CN110806480B

CN110806480B - Tumor specific cell subset and characteristic gene and application thereof

Info

Publication number: CN110806480B
Application number: CN201911148439.8A
Authority: CN
Inventors: 赫捷; 陈应泰
Original assignee: Cancer Hospital and Institute of CAMS and PUMC
Current assignee: Cancer Hospital and Institute of CAMS and PUMC
Priority date: 2019-11-21
Filing date: 2019-11-21
Publication date: 2020-09-29
Anticipated expiration: 2039-11-21
Also published as: CN110806480A

Abstract

The invention discloses a tumor specific cell subset and a characteristic gene and application thereof; the invention utilizes the method of single cell transcriptome sequencing combined with bioinformatics analysis to separate and characterize the cell subset capable of reflecting the tumor, namely the tumor vascular endothelial cell highly expressing RALGPA 2, and further determines the correlation between RALGPA 2 and gastric cancer.

Description

Tumor specific cell subset and characteristic gene and application thereof

Technical Field

The invention belongs to the field of biological medicine, and relates to a tumor specific cell subset and a characteristic gene and application thereof.

Background

Malignant tumors are a serious disease threatening the life and health of human beings worldwide. The tumor is difficult to cure, the mortality rate is high, and the core reason is the relapse and the metastasis of the tumor. Tumor metastasis is a complex, multi-step process, and tumor stem cells, circulating tumor cells, micrometastases may play an important role in tumor metastasis and recurrence. Malignant tumors are accompanied by genome changes during their development, so genetic level analysis of tumors can further understand their molecular characteristics and mechanisms of development. However, these tumor cells or metastases playing an important role in the process of tumor metastasis generally consist of trace cells, even existing in the form of single cells, and extensive heterogeneity among tumor cells is proved by more and more researches, while traditional biological and medical research means are often based on analysis of a large number of cells, and the genomic characteristics and molecular mechanisms in the process of tumor metastasis recurrence are not clear.

With the development of biological research, Sequencing technologies have experienced rapid development from low-throughput to high-throughput over the last decades, and Next Generation Sequencing (NGS) technologies have enabled thousands of trillions of base sequence data to be obtained quickly and efficiently, greatly changing the field of basic biological and medical research. Although conventional multi-cell transcriptome sequencing techniques provide researchers with a preliminary understanding of the biological characteristics of cancer, the gene expression profiling data obtained therefrom is an average of the gene expression levels of various cells in a tumor tissue sample, thereby ignoring tumor tissue heterogeneity and masking genetic information of some cell subtypes. Studies targeting cell populations are somewhat limiting and do not explain the problems that arise in scientific research. The development of the single cell technology solves the challenge of researching a trace sample on one hand, can better understand the heterogeneity among cells on the other hand, and provides powerful technical support for analyzing the characteristics, the behavior mechanism and the like of a single cell. In recent years, the related analysis technique of single cells has gradually become a hot spot of research.

The single cell sequencing technology is applied to realize the accurate identification of tumor stem cells, tumor micrometastasis and peripheral blood circulation tumor cells of cancer patients and the quantitative acquisition of the information of the genome and the like. Through bioinformatics analysis, key biological mechanisms of cancer occurrence, recurrence and metastasis and drug resistance are deeply revealed.

The application adopts a single cell sequencing technology, searches the cell types of each cell in the gastric cancer by using a data dimension reduction method on the basis of a large amount of single cell transcriptome data, finds cell groups and genes related to the cancer, and then analyzes the related genes by combining the multi-cell transcriptome data in TCGA to predict the effect of the gastric cancer.

Disclosure of Invention

The present inventors have completed the present invention based on the fact that the present inventors isolated and characterized cell subsets that reflect body tumors by analyzing single-cell gene expression profiles in cancer tissues and paracancerous tissues using single-cell transcriptome sequencing technology and bioinformatics analysis technology, and further studied and determined novel characteristic genes expressed by the cell subsets and the correlations between the characteristic genes and tumors.

Accordingly, the present invention is directed in a broad sense to markers, methods, compounds, compositions and articles of manufacture that can be used to identify or characterize, and optionally to isolate, partition, separate or enrich, a subpopulation of tumor cells.

The markers disclosed herein, which enable the identification or characterization of specific cell subsets from tumors, constitute a universal characterization of such tumor cells, and can be used for the elucidation of therapeutic targets. Moreover, it can be further used in clinical and non-clinical applications for diagnosis, monitoring, management and targeted therapy of tumor patients, and to provide related kits or other products.

