively parallel sequencing of whole or targeted genomes. The SOLiD System and associated workflows, protocols, chemistries, etc. are described in more detail in PCT Publication No. WO 2006/084132, entitled “Reagents, Methods, and Libraries for Bead-Based Sequencing,” international filing date Leb. 1, 2006, U.S. patent application Ser. No. 12/873,190, entitled “Low-Volume Sequencing System and Method of Use,” filed on Aug. 31, 2010, and U.S. patent application Ser. No. 12/873,132, entitled “Last-Indexing Lilter Wheel and Method of Use,” filed on Aug. 31, 2010, the entirety of each of these applications being incorporated herein by reference thereto.

III. Conventional Methods for Determining Hemodilution in Bone Marrow

Aspirates

[0085] Assessing the quality of bone marrow samples obtained via aspirations is important to evaluating their reliability. For example, hemodiluted bone marrow samples, those that have been contaminated by some amount of whole blood, can cause the results of analysis using the bone marrow samples to be unreliable. Some conventional methods for assessing the amount of hemodilution in bone marrow samples are qualitative, too complex for regular practice, unable to distinguish lower levels of hemodilution (e.g., about 25% hemodilution, about 50% hemodilution, etc.), or a combination thereof.

[0086] One conventional method includes a morphological assessment of the hemodilution in bone marrow samples prior to performing flow cytometry. A morphological assessment may include evaluating the morphology of the cells found in a smear of the bone marrow sample. A morphological assessment is a qualitative assessment that can be used to identify bone marrow samples with high levels of hemodilution but does not provide the information needed to quantify lower levels of hemodilution. For example, morphological assessments are generally unable to distinguish between about 25% hemodilution, about 50% hemodilution, and about 75% hemodilution.

[0087] Another method of assessing hemodilution includes using a hematology analyzer to measure the number of white blood cells (WBC) in a bone marrow sample. While this type of analysis provides a relatively accurate indication of hemodilution, white blood cells are generally not a reliable marker for regular practice. For example, white blood cell count may vary greatly from subject to subject. Further, white blood cell count may be particularly unreliable with respect to subjects having hematological disorders.

[0088] Yet another method for assessing hemodilution includes analyzing the percentage of plasma cells, and cells that express the cell surface marker CD34 (i.e., CD34+ cells), and granulocytes that express the cell surface maker CD10 (i.e., CD10+ G) in bone marrow samples. Plasma cells and CD34+ cells are two cell populations that are nearly absent from whole blood, while CD 10+ G make up the majority of the granulocyte population in whole blood. Samples having an increased percentage of CD 10+ G simultaneously with a decreased percentage of plasma cells and CD34+ cells are considered “contaminated.” (See Jose Antonio Delgado, Francisco Guillen-Grima, Cristina Moreno, Carlos Panizo, Carmen Perez-Robles, Juan Jose Mata, Laura Moreno, Paula Arana, Silvia Chocarro, & Juana Merino. A simple-flow cytometry method to evaluate peripheral blood contamination of bone marrow aspirates, Journal of Immunological Methods 442, 54-58 (2017) (incorporated herein by reference in its entirety).) A peripheral blood contamination index (PCBI) is computed based on the percentage of plasma cells, CD34+ cells, and CD 10+ G found in a bone marrow sample and compared to a predetermined threshold. A bone marrow sample having a PCBI greater than or equal to the threshold is considered “contaminated,” while a bone marrow sample having a PCBI lower than the threshold is considered of good quality. While this method may identify samples having a lower level hemodilution (e.g., about 75% hemodilution) than can be assessed morphologically, this method may not provide the ability to distinguish between lower levels of hemodilution (e.g., distinguishing between about 25% hemodilution, about 50% hemodilution, and about 75% hemodilution, etc.).

[0089] Thus, conventional methods of assessing hemodilution in bone marrow samples do not provide the level of accuracy desired with respect to determining or measuring hemodilution in bone marrow samples, especially for lower levels of hemodilution.

IV. Exemplary Methods for Determining Hemodilution in Bone Marrow Aspirates [0090] The various method, kit, and system embodiments described herein enable repeatable, easy, and reliable quantitative assessment of hemodilution in bone marrow samples. In particular, the embodiments described herein enable distinguishing between different hemodilution levels between 0% and about 100%, including, but not limited to, about 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, and 100%. The disclosure herein provides a number of proteins (e.g., biomarkers) that are used to determine or measure hemodilution. More particularly, the embodiments described herein provide a method for analyzing a bone marrow sample to determine a quantity of cells in the bone marrow sample expressing CD13 and a complementary marker, and correlating the quantity of cells to a level of hemodilution of the bone marrow sample. Methods for analyzing the bone marrow sample may use, for example, flow cytometry, cell counting, mRNA sequencing, or a combination thereof. The example methods described below establish a standard for determining or measuring the hemodilution, or the purity/quality, of a bone marrow sample for use in research, diagnoses, or clinical studies. The methods described herein are robust and reproducible.

[0091] Figure 1 is a block diagram of an analysis system 100 in accordance with an example embodiment. Analysis system 100 includes computer system 102, expression measurement system 104, data storage 105, display system 106, and kit 108. Expression measurement system 104 may be in communication with computer system 102. In some examples, expression measurement system 104 and computer system 102 may be integrated together. Data storage 105 may be in communication with expression measurement system 104. Data storage 105 and display system 106 may be in communication with computer system 102. In some examples, data storage 105, display system 106, or both may be considered part of or otherwise integrated with computer system 102. Thus, in some examples, computer system 102, expression measurement system 104, data storage 105, and display system 106 may be separate components in communication with each other, but in other examples, some combination of these components may be integrated together.

[0092] Analysis system 100 may be used to analyze bone marrow samples to determine or measure hemodilution based on the expression or absence of expression of one or more combinations of cell surface markers. These cell surface markers may include, for example, molecules such as proteins, receptors, carbohydrates, etc.). As one example, kit 108 includes one or more binding agents 110 for labeling various cell surface markers. These cell surface markers may include, but are not limited to, CD5, CD10, CDllc, CD13, CD15, CD16, CD19, CD33, CD34, CD38, CD45, CD56, CD57, CD71, CD117, HLA-DR, some other cell surface marker, some other type of protein, or any combination thereof.

[0093] The one or more binding agents 110 may include one or more fluorophore- conjugated antibodies, one or more fluorophore-conjugated peptides, or a combination thereof. In further detail, one of the binding agents 110 may be an antibody labeled with a label selected from the group consisting of a fluorescent label, an enzymatic label, a radioisotope, a quantum dot, a molecule capable of a colorimetric reaction, a magnetic particle, or any other suitable molecule or compound capable of detection. One of the binding agents 110 may be a barcode used in RNA sequencing in accordance with various embodiments. A barcode can be part of an analyte. A barcode can be independent of an analyte to be bound and detected. A barcode can be a tag (e.g., nucleic acid molecule) attached to the analyte or a combination of the tag in addition to an endogenous characteristic of the analyte (e.g., size of the analyte or end sequence(s)). A barcode may be unique. Barcodes can have a variety of different formats. For example, barcodes can include barcode sequences, such as: polynucleotide barcodes; random nucleic acid and/or amino acid sequences; and synthetic nucleic acid and/or amino acid sequences. A barcode can be attached to an analyte in a reversible or irreversible manner. A barcode can be added to, for example, a fragment of a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sample before, during, and/or after sequencing of the sample. Barcodes can allow for identification and/or quantification of individual sequencing reads. [0094] Expression measurement system 104, in conjunction with kit 108, analyzes bone marrow sample 112. Expression measurement system 104 may include a flow cytometry system, an IHC system, which may include a fluorometer, a gene sequencing system, an imaging system, or a combination thereof.

[0095] In one or more examples, bone marrow sample 112 is a sample of bone marrow (e.g., liquid bone marrow, bone marrow tissue, etc.) taken from the bone of a subject. Kit 108 is used to prepare (e.g., stain) bone marrow sample 112 for use with expression measurement system 104. Expression measurement system 104 measures the expression level of the cells in bone marrow sample 112 based on the different cell surface markers labeled using kit 108. In particular, for each cell surface marker of interest, expression measurement system 104 generates data 114 that can provide a count of substantially each cell in bone marrow sample 112 having that cell surface marker.

[0096] Data 114 from expression measurement system 104 may be processed via computer system 102 to identify certain cell subpopulations within bone marrow sample 112. For example, computer system 102 may include software, firmware, hardware, or a combination thereof for determining or measuring hemodilution in bone marrow samples, such as bone marrow sample 112. As one example, computer system 102 includes one or more processors that are integrated as part of, in communication with, or otherwise associated with expression measurement system 104.

[0097] The cells in the bone marrow sample that can be characterized by a particular expression profile of interest form a “cell subpopulation.” An expression profile of interest is one that has been found to have a calculable correlation to the level of hemodilution in a bone marrow sample. For example, formula 118 may define this correlation. The nature of this correlation is dependent on the particular cell surface markers included in the expression profile. For example, in some cases, an increasing quantity (or percentage) of cells that have a particular expression profile substantially linearly correlates with an increasing level (or percentage) of hemodilution. In other cases, a decreasing quantity (or percentage) of cells that have a particular expression profile substantially linearly correlates with an increasing level (or percentage) of hemodilution. Thus, formula 118 may enable linearly correlating the quantity of cells in bone marrow sample 112 having a particular expression profile with a particular level or amount of hemodilution (e.g., about 25% hemodilution, about 50% hemodilution, etc.). In other cases, the correlation is to a range of hemodilution levels (e.g., between about 20-30%, between about 40-60%, about 60-90%, etc.).

[0098] Computer system 102 uses data 114 from expression measurement system 104 to measure one or more cell subpopulations (e.g., measure the quantity of cells in each cell subpopulation) having an expression profile of interest from the expression profiles 116 stored in data storage 105. Computer system 102 then correlates this quantity of cells to a level of hemodilution based on formula 118. Various simulation/experimental results indicate that the expression profile CD13+CD1 lc- strongly linearly correlates with hemodilution level and may be used to reliably determine or measure hemodilution level. Further, the expression profiles CD13+CD15+, CD13-HLA-DR-, CD13+HLA-DR-, and CD13+CD16+ also show a strong linear correlation with hemodilution level and may likewise be used to reliably measure hemodilution level. These expression profiles are identified below in Table 1 (further discussion of these expression profiles is provided below in Section V):

Table 1: Expression Profiles of Interest that Linearly Correlate with Hemodilution [0099] While the embodiments herein are described with respect to a substantially linear correlation between the above-identified cell subpopulations and hemodilution level, it should be appreciated that other cell subpopulations may reveal different types of mathematical functions or otherwise mathematically quantifiable relationships/correlations to hemodilution level.

[0100] Computer system 102 may generate report 119, which indicates the level or amount of hemodilution determined or measured for bone marrow sample 112, for use by operator 120 to assess the quality of bone marrow sample 112. In other examples, computer system 102 provides an assessment of the quality of bone marrow sample 112 in report 119. Computer system 102 may display report 119 or at least some portion of report 119 on display system 106. In some cases, computer system 102 displays cell subpopulation plots generated by expression measurement system 104 on display system 106.

