Method for screening key functional proteins of different cells of bovine testis and application

Abstract

The invention discloses a method for screening key functional proteins of different bovine testis cells and application, the method is based on a single cell RNA sequencing technology, and five different germ cell types and eight somatic cell types in the bovine testis cells are distinguished in detail; the invention also provides a gene marker, namely a key functional protein gene, special for testicular cell types, the cell types can be quickly and accurately identified according to the expression conditions of the proteins, and in addition, the proteins can also help people develop targeted intervention measures to improve the fertility of livestock.

Description

Technical Field

[0001] The invention relates to the technical field of molecular biology and genetic breeding, and in particular to a method for screening key functional proteins of different cells in bovine testicles and its application. Background Art

[0002] Breeding bulls have a genetic influence of more than 75% on the dairy cattle population, highlighting the critical role of bull semen in animal reproduction. The quality of bull semen significantly affects the reproductive efficiency and genetic enhancement of livestock. The bovine testis is a key reproductive organ that plays a vital role in the sperm production process. Spermatogenesis is a complex process that requires coordination between different types of testicular cells, which can be divided into three major phases: proliferation, meiosis, and differentiation. Certain spermatogonial stem cells (SSCs) undergo mitosis to maintain the population in the proliferation phase, while others enter the meiotic phase, where spermatocytes undergo two rounds of cell division. During differentiation, spermatids produced during meiosis undergo a complex series of changes to mature into functional spermatids, involving the formation of the acrosome, the development of the flagellum, and the elimination of excess cytoplasm. However, despite the importance of this aspect, our understanding of bull testicular spermatogenesis, the cellular composition and underlying biology of the bovine testis, remains limited.

[0003] While traditional batch analysis methods provide valuable guidance, they often mask the inherent cellular heterogeneity in tissues such as the testis, limiting the ability to capture the subtle dynamics of individual cell types. How to quickly classify and identify different cells in bovine testis, and how to discover key functional proteins in different testicular cells for in-depth research are still in their early stages, making it impossible to intervene in bull gene enhancement and affect the improvement of bull fertility and reproductive capacity. Summary of the invention

[0004] The purpose of the present invention is to provide a method for screening key functional proteins in different cells of bovine testis, which can effectively obtain key functional proteins in different types of cells of bovine testis, and then be used for identifying cell types, intervening in genetic breeding such as intervening in bull gene enhancement, improving bull reproductive capacity, etc.

[0005] The technical solution of the present invention is described in detail as follows: In a first aspect, the present invention provides a method for screening key functional proteins of different cells in bovine testis, comprising the following steps: (1) Isolate bovine testicular cells and generate single-cell libraries using the 10×Genomics platform. Perform single-cell RNA sequencing after standard quality control to obtain sequencing raw data files; (2) The raw data files were deduplicated using the cell Ranger mkfastq program to generate FASTQ files. The FASTQ files were aligned, filtered, barcode counted, and UMI counted on the bovine UMD3.1 genome, and processed using the count tool of the cellRanger software to obtain output files. (3) The output file was imported into the Seurat package to standardize the gene expression values ​​in different samples. The top 1000 genes with high differential changes were determined by the canonical correlation analysis (CCA) method to reduce the data dimension and obtain a merged data set. The merged dataset was subjected to clustering and UMAP (Uniform Manifold Approximation and Projection) analysis. The first 1 to 12 canonical components (CCs) were used to analyze the weighted shared nearest neighbor (SNN) graph-based clustering method. The FindAllMarkers function of the Seurat package was used with default parameters to find differentially expressed genes in different cell clusters. Genes with an expression level exceeding 20% ​​and a logarithmic change greater than 0.25 were identified and screened to obtain different cell clusters and marker genes for each cell cluster. The marker genes for each cluster were determined using the Wilcoxon test. Marker genes express key functional proteins of the cell clusters in which they are located.

[0006] The 10×Genomics platform is a comprehensive single-cell isolation and library construction platform. The high-throughput single-cell RNA-seq technology of the 10×Genomics platform can fully connect to Illumina's sequencer to obtain the original sequencing data file. The original data is in BCL format (Base Call Library). This format of data needs to be processed by the cellranger mkfastq tool on the sequencer program to be converted into a FASTQ format file for subsequent bioinformatics analysis. Seurat is an R package for quality control, analysis, and exploration of single-cell RNA-seq data. It was developed by the Satija Lab of the New York Genome Center. Its main functions include quality control, cell screening, cell type identification, feature gene selection, differential expression analysis, data visualization, etc. UMAP is a nonlinear dimensionality reduction algorithm based on topology and manifold learning. Canonical components (CCs) are low-dimensional embedding coordinates calculated by the UMAP algorithm.