A first aspect of the invention provides a biomarker for diagnosing tumour specific vascular endothelial cells, said biomarker being ralga 2.

Further, the biomarkers are highly expressed in tumor-specific vascular endothelial cells.

Further, the tumor is gastric cancer, especially gastric adenocarcinoma.

In a second aspect, the invention provides the use of a biomarker according to the first aspect of the invention for sorting tumour specific vascular endothelial cells.

The third aspect of the invention provides the use of an agent for detecting RALGAPA2 in the manufacture of a product for the diagnosis of gastric cancer.

Further, the reagent includes nucleic acids, ligands, enzymes, substrates, antibodies.

In a fourth aspect of the present invention, there is provided a kit for diagnosing gastric cancer, which comprises a binding agent capable of binding to the gene RALGAPA2 or a protein expressed therefrom.

Further, the binding agent binds to the gene ralga 2 or a protein expressed thereof in tumor-specific vascular endothelial cells.

Further, the binding agent includes nucleic acids, ligands, enzymes, substrates, antibodies.

As an alternative embodiment, the kit comprises a container or containers comprising one or more of the binding agents. More preferably, the kit further comprises instructional materials, such as instructions, for using the kit. In a preferred embodiment, the description describes the upregulation of the expression of RALGAPA2 in gastric cancer patients. In a more preferred embodiment, the specification describes that RALGAPA2 is highly expressed in gastric cancer-specific vascular endothelial cells.

A fifth aspect of the invention provides the use of ralga 2 in the manufacture of a medicament for the treatment of a tumour.

Further, the medicament comprises an inhibitor of ralga 2.

Further, the inhibitor inhibits the expression of ralga 2 in tumor-specific vascular endothelial cells.

By way of non-limiting example, an inhibitor is any substance that reduces the activity of ralga 2 protein, reduces the stability of ralga 2 gene or protein, down-regulates the expression of ralga 2 protein, reduces the duration of effective action of ralga 2 protein, or inhibits transcription and translation of ralga 2 gene, which can be used in the present invention as a substance useful for down-regulating ralga 2, and thus can be used for preventing or treating tumors. For example, the inhibitor includes nucleic acid inhibitors, protein inhibitors, proteolytic enzymes, protein binding molecules.

Further, the tumor is gastric cancer, especially gastric adenocarcinoma.

In a sixth aspect, the present invention provides a method for screening tumor cell marker molecules, comprising the steps of:

1) extracting cells from the tumor tissue;

2) separating single cells and performing lysis;

3) carrying out reverse transcription on mRNA of a sample to obtain cDNA;

4) constructing a sequencing library;

5) performing single cell sequencing;

6) and (4) performing bioinformatics analysis on the sequencing result, and searching for tumor cell marker molecules.

Further, the step of bioinformatics analysis includes:

1) sequencing data quality control and expression quantity quantification;

2) cell clustering analysis;

3) identifying the marker gene;

4) analyzing the genome variation;

5) identifying the tumor cells;

6) identifying differentially expressed genes;

7) and (4) analyzing the track of the cells.

Further, the conditions for cell quality control were:

removing cells identified with a gene number >3000, or < 200;

removing cells with a total number of UMIs > 10000;

the cells with the UMI-removed mitochondrial gene expression of more than 10 percent are obtained.

Further, the conditions for screening the marker gene were:

FC >1.5 times;

expression is detectable in cells in > 15% of a subpopulation of cells of interest;

the average expression level is above 0.1 or the average expression level is 10 times lower than the target subpopulation in other cell subpopulations of all cell types > 50%.

The invention has the advantages and beneficial effects that:

the invention utilizes the single cell transcriptome analysis technology, and discovers a new cell gene capable of reflecting the tumor specificity of an organism by analyzing the single cell gene expression profiles of cancer tissues and cancer adjacent tissue cells.

The invention discovers 3 tumor-specific vascular endothelial cells for the first time, and in the vascular endothelial cells, RALGAPA2 shows high expression, which means that RALGPA 2 gene can become functional molecules of tumor and tumor vascular endothelial cells, and can be used for immunotherapy of tumor by inhibiting the expression of the gene or the activity of the expression protein thereof.