[0101] Operator 120 may take any of a number of different forms. For example, operator 120 may be an engine (e.g., hardware, firmware, software, or a combination thereof) within computer system 102 or another computer system. In other examples, operator 120 may be a human operator such as an analyst, a medical professional, a technician, or another other type of human operator.

[0102] Based on the quality assessment of bone marrow sample 112, operator 120 may determine whether any action is needed. Such action may include, but is not limited to, disqualifying bone marrow sample 112 from use if the level of hemodilution is above some selected threshold (e.g., about 50%, about 40%, about 30%, about 20%, etc.). In some cases, operator 120 may determine that if bone marrow sample 112 is to be used for diagnosis, treatment, or some other laboratory or medical purpose, adjustments may be needed (e.g., with respect to blast count) to account for the assessed level of hemodilution.

[0103] In some examples, a low hemodilution state for the bone marrow sample is called if the percent hemodilution is less than about 100%. In some examples, a low hemodilution state for the bone marrow sample is called if the percent hemodilution is less than about 75%. In some examples, a low hemodilution state for the bone marrow sample is called if the percent hemodilution is less than about 50%. In some examples, a low hemodilution state for the bone marrow sample is called if the percent hemodilution is less than about 25%. [0104] In various embodiments, report 119 includes an identification of the level of hemodilution, the assessment of the quality of bone marrow sample 112, one or more recommended actions to be taken based on the assessment, or a combination thereof. A recommended action may include, for example, making a software adjustment, analysis adjustment, measurement adjustment (e.g., with respect to blast count), some other type of adjustment, or a combination thereof to account for the assessed level of hemodilution. A recommended action may include, for example, discarding of the bone marrow sample. A recommended action may include, for example, entering a note in a record or file associated with the bone marrow sample or its analysis.

[0105] In some embodiments, computer system 102 is configured to display report 119 on display system 106 such that the assessed level of hemodilution and one or more corresponding recommended actions are displayed simultaneously. This type of visual representation of the report may enable operator 120 to quickly and efficiently understand or identify the quality of the bone marrow sample along with the one or more corresponding recommended actions. [0106] Figure 2 is a flowchart illustrating a method for determining a level of hemodilution of a bone marrow sample in accordance with various embodiments. Method 200 may be implemented using, for example, analysis system 100 described with respect to Figure 1 or a similar analysis system.

[0107] Step 202 includes obtaining a bone marrow sample from a subject. One technique for obtaining a bone marrow sample includes bone marrow aspiration. Bone marrow aspiration involves drawing liquid bone marrow from within a bone of a subject. In some examples, the bone marrow may be a previously collected sample that has been held in storage or that was transported from a laboratory, hospital, testing facility, or other location for use. Another technique for obtaining a bone marrow sample include performing a bone marrow biopsy to obtain a bone marrow tissue sample.

[0108] Step 204 includes analyzing the bone marrow sample determine a quantity of cells in the bone marrow sample expressing CD 13 and a complementary marker. The complementary marker may be, for example, CDllc, CD15, CD16, or HLA-DR. As one example, step 204 includes determining the quantity of cells in the bone marrow sample that have an expression profile of interest such as one of CD13+, CDllc-, CD13+CD15+, CD13+CD16+, CD13-HL-ADR-, CD13+HLA-DR-, or another expression profile. Step 204 may be performed using any number of or combination of techniques including, but not limited to, flow cytometry, cell counting, IHC with fluorometer, and single cell sequencing methods. [0109] Step 206 includes correlating the quantity of cells to a level of hemodilution of the bone marrow sample. As one example, the analysis system 100 of Figure 1 correlates the quantity of cells to a level of hemodilution based on a linear correlation previously identified between the expression profile of interest and the level of hemodilution.

[0110] In some embodiments, method 200 includes step 208. Step 208 includes generating a report based on the level of hemodilution. The report may include, for example, an identification of the level of hemodilution, the assessment of the quality of bone marrow sample 112, one or more recommended actions to be taken based on the assessment, or a combination thereof. A recommended action may include, for example, making a software adjustment, analysis adjustment, measurement adjustment (e.g., with respect to blast count), some other type of adjustment, or a combination thereof to account for the assessed level of hemodilution; discarding the bone marrow sample; entering a note in a record or file associated with the bone marrow sample or its analysis; or a combination thereof. In various embodiments, method 200 (e.g., step 208) includes displaying the report on a display system to allow a human operator to easily and quickly understand the quality of the bone marrow sample.

[0111] The level of hemodilution determined via the method 200 may be used to evaluate a disease or disorder. For example, the level of hemodilution may be used to assess whether the level of hemodilution passes a pre-set hemodilution criteria. If the level of hemodilution passes the pre-set hemodilution criteria, the level of hemodilution can be used to ascertain a progression of the disease or disorder in a subject with the bone marrow sample or verify an improvement of the disease or disorder in the subject to thereby evaluate the efficacy of a therapeutic regimen. If the level of hemodilution does not pass the pre-set hemodilution criteria, the bone marrow sample may be considered too unreliable to use and discarded or disqualified. IV.A Analysis using Flow Cytometry

IV.A.l. General Methodology

[0112] Figure 3 is a flowchart illustrating a method for obtaining data for use in determining a level of hemodilution of a bone marrow sample using flow cytometry in accordance with various embodiments. Method 300 may be implemented using analysis system 100 described with respect to Figure 1 or a similar analysis system.

[0113] Step 302 includes mixing a bone marrow sample with one or more binding agents for labeling CD 13 and at least one complementary marker that includes one or more cell surface markers selected from a group consisting of CDllc, CD15, CD16, and HLA-DR. In some cases, the complementary marker may include one or more proteins or other cell surface markers other than those listed herein. This mixing may be performed by staining the bone marrow sample with the one or more binding agents. In some examples, a single binding agent is used to label CD 13 and the at least one complementary marker. In other examples, multiple binding agents are needed. A binding agent may include, for example, a fluorophore- conjugated antibody. A binding agent may include, for example, a peptide conjugated with a fluorophore (i.e., a fluorophore-conjugated peptide). A binding agent may include, for example, an antibody labeled with a fluorescent label, an enzymatic label, a radioisotope, a quantum dot, or other type of label. Examples of binding agents (or reagents) that may be used in step 302 are shown in Table 3 in Section IV.E below.

[0114] Step 304 includes analyzing the bone marrow sample that has been mixed with the one or more binding agents using flow cytometry to generate data characterizing cells of the bone marrow sample. As one example, step 304 includes generating, for each cell surface marker (e.g., a protein, a carbohydrate, etc.) of interest, a measurement of the number of cells in the bone marrow sample that express that cell surface marker of interest. In this example, step 304 also includes generating a count of the total number of cells of the bone marrow sample analyzed.

[0115] Step 306 includes gating the cells based on the data to determine a percentage of cells within the bone marrow sample that has an expression profile selected from the group consisting of CD13+CDllc-, CD13+CD15+, CD13+CD16+, CD13+HLA-DR-, and CD13- HLA-DR-. As one example, step 306 includes using a flowDensity software package to automate the gating such that the analysis of the flow cytometry data generated in step 304 is automated. ( See Malek, M. et al. flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics 31, 606-607 (2015) (incorporated herein by reference in its entirety).) In various embodiments, prior to step 306, the data is processed and “cleaned” to exclude outlier events based on signal stability over acquisition time. Further, only data for “singlets” is processed. A singlet is a single particle/cell. Filtering for singlets may be performed at step 306, at step 304, or in between steps 304 and 306.

IV.A.2. Example of Protocol for Obtaining Flow Cytometry Data [0116] An example of a flow cytometric protocol that can be used to analyze a bone marrow sample and obtain flow cytometric data is shown below in Table 2:

[0117] The protocol shown in Table 2 is for performing eight-color flow cytometry on every bone marrow sample (e.g., hemodiluted specimen) of every donor using a FACSCanto II flow cytometer in conjunction with a 4-tube antibody panel designed to characterize hemodilution levels. From each hemodiluted specimen, four lOOuL aliquots are transferred to 5mL tubes. The corresponding antibody cocktail are added to each tube. These tubes are then incubated for 30 minutes at room temperature, in the dark. Once the incubation period ends, sample preparation continues according to a standard whole-blood lysis procedure, with the use of Biolegend RBC Lysis solution: the 10X RBC Lysis solution is diluted to a IX solution; three milliliters of the IX solution is added to each tube and these tubes are then vigorously vortexed. The samples are incubated for 15 minutes at room temperature, in the dark. Post incubation with IX RBC Lysis solution, the samples are centrifuged at 1600rpm for 5 minutes. The supernatant is decanted and the tubes are slightly vortexed, to break pellet. This is followed by washing the samples with three milliliters of BD Biosciences’ Stain Buffer (FBS). The samples are then centrifuged at 1600rpm for 5 minutes. This cell wash and decant procedure is repeated once more. After the last decant of supernatant, the tubes are slightly vortex and lOOuL of 1% Formaldehyde in aqueous solution (VWR’s Formaldehyde 16% in aqueous solution diluted in water) is added. Samples are incubated for 15 minutes, at 4 degrees Celsius in the dark, before acquisition. Samples are acquired in a FACSCantoII Flow Cytometer and data is acquired via FACS Diva software (available from Becton Dickinson Biosciences, San Jose, CA.) Manual gating is performed using FCS Express software program (e.g., De Novo Software, Los Angeles, CA).

IV.B Analysis using IHC

IV.B.l. IHC with Fluorometer

[0118] Figure 4 is a flowchart illustrating a method 400 for determining a quantity of cells expressing CD 13 and a complementary marker using immunohistochemistry with a fluorometer in accordance with various embodiments. The complementary marker may be one selected from the group consisting of CDllc, CD15, CD16, and HLA-DR.

[0119] Step 402 includes staining a bone marrow sample with fluorophore conjugated antibodies for complementary binding to CD 13 and the complementary marker. The bone marrow sample is a bone marrow tissue sample in these examples. Examples of antibodies (or reagents) and the fluorochromes that they may be conjugated with for use in step 402 are shown in Table 3 in Section IV.E below.

[0120] Step 404 includes applying excitation energy to the bone marrow sample.

[0121] Step 406 includes measuring a fluorescence emission level from the bone marrow sample to determine the quantity of cells that express CD 13 and the complementary marker. IV.B.2. IHC with Cell Counting

[0122] Figure 5 is a flowchart illustrating a method 500 for determining a quantity of cells expressing CD 13 and a complementary marker using immunohistochemistry with cell counting in accordance with various embodiments. The complementary marker may be one selected from the group consisting of CDllc, CD15, CD16, and HLA-DR.

[0123] Step 502 includes staining a bone marrow sample with fluorophore conjugated antibodies for complementary binding to CD 13 and the complementary marker. The bone marrow sample is a bone marrow tissue sample in these examples. In step 502, staining the bone marrow sample includes using at least two different types or colors of fluorophore conjugated antibodies. Examples of antibodies (or reagents) and the fluorochromes that they may be conjugated with for use in step 502 are shown in Table 3 in Section IV.E below. [0124] Step 504 includes applying excitation energy to the bone marrow sample.