[0007] Optionally or preferably, in the above method, the standard quality control conditions in step (1) are: all genes are expressed in more than 3 cells, the number of genes detected in each cell is between 200 and 4000, and the percentage of mitochondrial gene expression in the cell is less than 5% to remove cell contamination caused by dead cells.

[0008] In a second aspect, the present invention provides application of any of the above methods in bull breeding for bull gene enhancement.

[0009] In a third aspect, the present invention provides the use of a key functional protein obtained by any of the above methods in identifying different types of cells in bovine testicles.

[0010] In a fourth aspect, the present invention provides a method for identifying different types of bovine testicular cells, wherein the cells are identified by detecting the expression levels of different genes in the cells, wherein the expression levels of one or more genes in the following different types of cells are higher than those in other types of cells: Spermatogonia: TKTL1 , FMR1NB , ESX1 , WDHD1 , RBBP8 ; Support cells: DEFB119 , INHA , OXT , AQP8 , WFDC15B , AARD ; Interstitial cells: GSN , APOD , OGN , COL1A2 , APOE , COL1A1 , GSTM4 , LAMA1 , C4A ; Fibroblasts: BMX .

[0011] Optionally or preferably, in the above method, one or more genes corresponding to different types of cells can also be combined with any of the following genes to identify cell types: Spermatogonia: DAZL , DMRT1 , UCHL1 , SYCP3 , STRA8 ; Support cells: FATE1 , CITED1 ; Interstitial cells: IGF1 , DCN , SFRP2 ; Fibroblasts: CXCL12 .

[0012] Compared with the prior art, the present invention has the following beneficial effects: Based on genomic single-cell RNA sequencing (scRNA-seq) technology, this invention provides a simple and effective method for screening key functional proteins in bovine testicular cells. A total of 60 key functional proteins were screened in bovine testicular cells, belonging to five different germ cells and eight somatic cells. These proteins help us to clarify key regulatory elements and signaling pathways by depicting the unique characteristics and gene expression patterns of each cell type, thereby gaining a deeper understanding of the complex cellular processes behind bovine testicular function. The expression of these proteins can also quickly and accurately identify cell types. In addition, these proteins can also help us develop targeted interventions to improve the fertility of livestock. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The figure shows the results of cell clustering analysis of bovine testis single-cell RNA sequencing, where A is the experimental workflow diagram for sample collection and analysis; B is the HE staining result of bovine testis tissue; C is the UMAP diagram of cell clustering analysis of bovine testis single-cell transcriptome data, where each point represents a cell and is colored according to the cell cluster. 13 cell clusters can be seen in the UMAP diagram; D shows that the colored cells in each cell cluster indicate the origin of the testicular tissue.

[0014] Figure 2Results of cell types and specific marker genes identified in clustering and UMAP analysis of merged data sets in single-cell RNA sequencing, where A is the analysis result of the main testicular cell types corresponding to the bovine testicular cell clusters obtained by UMAP analysis; B is a heat map showing the relative expression of representative cell-specific marker genes in the 13 major cell types in the data set, and the color bar indicates the relative expression level of the selected gene; C is an expression pattern diagram of the selected markers used to assign cell types projected on the UMAP map, showing one cell marker gene for each cell type, and red (or gray) indicates high (or low) expression levels; D is a statistical comparison of the distribution of cell attributes of each type in the 13 cell types, where nGene represents the number of genes detected per cell, nUMI represents the number of unique molecular identifiers (UMIs) per cell, and nCell represents the number of cells in each cell type.

[0015] Figure 3 This is a statistical chart of gene expression results of different types of testicular cells in the cell clustering analysis results of single-cell RNA sequencing of bovine testis, showing significant differences in gene expression.

[0016] Figure 4 The statistical results of the enriched GO terms for each cell type in the cell clustering analysis results of bovine testis single-cell RNA sequencing are shown. The top five upregulated GO terms in the results are given based on the differentially expressed genes corresponding to the cell types.

[0017] Figure 5 The figure shows the developmental trajectory analysis results of bovine testicular germ cells, where A is the pseudo-time trajectory sequence of bovine testicular germ cells, each point represents a single cell, and the color bar from dark to light indicates the beginning and end of the trajectory; B is the continuous development trajectory of testicular germ cells, and the cells are colored according to their predicted positions along the pseudo-time trajectory, showing three discrete cell states (pre-branching, cell fate 1, cell fate 2) during germ cell development; C is the expression profile of selected genes involved in the cell state along the pseudo-time trajectory, and the black line indicates the expression trend of germ cells.