Description of the terms

The term "enriched" can be broadly construed as a treated cell population that contains a higher percentage of a selected cell type than in an untreated, otherwise equivalent cell population or sample. In some preferred embodiments, enriching a cell population refers to increasing the percentage of one cell type in the cell population by about 50% or greater than 50% as compared to the starting cell population. In other preferred embodiments, the enriched cell population of the invention will comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95%, 98% or 99% of the selected cell type.

The terms "marker", "marker" or "cellular marker" are synonymous and refer to any trait or characteristic in the form of a chemical or biological entity. The label may be morphological, functional or biochemical in nature. In preferred embodiments, the markers are differentially or preferentially expressed by particular cell types, or by cells under certain conditions (e.g., cytokines or surface antigens or membrane proteins or cytoplasmic proteins expressed at particular points in the cell cycle or under particular extracellular matrices, etc. more particularly, in the present invention, are those markers that indicate cells or cell subsets by virtue of their expression levels.

The terms "binding agent", "binding molecule" and "binding entity" are synonymous and may be used interchangeably. In the context of the present invention, the binding agent binds to, recognizes, interacts with, or otherwise associates with a selectable marker on the subpopulation of cells. Exemplary binding agents may include, but are not limited to, antibodies or fragments thereof, antigens, aptamers, nucleic acids (e.g., DNA and RNA), proteins (e.g., receptors, enzymes, enzyme inhibitors, enzyme substrates, ligands), peptides, lectins, fatty acids or lipids, and polysaccharides. For example, in some embodiments of the invention, the binding agent comprises an antibody or fragment thereof, a nucleic acid (e.g., DNA and RNA). As alternative embodiments, the nucleic acid includes, but is not limited to, primers, probes.

The term "antibody" is used in the broadest sense and specifically covers synthetic antibodies, monoclonal antibodies, polyclonal antibodies, recombinant antibodies, intrabodies, multispecific antibodies, bispecific antibodies, monovalent antibodies, multivalent antibodies, human antibodies, humanized antibodies, chimeric antibodies, primatized antibodies, Fab fragments, F (ab') fragments, single chain fvfc (scfvffc), single chain fv (scfv), anti-idiotypic (anti-Id) antibodies, and any other immunologically active antibody fragments, so long as they exhibit the desired biological activity (i.e., label-related or binding). In a broader sense, the antibodies of the invention include immunoglobulin molecules and immunologically active fragments of immunoglobulin molecules (i.e., molecules that contain an antigen binding site), where these fragments may or may not be fused to another immunoglobulin domain, including but not limited to an Fc region or fragment thereof. Furthermore, as outlined herein in more detail, the term antibody and antibodies specifically includes Fc variants or fragments thereof, including full length antibodies and variant Fc-fusions comprising an Fc region, optionally comprising at least one amino acid residue modification and fused to an immunologically active fragment of an immunoglobulin.

By "fragment" of a molecule is meant any contiguous polypeptide or subset of nucleotides of the molecule. For example, a fragment of a transmembrane protein may comprise a construct that comprises only the extracellular domain or some portion thereof. For purposes of the present invention, a marker fragment or derivative may include any immunoreactive or immunologically active portion of a selectable marker.

The term "inhibit" refers to a decrease in the activity of a protein or cell as compared to the absence of an inhibitor. In some embodiments, the term "inhibit" refers to a decrease in activity of at least about 25%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, or at least about 95%. In other embodiments, inhibition refers to a decrease in activity of about 25% to about 50%, about 50% to about 75%, or about 75% to 100%. In some embodiments, inhibition refers to a decrease in activity of about 95% to 100%, e.g., a decrease in activity of 95%, 96%, 97%, 98%, 99%, or 100%. Such a reduction can be measured using a variety of techniques known to those skilled in the art.

The terms "expression" and "gene expression" are synonymous and mean that a cell converts genetic information stored in a DNA sequence during its life through transcription and translation into a biologically active protein molecule.

The terms "increased expression" and "high expression" are synonymous and refer to an increased copy number of a gene transcript, and/or increased translation, as compared to normal levels.

In the present invention, the term "primer" means 7 to 50 nucleic acid sequences capable of forming a base pair (basepair) complementary to a template strand and serving as a starting point for replication of the template strand. The primers are generally synthesized, but naturally occurring nucleic acids may also be used. The sequence of the primer does not necessarily need to be completely identical to the sequence of the template, and may be sufficiently complementary to hybridize with the template. Additional features that do not alter the basic properties of the primer may be incorporated. Examples of additional features that may be incorporated include, but are not limited to, methylation, capping, substitution of more than one nucleic acid with a homolog, and modification between nucleic acids.