[0125] Step 506 includes imaging the bone marrow sample using a set of wavelength filters corresponding to the fluorophore conjugated antibodies.

[0126] Step 508 includes counting the cells captured via the imaging to determine the quantity of cells that express CD 13 and the complementary marker. This counting may be performed manually or may be automated.

IV.C Analysis using Gene Expression

[0127] In some embodiments of the methods and systems provided herein for the analysis of expression of nucleic acids in a sample, for example, a bone marrow sample.

IV.C.l. Single-cell Sequencing

[0128] Figure 6 is a flowchart illustrating a method 600 for determining expression levels of markers in single cells for hemodilution assessment using single-cell sequencing in accordance with various embodiments. The markers can include CD13 and one or more complementary markers such as CDllc, CD15, CD16, and HLA-DR or any combination thereof.

[0129] At step 602, cells can be isolated or selected from which RNA is to be extracted, for example, from a bone marrow sample. The bone marrow sample may be readily available from storage or can be isolated before cell isolation. In various embodiments, the method may comprise separating a population of cells (e.g., by flow cytometry, microfluidic partitioning or separation, etc.) to provide a plurality of single cells, for example, by separating them into individual compartments, for example, individual wells of a plate or individual droplets. In some embodiments, barcode-based multiplexing can be provided to allow sequenced cDNA to be traced to a particular cell from among a subset of cells. Any of the foregoing (or any of the nucleic acids, reagents, kits, and methods described herein may be provided and/or used alone or in any combination). To mitigate batch effects, all laboratory procedures, for both the control and the experimental group(s), can be done on the same day, in the same lab and performed by the same person.

[0130] At step 604, mRNA can be extracted from lysed cells, especially targeted mRNA molecules associated with expression of any target marker (e.g., CD13 and one or more complementary markers such as CDllc, CD15, CD16, and HLA-DR or any combination thereof). In various embodiments, total RNA can be extracted from the cells or tissue sample, for example, a bone marrow sample. Ribosomial RNA (rRNA) and novel classes of RNA (ncRNA) such as micro RNA (miRNA) can be removed from the total RNA. Probes complementary to one or more target markers can be used to extract targeted mRNA molecules associated with expression of CD 13 and one or more complementary markers. In some embodiments, Unique Molecular Identifiers (UMIs) (e.g., polynucleotides comprising UMIs) can be provided, thereby acting as a robust guard against amplification biases. UMIs can specifically tag individual cDNA species as they are created from mRNA. Each UMI can enable a sequenced cDNA to be traced back to a single particular mRNA molecule that was present in a cell.

[0131] At step 606, the targeted mRNA molecules can be sequenced using any available sequencing method to obtain sequencing data. Sequencing methods can include, but are not limited to, next-generating sequencing (“NGS”), microarray sequencing, or RT-PCR. The targeted mRNA molecules can be used to make cDNA through cDNA synthesis. cDNA synthesis can be followed by library preparation, PCR amplification and sequencing such as NGS sequencing to produce either single-end or paired-end reads.

[0132] At step 608, an expression level of CD13 and one or more complementary cell surface markers for each isolated cell can be determined using the sequencing data obtained from the targeted mRNA molecules. The sequence reads can be checked for quality. Short reads and low-quality reads can be removed to improve quality. Sequence reads can be aligned into transcripts through reference-based mapping. The abundance of reads can be mapped for each target marker to obtain an expression level of each target marker.

[0133] At step 610, cells are grouped into different populations based on an expression level of CD 13 and one or more of the complementary cell surface markers. For example, cells expressing CD13 and CD1 lc can be separated from cells expressing CD13 but not expressing CD1 lc. Numbers of cells in each group can then be counted. The expression pattern of CD13 and one or more of the complementary markers in cells, for example, the number of cells that are expressing the markers can be used as an indicator for hemodilution.

[0134] A method for monitoring progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject is also provided. The method can comprise determining a quantity of cells expressing CD 13 and a complementary marker in the bone marrow sample and correlating the quantity of cells to a level of hemodilution of the bone marrow sample. The method can further comprise assessing whether the level of hemodilution passes a pre-set hemodilution criteria and ascertaining a progression of the disease or disorder in the subject with the bone marrow sample if the level of hemodilution passes the pre-set hemodilution criteria. A bone marrow sample with a level of hemodilution that passes a pre set hemodilution criteria indicates that the bone marrow sample can be analyzed and used to accurately track progression of a disease or disorder. In some embodiments, the pre-set hemodilution criteria is a percent hemodilution value for the bone marrow sample. For example, the pre-set hemodilution criteria can be, but is not limited to: 0% hemodilution, 5% hemodilution, 10% hemodilution, 15% hemodilution, 20% hemodilution, 25% hemodilution, 30% hemodilution, 35% hemodilution, 40% hemodilution, 45% hemodilution, or 50% hemodilution of the bone marrow sample.

[0135] A method for determining the effectiveness of a therapeutic regimen in treating a disease or disorder in a subject is also provided. The method can comprise analyzing a bone marrow sample obtained from the subject being treated with the therapeutic regimen. The method can comprise determining a quantity of cells expressing CD 13 and a complementary marker in the bone marrow sample and correlating the quantity of cells to a level of hemodilution of the bone marrow sample. The method can further comprise assessing whether the level of hemodilution passes a pre-set hemodilution criteria. A bone marrow sample with a level of hemodilution that passes a pre-set hemodilution criteria indicates that the bone marrow sample can be analyzed and used to evaluate the effectiveness of the therapeutic regimen in treating a disease or disorder. The method can further comprise verifying an improvement of the disease or disorder in the subject based if the expression level of CD13 and the complementary marker passes the pre-set hemodilution criteria.

IV.C.2. Bulk-cell Sequencing

[0136] The foregoing is also applicable to populations of cells, cell lysates, tissue lysates, and/or extracted/purified RNA. For example, nucleic acids, kits, and methods can also be provided for sequencing of extracted/purified RNA (bulk RNA sequencing) or for analysis of an isolated population of cells (e.g., from an isolated population of cells or a tissue; analysis of a cell or tissue lysate). In certain embodiments, any of the compositions, reagents, and methods described herein as applicable to single cells are also applicable to other sources of starting materials, such as extracted RNA, purified RNA, cell lysates, or tissue lysates, and such application is contemplated. In certain embodiments, any of the compositions, reagents, and methods described herein as applicable to extracted RNA, purified RNA, cell lysates or tissue lysates, are also applicable to single cells, and such application is contemplated extracting targeted mRNA molecules associated with expression of CD13 and the complementary cell surface marker (e.g., cell surface protein) from the bone marrow sample.

[0137] Figure 7 is a flowchart illustrating a method for determining expression levels of markers in a plurality of cells for hemodilution assessment using bulk-cell sequencing in accordance with various embodiments. The markers can include CD13 and one or more complementary markers such as CDllc, CD15, CD16, and HLA-DR or any combination thereof.

[0138] At step 702, targeted mRNA molecules associated with expression of CD13 and the complementary marker can be extracted from a bone marrow sample that comprise a plurality of cells. The bone marrow sample can be from a single individual or may be from a plurality of individuals.

[0139] At step 704, the targeted mRNA molecules can be sequenced using any available sequencing method to obtain sequencing data. Sequencing methods can include, but not be limited to, next-generating sequencing (“NGS”), microarray sequencing, or RT-PCR. [0140] At step 706, an expression level of CD13 and one or more complementary markers in the bone marrow sample can be determined using sequence data obtained from the targeted mRNA molecules. The expression level of CD 13 and one or more complementary markers can be used as an indicator of a count of the number of cells that express CD13 and one or more complementary markers and an indicator for hemodilution. In various embodiments, a standard curve showing a plurality of expression levels of CD 13 and one or more complementary markers in the bone marrow sample and a plurality of hemodilution levels can be correlated to provide a way to determine a hemodilution level of a bone marrow sample. [0141] A method for monitoring progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject is also provided. The method can comprise extracting targeted mRNA molecules associated with CD 13 and the complementary marker from the bone marrow sample. The method can further comprise sequencing the targeted mRNA molecules extracted from the bone marrow sample using any available sequencing method to obtain sequencing data. Sequencing methods can include, but not be limited to, next-generating sequencing (“NGS”), microarray sequencing, or RT-PCR. The method can further comprise determining an expression level of CD 13 and the complementary marker in the bone marrow sample using sequence data obtained from the targeted mRNA molecules. The method can further comprise assessing whether the expression level of CD13 and the complementary marker passes a pre-set complementary marker expression criteria. The method can further comprise ascertaining a progression of the disease or disorder in the subject with the bone marrow sample if the expression level of CD13 and the complementary marker passes the pre-set complementary marker expression criteria. The expression level of CD 13 and the complementary marker passing the pre-set complementary marker expression criteria indicates that the bone marrow sample can be analyzed and used to accurately track the progression of a disease or disorder.

[0142] A method for monitoring effectiveness of a therapeutic regimen in treating a disease or disorder can be provided by analyzing a bone marrow sample obtained from a subject being treated with the therapeutic regimen. The method can comprise extracting targeted mRNA molecules associated with CD 13 and the complementary marker from the bone marrow sample. The method can further comprise sequencing the targeted mRNA molecules extracted from the bone marrow sample using any available sequencing method to obtain sequencing data. Sequencing methods can include, but not be limited to, next-generating sequencing (“NGS”), microarray sequencing, or RT-PCR. The method can further comprise determining an expression level of CD 13 and the complementary marker in the bone marrow sample using sequence data obtained from the targeted mRNA molecules. The method can further comprise assessing whether the expression level of CD13 and the complementary marker passes a pre set complementary marker expression criteria. The pre-set complementary marker expression criteria can be determined by any suitable statistical method as a cut-off value to determine hemodilution. The expression level of CD 13 and the complementary marker passing the pre set complementary marker expression criteria indicates that the bone marrow sample can be analyzed and used to evaluate the effectiveness of the therapeutic regimen in treating a disease or disorder. The method can further comprise verifying an improvement of the disease or disorder in the subject based if the expression level of CD13 and the complementary marker passes the pre-set complementary marker expression criteria.

IV.D Analysis using Statistical Distancing

[0143] Figure 8 is a flowchart illustrating a method 800 for determining a hemodilution of a bone marrow sample using statistical distancing in accordance with various embodiments. [0144] Step 802 includes analyzing the bone marrow sample to identify a cell distribution for cells in the bone marrow sample expressing one or more cell surface markers. The one or more cell surface markers may be, for example, either a single cell surface protein or a combination of multiple cell surface proteins that, when expressed on a cell, provides some indication of whether that cell is a bone marrow cell or a peripheral blood cell. For example, the cells may be a cell subpopulation expressing CD71, a cell subpopulation expressing CD33 and CD117, a cell subpopulation expressing CD19, a cell subpopulation expressing CD33, or a cell subpopulation expressing CD56 and CD13.

[0145] Step 804 includes calculating a statistical distancing score for the cell distribution. The statistical distancing score can be calculated using a metric technique selected from a group consisting of Earth Mover’s Distance, frequency difference gating, probability binning, cytometric fingerprinting, and quadratic form. In some cases, the statistical distancing score may be an overall computed on the weighted individual scores generated using two or more of the above-described metric techniques. As one example, the statistical distancing score is computed using Earth Mover’s Distance based on a distance between the cell distribution for the cells in the bone marrow sample expressing the one or more cell surface markers and a control cell distribution. The control cell distribution may be, for example, for a control sample with about 0% hemodilution.

[0146] Step 807 includes correlating the statistical distancing score to a level of hemodilution of the bone marrow sample. A linear correlation exists between the statistical distancing score for the cell distribution and the level of hemodilution in the bone marrow sample. For example, the statistical distancing score may be used to determine a percent hemodilution of the bone marrow sample. The bone marrow sample is considered as having a low hemodilution state if the percent hemodilution is less than a selected threshold, wherein this threshold is selected from one of about 100%, about 75%, about 50%, or about 25%. If the percent hemodilution (or the statistical distancing score) does not pass certain pre-set criteria, one or more analytic measurements taken from the bone marrow sample may be proportionally adjusted to the percent hemodilution (or the statistical distancing score).

[0147] In some examples, the one or more cell surface markers includes CD71 and an increased statistical distancing score for the cell distribution expressing CD71 indicates increased hemodilution in the bone marrow sample. In other examples, the one or more cell surface markers includes CD33 and CD117 and an increased statistical distancing score for the cell distribution expressing CD33 and CD 117 indicates increased hemodilution in the bone marrow sample. In yet other examples, the one or more cell surface markers includes CD19 and an increased statistical distancing score for the cell distribution expressing CD 19 indicates increased hemodilution in the bone marrow sample. In some examples, the one or more cell surface markers includes CD56 and CD 13 and an increased statistical distancing score for the cell distribution expressing CD56 and CD13 indicates increased hemodilution in the bone marrow sample. In other examples, the one or more cell surface markers includes CD33 and an increased statistical distancing score for the cell distribution expressing CD33 indicates increased hemodilution in the bone marrow sample.

IV.E Kits for Performing the Analysis of Sections V.A-D [0148] Kits can be provided for performing the methods in accordance with various embodiments. Such kits can be prepared from readily available materials and reagents. For example, such kits can comprise any one or more of the following materials: enzymes, reaction tubes, buffers, detergent, primers and probes. In a particular embodiment, these kits allow a practitioner to measure cells expressing CD 13 and one or more complementary markers in a bone marrow sample, for example, using flow cytometry or IHC. Instructions for performing the assays can also be included in the kits. In various embodiments, the kits can comprise one, two, three, four, five, six or more antibodies for complementary binding to CD 13 and one or more complementary markers.

[0149] In various other embodiments, these kits can comprise a plurality of agents for assessing the expression of a plurality of markers, including CD13 and one or more complementary markers. The kit can be housed in a container. The kits can further comprise instructions for using the kit for assessing expression, converting the expression data into expression values and/or for analyzing the expression values to generate scores that predict hemodilution of the bone marrow sample. The agents in the kit for measuring marker expression can comprise a plurality of targeted mRNA capture reagents, PCR probes and/or primers for qRT-PCR and/or a plurality of antibody or fragments thereof for assessing expression of the markers or plurality of sequencing agents. For example, the agents in the kit for measuring marker expression can comprise a plurality or an array of polynucleotides complementary to the mRNA of one or more markers.

[0150] Any of the compositions or components described herein can be comprised in a kit. In a non-limiting example, reagents for isolating mRNA, labeling mRNA, and/or evaluating a mRNA population using an array or a sequencing method, nucleic acid amplification, and/or hybridization can be included in a kit, as well reagents for preparation of samples from bone marrow samples. The kit may further include reagents for creating or synthesizing targeted mRNA capture agents. In certain aspects, the kit can include amplification reagents. In other aspects, the kit can include various supports, such as glass, nylon, polymeric beads, magnetic beads, and the like, and/or reagents for coupling any probes and/or target nucleic acids. It can also include one or more buffers, such as reaction buffer, labeling buffer, washing buffer, or a hybridization buffer, compounds for preparing the targeted mRNA capture agents, and components for isolating mRNA. Other kits can include components for making a nucleic acid array comprising miNA, and thus, may include, for example, a solid support. [0151] Kits for implementing methods described herein are specifically contemplated. In some embodiments, there are kits for preparing and using targeted mRNA capture agents. The components of the kits may be packaged either in aqueous media or in lyophilized form. The container means of the kits will generally include at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed and suitably aliquoted. Where there is more than one component in the kit (labeling reagent and label may be packaged together), the kit also will generally contain a second, third or other additional container into which the additional components may be separately placed. However, various combinations of components can be comprised in a vial. The kits can also include a container for containing the nucleic acids, and any other reagent containers in close confinement for commercial sale. Such containers can include injection or blow molded plastic containers into which the desired vials are retained.

[0152] When the components of the kit are provided in one and/or more liquid solutions, the liquid solution is an aqueous solution, with a sterile aqueous solution being particularly preferred. However, the components of the kit may be provided as dried powder(s). When reagents and/or components are provided as a dry powder, the powder can be reconstituted by the addition of a suitable solvent. It is envisioned that the solvent may also be provided in another container means. In some embodiments, labeling dyes are provided as a dried power. It is contemplated that 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, 1000 pg or at least or at most those amounts of dried dye are provided in kits.

[0153] The container of kits can include at least one vial, test tube, flask, bottle, syringe and/or other container means, into which the nucleic acid formulations are placed, preferably, suitably allocated. The kits can also comprise a second container means for containing a sterile, pharmaceutically acceptable buffer and/or another diluent.

[0154] Examples of different antibody reagents that may be used for binding to various cell surface markers is shown below in Table 3: Table 3: Reagents for IHC with Fluorometer and Flow Cytometry

V. Exemplary Computer-Implemented System

[0155] In various embodiments, at least a portion of the methods for assessing hemodilution in bone marrow samples and identifying selected expression profiles for use in hemodilution assessment can be implemented via software, hardware, firmware, or a combination thereof.

[0156] That is, as depicted in Figure 1, the methods disclosed herein can be implemented on a computer system such as computer system 102 (e.g., a computing device/analytics server). In various embodiments, the computer system 102 can be communicatively connected to a data storage 105 and a display system 106 via a direct connection or through a network connection (e.g., LAN, WAN, Internet, etc.). It should be appreciated that the computer system 102 depicted in Figure 1 can comprise additional engines or components as needed by the particular application or system architecture.

[0157] Figure 9 is a block diagram of a computer system in accordance with various embodiments. Computer system 900 may be an example of one implementation for computer system 102 described above in Figure 1. In one or more examples, computer system 900 can include a bus 902 or other communication mechanism for communicating information, and a processor 904 coupled with bus 902 for processing information. In various embodiments, computer system 900 can also include a memory, which can be a random access memory (RAM) 906 or other dynamic storage device, coupled to bus 902 for determining instructions to be executed by processor 904. Memory also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 904. In various embodiments, computer system 900 can further include a read only memory (ROM) 908 or other static storage device coupled to bus 902 for storing static information and instructions for processor 904. A storage device 910, such as a magnetic disk or optical disk, can be provided and coupled to bus 902 for storing information and instructions.

[0158] In various embodiments, computer system 900 can be coupled via bus 902 to a display 912, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device(s) 914, including alphanumeric and other keys, can be coupled to bus 902 for communicating information and command selections to processor 904. Another type of user input device is a cursor control 916, such as a mouse, a joystick, a trackball, a gesture input device, a gaze -based input device, or cursor direction keys for communicating direction information and command selections to processor 904 and for controlling cursor movement on display 912. This cursor control 916typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. However, it should be understood that cursor control(s) 916 and other such input devices 914 allowing for three-dimensional (e.g., x, y and z) cursor movement are also contemplated herein.

[0159] Consistent with certain implementations of the present teachings, results can be provided by computer system 900 in response to processor 904 executing one or more sequences of one or more instructions contained in RAM 906. Such instructions can be read into RAM 906 from another computer-readable medium or computer-readable storage medium, such as storage device 910. Execution of the sequences of instructions contained in RAM 1606 can cause processor 904 to perform the processes described herein. Alternatively, hard-wired circuitry can be used in place of or in combination with software instructions to implement the present teachings. Thus, implementations of the present teachings are not limited to any specific combination of hardware circuitry and software.

[0160] The term "computer-readable medium" (e.g., data store, data storage, storage device, data storage device, etc.) or "computer-readable storage medium" as used herein refers to any media that participates in providing instructions to processor 904 for execution. Such a medium can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Examples of non-volatile media can include, but are not limited to, optical, solid state, magnetic disks, such as storage device 910. Examples of volatile media can include, but are not limited to, dynamic memory, such as RAM 906. Examples of transmission media can include, but are not limited to, coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 902.

[0161] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other tangible medium from which a computer can read.

[0162] In addition to computer readable medium, instructions or data can be provided as signals on transmission media included in a communications apparatus or system to provide sequences of one or more instructions to processor 904 of computer system 900 for execution. For example, a communication apparatus may include a transceiver having signals indicative of instructions and data. The instructions and data are configured to cause one or more processors to implement the functions outlined in the disclosure herein. Representative examples of data communications transmission connections can include, but are not limited to, telephone modem connections, wide area networks (WAN), local area networks (LAN), infrared data connections, NFC connections, optical communications connections, etc. [0163] It should be appreciated that the methodologies described herein, flow charts, diagrams, and accompanying disclosure can be implemented using computer system 900 as a standalone device or on a distributed network of shared computer processing resources such as a cloud computing network.

[0164] The methodologies described herein may be implemented by various means depending upon the application. For example, these methodologies may be implemented in hardware, firmware, software, or any combination thereof. For a hardware implementation, the processing unit may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.

In various embodiments, the methods of the present teachings may be implemented as firmware and/or a software program and applications written in conventional programming languages such as C, C++, Python, etc. If implemented as firmware and/or software, the embodiments described herein can be implemented on a non-transitory computer-readable medium in which a program is stored for causing a computer to perform the methods described above. It should be understood that the various engines described herein can be provided on a computer system, such as computer system 900, whereby processor 904 would execute the analyses and determinations provided by these engines, subject to instructions provided by any one of, or a combination of, the memory components RAM 906, ROM, 908, or storage device 910 and user input provided via input device 914.

VI. Examples

VI.A Flow Cytometry

V1.A.1. Method of Simulation/Experimentation for Analysis via Flow

Cytometry

[0165] Figure 10 is a flowchart illustrating a method for identifying an expression profile of interest in accordance with various embodiments. Method 1000 may be implemented using, for example, analysis system 100 described with respect to Figure 1 or a similar analysis system. [0166] Step 1002 includes obtaining a bone marrow test sample and a blood test sample from each of a plurality of subjects. In these examples, a medical professional may obtain the bone marrow test sample via bone marrow aspiration and the blood test sample, which is whole blood, via, for example, a blood draw. The number of subjects in the plurality of subjects may be, for example, 3, 4, 5, 11, 25, 50, 110, 500, 1100, or some other number. In these examples, each of the bone marrow test sample and the blood test sample are of sufficient volume (i.e., contain a sufficient number of cells) to enable the analysis of method 1000. In various embodiments, about 110pL of the bone marrow test sample and about 110pL of the blood test sample may be used for analysis.