The term "probe" refers to a nucleic acid fragment such as RNA or DNA as short as a few to as long as several hundred bases that can establish specific binding to mRNA and can determine the presence of a particular mRNA by the Labeling action. The probe can be prepared in the form of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, an RNA probe, or the like. In the present invention, gastric cancer can be predicted by hybridization using the labeled polynucleotide of the present invention and a complementary probe, and by the presence or absence of hybridization. The appropriate choice of probes and hybridization conditions can be modified based on what is known in the art.

Drawings

FIG. 1 is a graph showing the expression level of RALGAPA2 gene in each cell subset of gastric cancer.

FIG. 2 is a ROC plot of gene prediction diagnostic accuracy.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention. The experimental procedures, in which specific conditions are not specified in the examples, are generally carried out under conventional conditions or conditions recommended by the manufacturers.

Example 1

1. Clinical specimen collection

Collecting 10 cases of gastric cancer tissues and corresponding paracancerous tissues, wherein the cancer tissues are depleted of necrotic tissue; the paracancerous tissue is normal tissue at least 5cm away from the cancerous tissue.

2. Single cell suspension preparation

Grinding to obtain single cells of cancer tissue and paracancer normal tissue. HeadFirstly, cutting the isolated tissue of the operation into 1mm³The size pieces were soaked in RPMI-1640 medium and 10% calf serum was added. Tissues were rapidly ground using a copper mesh, tissue debris was removed by 40 μm sieving, and single cell suspensions were collected by centrifugation at 400g for 10 min. The erythrocytes mixed in the tissue were further removed using an erythrocyte lysate. The cells were also washed twice with 10ml PBS and finally lysed in 0.5ml PBS and 1% calf serum was added.

3. Single cell transcriptome sequencing

The prepared single cell suspension was combined with a mixture of gel beads containing barcode information and enzymes, and then droplet wrapped In a microfluidic "double cross" to form GEMs (GelBead-In-EMulsions) containing gel beads (with preformed 10x primers In the gel beads), single cells, and Master Mix. Then, cell lysis and reverse transcription reactions were performed within the GEMs. In effective GEMs, 10X Barcode is connected with cDNA products, then the GEMs are crushed and broken into oil drops, PCR amplification is carried out by taking the cDNA as a template, and quality inspection (the size of an amplified fragment and the yield of the amplified product) is carried out on the amplified products after the cDNA amplification is finished.

And after the quality of the amplification product is qualified, constructing a sequencing library. Firstly, breaking cDNA into fragments of about 200-300bp by a chemical method, carrying out cDNA fragmentation, end repair and A addition, carrying out cDNA fragment screening, connecting a P7 adapter linker, introducing sample Index through PCR amplification, and finally carrying out fragment screening to obtain a cDNA library.

And performing library inspection after the library is finished, sequencing by using an Illumina sequencing platform after the library inspection is qualified, obtaining sequencing data, and performing subsequent data analysis.

4. Bioinformatics analysis

1) Single cell RNA-seq data processing.

The original gene expression level of each sample was counted using the software cellrange (v2.0.2) based on a ginseng reference genome (GRCh38, supplied by cellrange). Subsequently, the single cells were filtered using the following conditions: 1) removing cells with a number of identified genes >3000 or < 200; 2) removing cells with the total number of UMI > 10000; 3) cells with mitochondrial gene expression in the UMI of > 10% were deleted. The genes were filtered using the following conditions: genes expressed in less than 3 cells were removed. The library size normalization of the cells remaining after the above filtration was performed using the NormalizeData function of seruat to obtain the normalized expression amount. Variable expressed genes were screened using the findVariablegenes function (default parameters) from Seurat and linearly transformed using the ScaleData function from Seurat (the 'scaling' method).

2) Cell clustering

Before cell clustering, Canonical Correlation Analysis (CCA) was performed on all sample linearly transformed gene expression matrices using the runmultica function of semuat, resulting in canonical correlation vectors, which were stored in a single semuat object. And comparing the CCAs data by using an align subspace function to obtain dimension-reduced data for subsequent cell clustering. Cell clustering analysis was performed on the top 30 dimensional data using findsolusters and RunTSNE functions. For the cell clustering results, cells were annotated to known biological cell types using typical marker genes. To identify subpopulations of 6 non-epithelial major cell types, cells belonging to each cell type were extracted from each sample and dropout was estimated using scImpute (v0.0.8) and the data was similarly processed using the steps described above.