[0167] Step 1004 includes analyzing the bone marrow test samples and the blood test samples using flow cytometry to generate data. This data may be referred to as initial cell data. This initial cell data may include characteristics of the bone marrow test samples and blood test samples including, for example, but not limited to, a count of cells in each of these different test samples and an identification of one or more cell surface markers (e.g., CD13, CDllc, CD16, CD15, HLA-DR, etc.) expressed on each of these cells. In various embodiments, step 1004 may also include using automated gating (e.g., flowDensity, AutoGate) to ensure that this initial cell data is only generated for singlets (single cells).

[0168] Step 1006 includes using the initial cell data to generate simulated samples, each of the simulated samples being a computationally mixed sampling of cells from the bone marrow test sample and from the blood test sample. For example, for each hemodilution level of interest (e.g., 0%, 25%, 50%, 75%, and0%), multiple simulated samples (e.g., 5, 10, 20, 100, or some other number of simulated samples, etc.) may be generated for each of the plurality of subjects. In this manner, each of these simulated samples is a computational mixture of a random sampling of cells from the bone marrow test sample and from the blood test sample. In some examples, ten simulated samples may be generated for each hemodilution level for each of the plurality of subjects. Table 4 below illustrates an example of how a simulated sample can be generated for different hemodilution levels. Table 4: Generating Simulated Samples

[0169] Step 1008 includes analyzing the plurality of simulated samples using automated gating based on a panel of cell surface markers (e.g., one or more of CD5, CD10, CDllc, CD13, CD15, CD16, CD19, CD33, CD34, CD38, CD45, CD56, CD57, CD71, CD117, HLA-

DR, etc.) to identify a set of expression profiles that correlate with hemodilution level. For example, a computational workflow may be used to partition single cell data based on multiple expression profiles and identify the particular one or more expression profiles that most strongly correlate with hemodilution level. The computational workflow may use any number of or combination of data analysis tools selected from the group including, but not limited to, flowDensity, flowType, RchyOptimyx, FAUST, diffcyt, flowMeans, flowSOM, phonograph, flowCut, CytoML, flowPeaks, densityClust, flowWorkspace, flowCore, and CytoDx.

[0170] In various embodiments, prior to the automated gating in step 1008, gating is used to exclude outlier events according to signal stability over acquisition time. Automated gating is used to partition the cells in each simulated sample into parent populations based on cell type (e.g., myeloid cells, monocytes, erythrocytes, blasts, lymphocytes, etc.).

[0171] The distribution of side scatter (SSC) and CD45 expression is analyzed to filter for singlets and exclude doublets. Unsupervised density-based clustering is applied to the singlets to identify clusters corresponding to myeloid cells and lymphocytes based on the position of their centroids. These clusters are fed into a support vector machine algorithm to further refine the gate boundaries for lymphocytes and myeloid cells. [0172] After filtering for singlets and excluding lymphocytes and myeloid cells, the distribution of side scatter area (SSC-A) and CD45 expression is analyzed to gate for monocytes and erythrocytes.

[0173] After filtering for singlets and excluding lymphocytes, myeloid cells, and monocytes, density-based clustering of the remaining singlets and the distribution of 0045^™ expression is used to identify blasts. 0045^™ cells are those with dim expression of CD45. [0174] Thus, automated gating is used to identify various parent populations (e.g., myeloid cells, monocytes, erythrocytes, blasts, lymphocytes, etc.). Each parent population is then partitioned into at least four subpopulations per combination of cell surface markers for each of the panel of cell surface makers.

[0175] As one example, for the particular combination of CD 13 and CD 15, myeloid cells may be partitioned into cell subpopulations corresponding to the following expression profiles (or phenotypes): CD13+CD15+, CD13-CD15-, CD13+CD15-, and CD13-CD15+. In other examples, manual gating may be used instead of the automated gating in step 1008. The data obtained via manual gating may be performed by an analyst performing visual assessment of flow cytometry biplots (e.g., CD45 vs Side scatter, CD13 vs CD15, etc.)

[0176] Step 1010 includes employing one or more algorithms, modeling techniques, or both to identify the one or more expression profiles that mostly strongly correlate with hemodilution level. For example, linear regression modeling is performed using the frequencies of cells matching the expression profiles with hemodilution level as an independent variable. The coefficient of determination (R²) is used to identify the one or more expression profiles for the cell subpopulations that are highly correlated with hemodilution level. These one or more expression profiles may then be selected for use in future quantification of hemodilution levels in bone marrow samples. In these examples, the expression profile showing the greatest variation between hemodilution levels is the expression profile with the strongest correlation to hemodilution level.

[0177] In various embodiments, a process similar to the method 1000 described in Figure 10 is performed using experimentally mixed samples. In these other examples, steps 1004 and 1006 may be optionally replaced with a step for creating a plurality of experimentally mixed samples. For example, for a particular hemodilution level, a first amount of the bone marrow test sample and a second amount of the blood test sample may be mixed such that the ratio of blood to bone marrow matches the particular hemodilution level.

VI.A.2. Experimental Results for Analysis using Flow Cytometry

[0178] Figures 11-21 are plots illustrating relationships between certain cell subpopulations and hemodilution in accordance with various embodiments. These plots are an example of plots generated during the analysis of steps 408 and/or 410 described above with respect to flow cytometric data generated for different cell subpopulations for the same three human subjects (e.g., donors): Subject A, Subject B, and subject C. Further, at least Figures 11, 14, 16, 18, and 20 may be plots generated based on simulated samples created via the computational mixing of bone marrow and blood according to the method 1000 described above with respect to Fig. 10. For example, the plots in Figures 11, 14, 16, 18, and 20 may be plots generated during for the analysis of step 1008 in Figure 10.

[0179] Figure 11 is a plot 1102 showing the relationship between the cell subpopulation matching the expression profile CD13+CDllc- and hemodilution level in accordance with various embodiments. Plot 1102 is based on simulated samples generated via computational mixing of bone marrow and blood. Plot 1102 includes y-axis 1104 for the percentage of cells in a sample that match the expression profile CD13+CD1 lc- and x-axis 1106 for hemodilution level. These percentages are tracked for Subject A, Subject B, and Subject C via curve 1108, curve 1110, and curve 1112, respectively. As shown by plot 1102, as the hemodilution level increases, the percentage of cells with the expression profile CD13+CDllc- decreases in a substantially linear manner. This substantially linear correlation enables the determination or measurement of hemodilution based on a given percentage of this particular cell subpopulation in a blood marrow sample.

[0180] Figure 12 is a plot 1202 showing the relationship between the cell subpopulation matching the expression profile CD13+CDllc- and hemodilution level in accordance with various embodiments. Plot 1202 is based on samples created via experimental mixing of bone marrow and blood. Plot 1202 includes y-axis 1204 for the percentage of cells in a sample that match the expression profile CD13+CDllc- and x-axis 1206 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 1208, curve 1210, and curve 1212, respectively. As shown by plot 1202, as the hemodilution level increases, the percentage of cells with the expression profile CD13+CDllc- generally decreases in a substantially linear manner. This correlation generally validates the findings of plot 1102 with respect to the simulated samples.

[0181] Figure 13 is a plot series 1302 that demonstrates that the expression profile CD13+CDllc- has a strong correlation with hemodilution level in accordance with various embodiments. Plot series 1302 includes plots 1304, 1306, 1308, 1310, and 1312, each of which corresponds to a different hemodilution level. Plot 1304 corresponds to 100% hemodilution; plot 1306 corresponds to 75% hemodilution, plot 1308 corresponds to 50% hemodilution, plot 1310 corresponds to 25% hemodilution, and plot 1312 corresponds to 0% hemodilution. The y-axis for each of plots 1304, 1306, 1308, 1310, and 1312 is the measurement of CD13 expression on a logarithmic scale; the x-axis for each of plots 1304, 1306, 1308, 1310, and 1312 is the measurement of CDllc expression on a logarithmic scale.

[0182] The upper left quadrants of plots 1304, 1306, 1308, 1310, and 1312 represents those cells that express CD13 (CD13+) but do not express CDllc (CDllc-). As the hemodilution level increases, the number of cells that express CD13 decreases and the number of cells that do not express CDllc decreases such that the number of cells that fall within the upper quadrant decreases in a manner that aligns with the findings illustrated via plot 1102 in Figure 11 and plot 1202 in Figure 12.

[0183] Figure 14 is a plot 1402 showing the relationship between the cell subpopulation matching the expression profile CD13+CD15+ and hemodilution level in accordance with various embodiments. Plot 1402 is based on simulated samples generated via computational mixing of bone marrow and blood. Plot 1402 includes y-axis 1404 for the percentage of cells in a sample that match the expression profile CD13+CD15+ and x-axis 1406 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 1408, curve 1410, and curve 1412, respectively. As shown by plot 1402, as the hemodilution level increases, the percentage of cells with the expression profile CD13+CD15+ decreases in a substantially linear manner. This substantially linear correlation enables the determination or measurement of hemodilution based on a given percentage of this particular cell subpopulation in a blood marrow sample.

[0184] Figure 15 is a plot 1502 showing the relationship between the cell subpopulation matching the expression profile CD13+CD15+ and hemodilution level in accordance with various embodiments. Plot 1502 is based on samples created via experimental mixing of bone marrow and blood. Plot 1502 includes y-axis 1504 for the percentage of cells in a sample that match the expression profile CD13+CD15+ and x-axis 1506 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 1508, curve 1510, and curve 1512, respectively. As shown by plot 1502, as the hemodilution level increases, the percentage of cells with the expression profile CD13+CD15+ generally decreases in a substantially linear manner. This correlation generally validates the findings of plot 1402 with respect to the simulated samples.

[0185] Figure 16 is a plot 1602 showing the relationship between the cell subpopulation matching the expression profile CD13+CD16+ and hemodilution level in accordance with various embodiments. Plot 1602 is based on simulated samples generated via computational mixing of bone marrow and blood. Plot 1602 includes y-axis 1604 for the percentage of cells in a sample that match the expression profile CD13+CD16+ and x-axis 1606 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 1608, curve 1610, and curve 1612, respectively. As shown by plot 1602, as the hemodilution level increases, the percentage of cells with the expression profile CD13+CD16+ decreases in a substantially linear manner. This substantially linear correlation enables the determination or measurement of hemodilution based on a given percentage of this particular cell subpopulation within a blood marrow sample.

[0186] Figure 17 is a plot 1702 showing the relationship between the cell subpopulation matching the expression profile CD13+CD16+ and hemodilution level in accordance with various embodiments. Plot 1702 is based on samples created via experimental mixing of bone marrow and blood. Plot 1702 includes y-axis 1704 for the percentage of cells in a sample that match the expression profile CD13+CD16+ and x-axis 1706 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 1708, curve 1710, and curve 1712, respectively. Plot 1702 illustrates that as the hemodilution level increases, the percentage of cells with the expression profile CD13+CD16+ generally decreases in a substantially linear manner. This correlation generally validates the findings of plot 1602 with respect to the simulated samples.

[0187] Figure 18 is a plot 1802 showing the relationship between the cell subpopulation matching the expression profile CD13+HLA-DR- and hemodilution level in accordance with various embodiments. Plot 1802 is based on simulated samples generated via computational mixing of bone marrow and blood. Plot 1802 includes y-axis 1804 for the percentage of cells in a sample that match the expression profile CD13+HLA-DR- and x-axis 1806 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 1808, curve 1810, and curve 1812, respectively. As shown by plot 1802, as the hemodilution level increases, the percentage of cells with the expression profile CD13+HLA- DR- decreases in a substantially linear manner. This substantially linear correlation enables the determination or measurement of hemodilution based on a given percentage of this particular cell subpopulation in a blood marrow sample.

[0188] Figure 19 is a plot 1902 showing the relationship between the cell subpopulation matching the expression profile CD13+HLA-DR- and hemodilution level in accordance with various embodiments. Plot 1902 is based on samples created via experimental mixing of bone marrow and blood. Plot 1902 includes y-axis 1904 for the percentage of cells in a sample that match the expression profile CD13+HLA-DR- and x-axis 1906 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 1908, curve 1910, and curve 1912, respectively. Plot 1902 illustrates that as the hemodilution level increases, the percentage of cells with the expression profile CD13+HLA-DR- generally decreases in a substantially linear manner. This correlation generally validates the findings of plot 1802 with respect to the simulated samples.

[0189] Figure 20 is a plot 2002 showing the relationship between the cell subpopulation matching the expression profile CD13-HLA-DR- and hemodilution level in accordance with various embodiments. Plot 2002 is based on simulated samples generated via computational mixing of bone marrow and blood. Plot 2002 includes y-axis 2004 for the percentage of cells in a sample that match the expression profile CD13-HLA-DR- and x-axis 2006 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 2008, curve 2010, and curve 2012, respectively. As shown by plot 2002, as the hemodilution level increases, the percentage of cells with the expression profile CD13-HLA- DR- also increases in a substantially linear manner. This substantially linear correlation enables the determination or measurement of hemodilution based on a given percentage of this particular cell subpopulation in a blood marrow sample. [0190] Figure 21 is a plot 2102 showing the relationship between the cell subpopulation matching the expression profile CD13-HLA-DR- and hemodilution level in accordance with various embodiments. Plot 2102 is based on samples created via experimental mixing of bone marrow and blood. Plot 2102 includes y-axis 2104 for the percentage of cells in a sample that match the expression profile CD13-HLA-DR- and x-axis 2106 for hemodilution level. These percentages are tracked for Subject A, Subject, B, and Subject C via curve 2108, curve 2110, and curve 2112, respectively. Plot 2102 illustrates that as the hemodilution level increases, the percentage of cells with the expression profile CD13-HLA-DR- generally also increases in a substantially linear manner. This correlation generally validates the findings of plot 2002 with respect to the simulated samples.

[0191] While Figures 11, 12, and 14-21 show a substantially linear correlation between the above-identified cell subpopulations and hemodilution level, it should be appreciated that other cell subpopulations may reveal different types of mathematical functions (e.g., logarithmic, exponential, etc.) or otherwise mathematically quantifiable relationships/correlations to hemodilution level.

VI.B Statistical Distancing

VI.B.l. Experimental Results for Analysis using Statistical

Distancing

[0192] Figures 22-26 are plots illustrating relationships between certain cell subpopulations and hemodilution in accordance with various embodiments. These plots are an example of plots generated during the analysis of steps 804 and 806 described above with respect to statistical distancing scores generated for different cell subpopulations for the same three human subjects (e.g., donors): Subject A, Subject B, and subject C.

[0193] Figure 22 is a plot 2200 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD71 and hemodilution level in accordance with various embodiments. In particular, plot 2200 includes the y-axis 2202 showing the Earth Mover’s Distance (EMD) score the cell subpopulation expressing CD71 and the x-axis 2204 for the hemodilution level (shown as a percentage). These percentages are tracked for Subject A, Subject, B, and Subject C via curves 2206. [0194] Figure 23 is a plot 2300 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD33 and hemodilution level in accordance with various embodiments. In particular, plot 2300 includes the y-axis 2302 showing the EMD score the cell subpopulation expressing CD33 and the x-axis 2304 for the hemodilution level (shown as a percentage). These percentages are tracked for Subject A, Subject, B, and Subject C via curves 2306.

[0195] Figure 24 is a plot 2400 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD33 and CD 117 and hemodilution level in accordance with various embodiments. In particular, plot 2400 includes the y-axis 2402 showing the EMD score the cell subpopulation expressing CD33 and CD 117 and the x-axis 2404 for the hemodilution level (shown as a percentage). These percentages are tracked for Subject A, Subject, B, and Subject C via curves 2406.

[0196] Figure 25 is a plot 2500 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD56 and CD 13 and hemodilution level in accordance with various embodiments. In particular, plot 2500 includes the y-axis 2502 showing the EMD score the cell subpopulation expressing CD56 and CD13 and the x-axis 2504 for the hemodilution level (shown as a percentage). These percentages are tracked for Subject A, Subject, B, and Subject C via curves 2506.

[0197] Figure 26 is a plot 2600 showing the relationship between a statistical distancing score for the cell subpopulation expressing CD 19 and hemodilution level in accordance with various embodiments. In particular, plot 2600 includes the y-axis 2602 showing the EMD score the cell subpopulation expressing CD 19 and the x-axis 2604 for the hemodilution level (shown as a percentage). These percentages are tracked for Subject A, Subject, B, and Subject C via curves 2606.

VI.B.2. Methodology for Analysis using Statistical Distancing

[0198] The statistical distancing scores discussed with respect to Figures 22-26 are EMD scores. Below is a discussion of how such EMD scores may be calculated.

[0199] EMD is computed by comparing two (or more) cell populations that are defined either by a manual or an automated gating algorithm. First cell populations are identified. Second, signatures are computed using adaptive binning, which are histogram-like approximations of the data, for each of the two cell populations. Once the signatures are generated, the EMD computation can be stated in terms of a linear programming problem. [0200] Cell Population Identification: Cell populations of interest are identified in preprocessed flow cytometry data. Cell populations of interest can be identified by manual analysis (e.g., FlowJo), automated analysis (e.g., AutoGate (http://CytoGenie.org/) or any other clustering algorithm), or by physical cell sorting procedure (FACS sort).

[0201] Fig. 27 is a plot series 2700 illustrating an example gating strategy performed in AutoGate to identify cell population of interests in accordance with various embodiments. In Figure 27, plot series 2700 includes plot 2702 for identifying singlets 2704, plot 2706 for identifying live singlets 2708, and plot 2710 for identifying myeloids 2712 and lymphocytes 2714.

[0202] Preprocessing steps may include flow cytometry data compensation, logicle transformation (Moore WA and Parks DR. Update for the logicle data scale including operational code implementations. Cytometry A. 2012; 81: 273-277.), and clustering the transformed data with DBM (Walther G, Zimmerman N, Moore W, Parks D, Meehan S, Belitskaya I, et al. Automatic clustering of flow cytometry data with density-based merging. Adv Bioinformatics. 2009; 686759-686765.). AutoGate may be used to perform the preprocessing steps; in other examples, other software libraries may be used. The flow cytometry data preprocessing methods used here do not require user input for parameters such as number of clusters, number of grid bins, density threshold, manual gating for compensation purposes, etc.

[0203] Signatures: For efficiency, distributions are summarized by signatures, which allow for more granularity in high-density areas of the data, and less granularity in sparse areas, i.e., signature bins are variable in size whereas histogram bins are typically derived from a fixed size partitioning of the distribution.

[0204] Formally, a signature { .s;, = (m_j, w_mj)} is represented by the mean (m,) of a group j of observations , and the fraction (w_mj) of observations that belong to the group j. The binning algorithm used to bin the data into the groups used in the signature is described by Roederer et al. (Roederer M, Moore W, Treister A, Hardy RR, Herzenberg FA. Probability binning comparison: a metric for quantitating multivariate distribution differences. Cytometry. 2001; 45: 37-46). Thus, first, the variance of the data is calculated for each of the parameters (dimensions) included in the analysis. Then, the dimension with the largest variance is chosen. The events are split into two bins along the median value in that dimension such that half of the events fall in each of the two resulting bins. Next, this process is recursively performed until a pre-defined threshold is met, e.g., 2ln(N) observations per bin, where /Vis a total number of events. For each bin to be split, the algorithm chooses the dimension that maximizes variance, splitting the data about the median value in that dimension. The result is a series of n-dimensional hyper-rectangular bins, each containing an equal number of events.

[0205] Computing EMD; Formally, EMD can be stated in terms of a linear programming problem: two distributions represented by signatures, P = {(p i,w_pi (p_m,w_Pm)} and Q={(qi,W_qi),...,(q_n,W_qn)} where p qt are bin centroids with frequencies w_Pi,w_qi, and D = [di_j] the matrix containing the Euclidean distances between p, and _<¾ for all i,j. To ensure that P and Q have the same total mass of unity (equal to 1), each of the two distributions is normalized. Next, a flow F = [fi_j] between p, and _<¾ is determined that minimizes the total cost: subject to the following constraints:

[0206] Constraint (2) ensures that mass is only transported in one direction (e.g., from the source sample to the destination sample). Constraints (3) and (4) limit the amount of mass that can be moved from/to a given signature bin to their respective weights; and, constraint (5) ensures that the amount of mass moved does not exceed the maximum possible amount. [0207] In the case of signatures with the same total mass, EMD is an accurate metric for distributions and is equivalent to the Mallow’s distance (Mallows CL. A Note on Asymptotic Joint Normality. Ann. Math. Statist. 1972; 43: 508-515) (demonstrated by Levina E and Bickel P. The earth mover’s distance is the Mallows distance: Some insights from statistics. Proc. ICCV. 2001; 2: 251-256). Thus, when applied to probability distributions, EMD has a clear probabilistic interpretation as the Mallow’s distance. Herein, the process ensures equal mass of two samples but retains the EMD notation.

[0208] Solving the above linear programming problem determines the optimal flow, F, between the source and destination signatures subject to constraints (2-5). Then. EMD is defined as a function of the optimal flow F=[fi_j] and the ground distance D=[di_j] [Error! Bookmark not defined.]:

As one example, EMD calculations may be performed using the code found at https://www.mathworks.com/matlabcentral/fileexchange/22962-the-earth-mover-s-distance. EMD performance for flow data analysis was initially examined by Zimmerman, who reports the robustness of EMD performance in terms of binning parameters and sample size in his doctoral thesis (Zimmerman N. A computational approach to identification and comparison of cell subsets in flow cytometry data. Ph.D. Thesis, Stanford University. 2011. Available at: https://stacks.stanford.edU/file/druid:hgl37hq6178/Zimmerman-Dissertation- v2- augmented.pdf). Comparisons of small populations of cells with low frequencies may require finer binning than comparisons of larger populations. However, as Zimmerman shows, overall EMD is robust regardless of the choice of the number of bins.