3) Identification of marker genes

To identify marker genes for different cell subsets, the Findmarkers function of sourtat was used to compare cells of different subsets with all other cells of the subset and cells of all other cell types, respectively. The conditions for screening the marker gene were: FC >1.5 times; expression is detectable in cells in > 15% of the subpopulation of cells of interest; the average expression level is above 0.1 or the average expression level is 10 times lower than the target subpopulation in other cell subpopulations of all cell types > 50%.

4) Genomic variation analysis (GSVA).

Pathway analysis was performed on 50 hallmark pathways described in the molecular markers database (V7.0), and GSVA scores were obtained for 50 hallmark pathways per cell using the GSVA package (V1.26.0). The scores for each cell were compared using a generalized linear model to assess the differences in pathways between subpopulations.

5) Tumor cells were identified.

To identify tumor cells from normal epithelial cells, we calculated a CNV score for each cell using inferCNV (v0.3) (cells from normal tissue of the patient as a reference baseline). The CNV scores were calculated over a moving window of 101bps, with scores ranging from-1 to 1, and scores between-0.2 and 0.2 were all set to 0. The region where expression levels are significantly higher or lower than normal cells may be the presence of an amplification or deletion on the chromosome, and to confirm this, the body cells CNV were examined for WES data and relevant images were drawn.

6) Differentially expressed genes were identified.

Using FindMarkers function identification in source for differentially expressed genes between subpopulations, screening criteria: FC > 2 and Benjamini-Hochberg corrected p-value < 0.05.

7) Trajectory analysis

Trajectory analysis was performed using Monocle2(v 2.6.4). Genes with average expression level more than or equal to 0.1 are used for cell sequencing, and then default methods and parameters are used for carrying out dimensionality reduction and trajectory construction on the selected genes.

8) Cell proportion analysis

The proportion of cell subsets in the tissue was assessed using scBio (v0.1.2), and the relative abundance of each cell type was inferred from a large number of gene expression profiling data using default parameters.

5. Results

Cell clustering found 43 cell subsets in total, which belong to cell types such as vascular endothelial cells, CD4T cells, CD8T cells, B cells, natural killer cells, monocytes, dendritic cells, macrophages, fibroblasts, epithelial cells and cancer cells, wherein 3 cell subsets (C3-ENDO-CTHRC1, C4-ENDO-ESM1, C5-ENDO-IL6) are cell subsets specific to gastric cancer tissues.

The gene RALGAPA2 is highly expressed in vascular endothelial cell subsets which are specific to two gastric cancer tissues, and the expression level is very low in normal control tissues. Ralga 2 was hardly expressed in CD4T cells, CD8T cells, B cells, natural killer cells, monocytes, dendritic cells, macrophages, fibroblasts, epithelial cells and cancer cells, and had very high cell type specificity (fig. 1).

Example 2 prediction of Gene diagnostic accuracy

Data of 375 carcinoma tissues and 32 paracancerous tissues of gastric carcinoma were downloaded from the TCGA database, and AUC values of genes were calculated using the master function ROC in the pROC package in R3.6.1 software, and the calculation results were visualized using the plot.

The data statistics result is shown in fig. 2, the AUC value of ralga 2 is 0.835, which indicates that ralga 2 has higher accuracy in the diagnosis of gastric cancer, and ralga 2 can be used as a molecular marker in the diagnosis of gastric cancer.

Based on the high expression of ralga 2 in gastric cancer tissues and in the vascular endothelial cells characteristic of gastric cancer, inhibitors targeted to ralga 2 can be designed, more preferably, ralga 2, which is specific to vascular endothelial cells, for the treatment of gastric cancer.

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

Claims

1. Use of a biomarker for sorting gastric cancer specific vascular endothelial cells for non-diagnostic purposes, wherein the biomarker is ralga 2.

2. The use according to claim 1, wherein the biomarker is highly expressed in gastric cancer-specific vascular endothelial cells.

3. Application of a reagent for detecting RALGAPA2 in preparation of products for diagnosing gastric cancer.

4. The use of claim 3, wherein the agent comprises a nucleic acid, a ligand, an enzyme, a substrate, or an antibody.