VII. Additional Considerations

[0209] The headers and subheaders between sections and subsections of this document are included solely for the purpose of improving readability and do not imply that features cannot be combined across sections and subsection. Accordingly, sections and subsections do not describe separate embodiments. [0210] Some embodiments of the present disclosure include a system including one or more data processors. In some embodiments, the system includes a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform part or all of one or more methods and/or part or all of one or more processes disclosed herein. Some embodiments of the present disclosure include a computer-program product tangibly embodied in a non- transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform part or all of one or more methods and/or part or all of one or more processes disclosed herein.

[0211] The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention as claimed has been specifically disclosed by embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims.

[0212] The ensuing description provides preferred exemplary embodiments only, and is not intended to limit the scope, applicability or configuration of the disclosure. Rather, the ensuing description of the preferred exemplary embodiments will provide those skilled in the art with an enabling description for implementing various embodiments. It is understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope as set forth in the appended claims.

[0213] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. [0214] In describing the various embodiments, the specification may have presented a method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the various embodiments.

[0215] All references cited herein, including patent applications, patent publications, and UniProtKB/Swiss-Prot Accession numbers are herein incorporated by reference in their entirety, as if each individual reference were specifically and individually indicated to be incorporated by reference.

Claims

CLAIMS What is claimed is:

1. A method for determining hemodilution of a bone marrow sample, comprising: analyzing the bone marrow sample to determine a quantity of cells in the bone marrow sample expressing a CD 13 protein and a complementary marker comprising a set of cell surface markers; and correlating the quantity of cells expressing the CD 13 protein and the complementary marker to a level of hemodilution of the bone marrow sample.

2. The method of claim 1, wherein the complementary marker includes CD1 lc and wherein a decreased quantity of cells in the bone marrow sample that are CD13+ and CD1 lc- indicates increased hemodilution in the bone marrow sample.

3. The method of claim 1, wherein the complementary marker includes CD 15 and wherein a decreased quantity of cells in the bone marrow sample that are CD 13+ and CD 15+ indicates increased hemodilution in the bone marrow sample.

4. The method of claim 1, wherein the complementary marker includes CD 16 and wherein a decreased quantity of cells in the bone marrow sample that are CD 13+ and CD 16+ indicates increased hemodilution in the bone marrow sample.

5. The method of claim 1, wherein the complementary marker includes a Human Leukocyte Antigen - DR isotype (HLA-DR) protein and wherein an increased quantity of cells in the bone marrow sample that are CD 13- and HLA-DR- indicates increased hemodilution in the bone marrow sample.

6. The method of claim 1, wherein the complementary marker includes a HLA-DR protein and wherein a decreased quantity of cells in the bone marrow sample that are CD 13+ and HLA-DR- indicates increased hemodilution in the bone marrow sample.

7. The method of any one of claims 2 to 6, wherein a linear correlation exists between the quantity of cells expressing the CD 13 protein and the complementary marker and hemodilution in the bone marrow sample.

8. The method of any one of claims 2 to 6, wherein the quantity of cells is determined using expression of 2, 3, 4, 5 or more complementary markers.

9. The method of any one of claims 1 to 8, wherein the quantity of cells expressing the CD 13 protein and the complementary marker is measured using a flow cytometer.

10. The method of claim 9, wherein the complementary marker includes one or more cell surface markers selected from the group consisting of CDllc, CD15, CD16, and HLA-DR.

11. The method of any one of claims 1 to 7, wherein the quantity of cells expressing the CD 13 protein and the complementary marker is determined by: staining the bone marrow sample with fluorophore conjugated antibodies for complementary binding to the CD 13 protein and the complementary marker; applying excitation energy to the bone marrow sample; and measuring a fluorescence emission level from the bone marrow sample to determine the quantity of cells.

12. The method of claim 11, wherein the complementary marker includes one or more cell surface markers selected from the group consisting of CDllc, CD15, CD16, and HLA- DR.

13. The method of any one of claims 1 to 7, wherein the quantity of cells expressing the CD 13 protein and the complementary marker is determined by: isolating each cell in the bone marrow sample; extracting targeted mRNA molecules associated with expression of the CD 13 protein and the complementary marker; sequencing the targeted mRNA molecules extracted from each isolated cell to obtain sequencing data; determining an expression level of the CD 13 protein and the complementary marker for each isolated cell using the sequence data obtained from the targeted mRNA molecules; and grouping cells which express the CD 13 protein and the complementary marker from cells that do not.

14. The method of claim 13, wherein the complementary marker includes one or more cell surface markers selected from the group consisting of CDllc, CD15, CD16, and HLA- DR.

15. The method of any one of claims 1 to 7, wherein the quantity of cells expressing the CD 13 protein and the complementary marker is determined by: extracting targeted mRNA molecules associated with expression of the CD 13 protein and the complementary marker from the bone marrow sample; sequencing the targeted mRNA molecules extracted from the bone marrow sample; and determining an expression level of the CD 13 protein and the complementary marker in the bone marrow sample using sequence data obtained from the targeted mRNA molecules to determine the quantity of cells.

16. The method of claim 15, wherein the complementary marker includes one or more cell surface markers selected from the group consisting of CDllc, CD15, CD16, and HLA- DR.

17. The method of any one of claims 1 to 16, further including: determining a percent hemodilution of the bone marrow sample.

18. The method of claim 17, wherein the percent hemodilution is calculated as the quantity of cells expressing the CD 13 protein and a complementary marker in the bone marrow sample relative to a total number of cells counted in the bone marrow sample.

19. The method of claim 18, further including: calling a low hemodilution state for the bone marrow sample if the percent hemodilution is less than about 25%.

20. The method of claim 18, further including: calling a low hemodilution state for the bone marrow sample if the percent hemodilution is less than about 50%.

21. The method of claim 18, further including: calling a low hemodilution state for the bone marrow sample if the percent hemodilution is less than about 75%.

22. The method of claim 18, further including: calling a low hemodilution state for the bone marrow sample if the percent hemodilution is less than about 100%.

23. The method of any one of claims 17 to 22, further including: adjusting one or more analytical measurements taken from the bone marrow sample proportionally to the percent hemodilution of the bone marrow sample.

24. The method of any one of claims 1-16, further including: adjusting one or more analytical measurements taken from the bone marrow sample proportionally to the expression level of the CD 13 protein and the complementary marker in the bone marrow sample.

25. The method of any one of claims 1-24, further including: generating a report based on the level of hemodilution to which the quantity of cells expressing the CD 13 protein and the complementary marker is correlated.

26. The method of claim 25, wherein the report includes an assessment of a quality of the bone marrow sample.

27. The method of claim 25 or claim 26, wherein the report includes a recommended action to be taken by an operator based on the level of hemodilution.

28. The method of any one of claims 25-27, further including: displaying the report on a display system to allow an operator to simultaneously view an assessment of the quality of the bone marrow sample and a recommended action to be taken by the operator based on the level of hemodilution.

29. A kit comprising two or more binding agents to carry out the method of any one of claims 1-12 or 17-28.

30. The kit of claim 29, wherein the two or more binding agents are for labeling CD 13 and a complementary marker includes one or more cell surface markers selected from the group consisting of CD1 lc, CD15, CD16, and HLA-DR.

31. A kit comprising two or more targeted mRNA capture reagents to carry out the method of any one of claims 13-28.

32. The kit of claim 31, wherein the two or more targeted mRNA capture reagents are for capture of mRNA molecules associated with expression of CD13 and a complementary marker includes one or more cell surface markers selected from the group consisting of CDllc, CD15, CD16, and HLA-DR.

33. A method for monitoring progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject, comprising: determining a quantity of cells in the bone marrow sample expressing a CD 13 protein and a complementary marker; correlating the quantity of cells expressing the CD 13 protein and a complementary marker to a level of hemodilution of the bone marrow sample; assessing whether the level of hemodilution passes a pre-set hemodilution criteria; and responsive to the level of hemodilution passing the pre-set hemodilution criteria, performing an assessment or analysis of the bone marrow sample.

34. The method of claim 33, wherein the complementary marker is selected from the group consisting of CD1 lc, CD15, CD16, and HLA-DR, and wherein at least one of: a decreased quantity of cells in the bone marrow sample that are CD13+ and CD1 lc- indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD 13+ and CD 15+ indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD16+ indicates increased hemodilution in the bone marrow sample, an increased quantity of cells in the bone marrow sample that are CD 13- and HLA-DR- indicates increased hemodilution in the bone marrow sample, or a decreased quantity of cells in the bone marrow sample that are CD13+ and HLA-DR- indicates increased hemodilution in the bone marrow sample.

35. A method for monitoring progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject, comprising: extracting targeted mRNA molecules associated with a CD 13 protein and the complementary marker from the bone marrow sample; sequencing the targeted mRNA molecules extracted from the bone marrow sample; determining an expression level of the CD 13 protein and the complementary marker in the bone marrow sample using sequence data obtained from the targeted mRNA molecules; assessing whether the expression level of the CD 13 protein and the complementary marker passes a pre-set complementary marker expression criteria; and responsive to the expression level of the CD 13 protein and the complementary marker passing the pre-set complementary marker expression criteria, performing an assessment or analysis of the bone marrow sample.

36. The method of claim 35, wherein the complementary marker is selected from the group consisting of CD1 lc, CD15, CD16, and HLA-DR, and wherein at least one of: a decreased quantity of cells in the bone marrow sample that are CD13+ and CD1 lc- indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD15+ indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD16+ indicates increased hemodilution in the bone marrow sample, an increased quantity of cells in the bone marrow sample that are CD 13- and HLA-DR- indicates increased hemodilution in the bone marrow sample, or a decreased quantity of cells in the bone marrow sample that are CD13+ and HLA-DR- indicates increased hemodilution in the bone marrow sample.

37. A method for monitoring effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing a bone marrow sample obtained from a subject being treated with the therapeutic regimen, comprising: determining a quantity of cells in the bone marrow sample expressing a CD 13 protein and a complementary marker; correlating the quantity of cells to a level of hemodilution of the bone marrow sample; assessing whether the level of hemodilution passes a pre-set hemodilution criteria; and responsive to the level of hemodilution passing the pre-set hemodilution criteria, performing an assessment or analysis of the bone marrow sample to at least one of monitor or verify a therapeutic effectiveness of the therapeutic regimen.

38. The method of claim 37, further including: administering a therapeutic to the subject prior to determining a quantity of cells expressing the CD 13 protein and a complementary marker in the bone marrow.

39. The method of claim 36 or claim 37, wherein the complementary marker is selected from the group consisting of CD1 lc, CD15, CD16, and HLA-DR, and wherein at least one of: a decreased quantity of cells in the bone marrow sample that are CD13+ and CD1 lc- indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD15+ indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD16+ indicates increased hemodilution in the bone marrow sample, an increased quantity of cells in the bone marrow sample that are CD 13- and HLA-DR- indicates increased hemodilution in the bone marrow sample, or a decreased quantity of cells in the bone marrow sample that are CD13+ and HLA-DR- indicates increased hemodilution in the bone marrow sample.

40. A method for monitoring effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing a bone marrow sample obtained from a subject being treated with the therapeutic regimen, comprising: extracting targeted mRNA molecules associated with a CD 13 protein and a complementary marker from the bone marrow sample; sequencing the targeted mRNA molecules extracted from the bone marrow sample; determining an expression level of the CD 13 protein and the complementary marker in the bone marrow sample using sequence data obtained from the targeted mRNA molecules; assessing whether the expression level of the CD 13 protein and the complementary marker passes a pre-set complementary marker expression criteria; and responsive to the expression level of the CD 13 protein and the complementary marker passing the pre-set complementary marker expression criteria, performing an assessment or analysis of the bone marrow sample to at least one of monitor or verify a therapeutic effectiveness of the therapeutic regimen.

41. The method of claim 40, further including: administering a therapeutic to the subject prior to extracting targeted mRNA molecules associated with the CD 13 protein and the complementary marker from the bone marrow sample.

42. The method of claim 40 or claim 41, wherein the complementary marker is selected from the group consisting of CD1 lc, CD15, CD16, and HLA-DR, and wherein at least one of: a decreased quantity of cells in the bone marrow sample that are CD13+ and CD1 lc- indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD15+ indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD16+ indicates increased hemodilution in the bone marrow sample, an increased quantity of cells in the bone marrow sample that are CD 13- and HLA-DR- indicates increased hemodilution in the bone marrow sample, or a decreased quantity of cells in the bone marrow sample that are CD13+ and HLA-DR- indicates increased hemodilution in the bone marrow sample.

43. A method for determining hemodilution of a bone marrow sample, comprising: analyzing the bone marrow sample to identify a cell distribution for cells in the bone marrow sample expressing one or more cell surface markers; calculating a statistical distancing score for the cell distribution; and correlating the statistical distancing score to a level of hemodilution of the bone marrow sample.

44. The method of claim 43, wherein the one or more cell surface markers includes CD71 and wherein an increased statistical distancing score for the cell distribution expressing CD71 indicates increased hemodilution in the bone marrow sample.

45. The method of claim 43, wherein the one or more cell surface markers includes CD33 and CD117 and wherein an increased statistical distancing score for the cell distribution expressing CD33 and CD 117 indicates increased hemodilution in the bone marrow sample.

46. The method of claim 43, wherein the one or more cell surface markers includes CD 19 and wherein an increased statistical distancing score for the cell distribution expressing CD 19 indicates increased hemodilution in the bone marrow sample.

47. The method of claim 43, wherein the one or more cell surface markers includes a CD56 protein and a CD 13 protein and wherein an increased statistical distancing score for the cell distribution expressing the CD56 protein and the CD13 protein indicates increased hemodilution in the bone marrow sample.

48. The method of claim 43, wherein the one or more cell surface markers includes CD33 and wherein an increased statistical distancing score for the cell distribution expressing CD33 indicates increased hemodilution in the bone marrow sample.

49. The method of any one of claims 43 to 48, wherein a linear correlation exists between the statistical distancing score for the cell distribution and hemodilution in the bone marrow sample.

50. The method of any one of claims 43 to 48, wherein the statistical distancing score is generated using a metric technique selected from a group consisting of Earth Mover’s Distance, frequency difference gating, probability binning, cytometric fingerprinting, and quadratic form.

51. The method of any one of claims 43 to 48, wherein the cell distribution is identified using a flow cytometer.

52. The method of claim 43, wherein a selected group of cell surface markers includes CD71, CD117, CD56, CD33, CD19, and CD13 and wherein the one or more cell surface markers includes either one or two cell surface markers from the selected group of cell surface markers.

53. The method of claim 43, wherein calculating the statistical distancing score for the cell distribution comprises: computing a distance between the cell distribution for the cells in the bone marrow sample expressing the one or more cell surface markers and a control cell distribution using Earth Mover’s Distance.

54. The method of claim 43, wherein the cells are selected from a group consisting of a cell subpopulation expressing CD71, a cell subpopulation expressing CD33 and CD 117, a cell subpopulation expressing CD19, a cell subpopulation expressing CD33, and a cell subpopulation expressing CD56 and CD13.

55. The method of claim 43, wherein correlating the statistical distancing score to the level of hemodilution comprises: determining a percent hemodilution of the bone marrow sample using the statistical distancing score.

56. The method of claim 55, further including: calling a low hemodilution state for the bone marrow sample in response to the percent hemodilution being less than about 25%.

57. The method of claim 55, further including: calling a low hemodilution state for the bone marrow sample in response to the percent hemodilution being less than about 50%.

58. The method of claim 55, further including: calling a low hemodilution state for the bone marrow sample in response to the percent hemodilution being less than about 75.

59. The method of claim 55, further including: calling a low hemodilution state for the bone marrow sample in response to the percent hemodilution being less than about 100%.

60. The method of claim 55, further including: adjusting one or more analytical measurements taken from the bone marrow sample proportionally to the percent hemodilution of the bone marrow sample.

61. The method of any one of claims 43-60, further including: adjusting one or more analytical measurements taken from the bone marrow sample proportionally to the statistical distancing score.

62. A kit comprising at least one binding agent to carry out the method of claim 43 or 50.

63. The kit of claim 62, wherein a binding agent of the at least one binding agent is for labeling CD71.

64. The kit of claim 62, wherein a binding agent of the at least one binding agent is for labeling CD33 and CD117.

65. The kit of claim 62, wherein a binding agent of the at least one binding agent is for labeling CD19.

66. The kit of claim 62, wherein a binding agent of the at least one binding agent is for labeling CD56 and CD 13.

67. The kit of claim 62, wherein a binding agent of the at least one binding agent is for labeling CD33.

68. A method for monitoring progression of a disease or disorder in a subject by analyzing a bone marrow sample obtained from the subject, comprising: calculating a statistical distancing score for a cell distribution of cells in the bone marrow sample expressing one or more cell surface markers; correlating the statistical distancing score to a level of hemodilution of the bone marrow sample; assessing whether the level of hemodilution passes a pre-set hemodilution criteria; and ascertaining a progression of the disease or disorder in the subject with the bone marrow sample if the level of hemodilution passes the pre-set hemodilution criteria.

69. The method of claim 68, wherein the cells are selected from a group consisting of a cell subpopulation expressing CD71, a cell subpopulation expressing CD33 and CD 117, a cell subpopulation expressing CD19, a cell subpopulation expressing CD33, and a cell subpopulation expressing CD56 and CD13, and wherein an increased statistical distancing score indicates increased hemodilution in the bone marrow sample.

70. The method of claim 68 or claim 69, wherein the statistical distancing score is generated using a metric technique selected from a group consisting of Earth Mover’s Distance, frequency difference gating, probability binning, cytometric fingerprinting, and quadratic form.

71. A method for monitoring effectiveness of a therapeutic regimen in treating a disease or disorder by analyzing a bone marrow sample obtained from a subject being treated with the therapeutic regimen, comprising: calculating a statistical distancing score for a cell distribution of cells in the bone marrow sample expressing one or more cell surface markers; correlating the statistical distancing score to a level of hemodilution of the bone marrow sample; assessing whether the level of hemodilution passes a pre-set hemodilution criteria; and verifying an improvement of the disease or disorder in the subject if the level of hemodilution passes the pre-set hemodilution criteria.

72. The method of claim 71, further including: administering a therapeutic to the subject prior to calculating the statistical distancing score.

73. The method of claim 71 or claim 72, wherein the cells are selected from a group consisting of a cell subpopulation expressing CD71, a cell subpopulation expressing CD33 and CD117, a cell subpopulation expressing CD19, a cell subpopulation expressing CD33, and a cell subpopulation expressing CD56 and CD13, and wherein an increased statistical distancing score indicates increased hemodilution in the bone marrow sample.

74. The method of any one of claims 71 to 73, wherein the statistical distancing score is generated using a metric technique selected from a group consisting of Earth Mover’s Distance, frequency difference gating, probability binning, cytometric fingerprinting, and quadratic form.

75. A method for determining hemodilution of a bone marrow sample, comprising: analyzing the bone marrow sample to identify a cell distribution for a cell subpopulation that expresses at least one protein of interest; calculating a statistical distancing score for the cell distribution with respect to a control cell distribution for the cell subpopulation in a control bone marrow sample with no hemodilution; and correlating the statistical distancing score to a level of hemodilution of the bone marrow sample.

76. The method of claim 75, wherein the cell subpopulation is selected from a group consisting of a first cell subpopulation expressing CD71, a second cell subpopulation expressing CD33 and CD117, a third cell subpopulation expressing CD19, a fourth cell subpopulation expressing CD33, and a fifth cell subpopulation expressing CD56 and CD13.

77. The method of claim 75 or claim 76, wherein the statistical distancing score is generated using a metric technique selected from a group consisting of Earth Mover’s Distance, frequency difference gating, probability binning, cytometric fingerprinting, and quadratic form.

78. The method of any one of claims 75 to 77, wherein an increased statistical distancing score indicates increased hemodilution in the bone marrow sample.

79. A non-transitory computer-readable medium storing computer instructions for determining hemodilution of a bone marrow sample, comprising: receiving, by one or more processors, data obtained from the bone marrow sample; analyzing, by the one or more processors, the data to determine a quantity of cells in the bone marrow sample expressing a CD13 protein and a complementary marker; and correlating, by the one or more processors, the quantity of cells expressing the CD13 protein and the complementary marker to a level of hemodilution of the bone marrow sample.

80. The non-transitory computer-readable medium of claim 79, wherein the complementary marker includes one or more selected from the group consisting of CD1 lc, CD15, CD16, and HLA-DR.

81. The non-transitory computer-readable medium of claim 79, wherein the complementary marker is selected from the group consisting of CDllc, CD15, CD16, and HLA-DR, and wherein at least one of: a decreased quantity of cells in the bone marrow sample that are CD13+ and CD1 lc- indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD15+ indicates increased hemodilution in the bone marrow sample, a decreased quantity of cells in the bone marrow sample that are CD13+ and CD16+ indicates increased hemodilution in the bone marrow sample, an increased quantity of cells in the bone marrow sample that are CD 13- and HLA-DR- indicates increased hemodilution in the bone marrow sample, or a decreased quantity of cells in the bone marrow sample that are CD13+ and HLA-DR- indicates increased hemodilution in the bone marrow sample.

82. A non-transitory computer-readable medium storing computer instructions for determining hemodilution of a bone marrow sample, comprising: receiving, by one or more processors, data obtained from the bone marrow sample; analyzing, by the one or more processors, the data to identify a cell distribution for cells in the bone marrow sample expressing one or more cell surface markers; calculating, by the one or more processors, a statistical distancing score for the cell distribution; and correlating, by the one or more processors, the statistical distancing score to a level of hemodilution of the bone marrow sample.

83. The non-transitory computer-readable medium of claim 82, wherein the cells are selected from a group consisting of a cell subpopulation expressing CD71, a cell subpopulation expressing CD33 and CD117, a cell subpopulation expressing CD19, a cell subpopulation expressing CD33, and a cell subpopulation expressing CD56 and CD13.

84. The non-transitory computer-readable medium of claim 82 or claim 83, wherein the statistical distancing score is generated using a metric technique selected from a group consisting of Earth Mover’s Distance, frequency difference gating, probability binning, cytometric fingerprinting, and quadratic form.