[0018] Figure 6 The heat map (left) and GO term enrichment analysis results (right) of significantly differentially expressed genes in the developmental trajectory analysis of bovine testicular germ cells. The heat map analysis shows that DEGs are significantly enriched in three gene clusters, and the GO terms associated with each gene cluster are shown on the corresponding right. The heat map gene order is from top to bottom Figure 7 The statistical results of two gene expressions in different cell types are shown in the figure below. The left one is in germ cells. DDX4 Gene expression statistics, on the right are in somatic cells VIM Expression statistics results.

[0019] Figure 8 This is a statistical diagram of the expression of marker genes in different types of cells. DETAILED DESCRIPTION

[0020] In order to make the technical personnel in this field better understand the present application scheme, the present application will be clearly and completely described below in conjunction with the embodiments and drawings. Obviously, the described embodiments are only embodiments of a part of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work should belong to the scope of protection of this application. The instruments and reagents used in the embodiments are all derived from commercial channels unless otherwise specified.

[0021] Example 1 Screening and verification of new key functional proteins in different cells of bovine testis 1. Animal samples and ethical statement All experimental procedures involving animals were ethically approved by the Institutional Animal Care and Use Committee of the Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences (IACC20060101). Five adult testicles of Chinese Holstein cows were obtained from a bull station in Jinan, Shandong Province, China. Bovine testicular tissues were rapidly removed and transported to the laboratory on ice within two hours with Hank's balanced salt solution (HBSS). Each testis was immediately cut into small pieces and stored frozen in liquid nitrogen. A portion of the testicular tissue was fixed in Bouin's solution and 4% paraformaldehyde for future analysis, while the remaining tissue was used for cell isolation.

[0022] 2. Cell isolation and single-cell RNA sequencing Single-cell suspensions of testicular tissue were prepared using a two-step enzymatic digestion method: After washing the testicular tissue with 1× phosphate-buffered saline (PBS) containing penicillin / streptomycin and calcium-free and magnesium-free, the tissue was placed in DMEM / F12 medium (Gibco, A4192001) containing 1.5 mg / mL collagenase I (Sigma, C5138-100MG) and 0.01 mg / mL DNase I (Sangon Biotech, B300065-0001) at 25°C for 10 minutes with gentle agitation to dissociate bovine testicular seminiferous tubules. Subsequently, the cell suspension was centrifuged at 500 g / min for 5 minutes, and the supernatant was treated with 0.25% trypsin-EDTA (Gibco, 25300054) in a 37°C water bath for 5 minutes with intermittent shaking. The reaction was terminated by adding DMEM / F12 containing 10% FBS. Single testicular cells were obtained by filtration through a 40 μm filter, followed by centrifugation at 500 g / min for 10 min and washing with 1× PBS. The resulting cells were resuspended in 1× PBS containing 0.04% bovine serum albumin (BSA) for the next step. Cell viability confirmed by trypan blue staining was required to be higher than 80%, and cell counts were determined using a hemocytometer.

[0023] According to the manufacturer's instructions, the single-cell suspension was loaded into each channel of the 10×Gneomics platform, and more than 5,000 single cells were captured. According to the standard protocol, single-cell barcoding (a technology that uses DNA fragments within the genome to identify individual cells), cDNA amplification, and library construction were performed using Chromium Single Cell 3'Reagent Kits (V3) (10×Gneomics, PN-1000092). Subsequently, the HiSeq 2500 high-throughput sequencing platform of Illumina was used to sequence by running paired-end sequencing to construct a single-cell library.

[0024] 3. Single-cell RNA sequencing and cell clustering analysis The single-cell data obtained by docking the 10×Genomics platform with the Illumina sequencing platform were processed by a set of analysis pipelines in the data analysis software cell Ranger (v7.0) (a product of 10×Genomics). The raw base call files of the Illumina sequencing platform were deduplicated using cellrangermkfastq to generate FASTQ files. These FASTQ files were aligned to the bovine UMD3.1 genome, filtered using cellranger counts, barcode counts, and UMI (Unique Molecular Indentifier) ​​counts. The output files of cellranger were then imported into Seurat (v4.0) for dimensionality reduction, clustering, and scRNA-seq data analysis.

[0025] A total of 26,889 single cells from five bull testes passed the quality control threshold. This was determined by ensuring that all genes were expressed in more than three cells, the number of genes detected per cell was between 200 and 4,000, and the percentage of mitochondrial gene expression was less than 5%. For data preprocessing, Seurat was also used to normalize the expression values ​​of different samples. Canonical Correlation Analysis (CCA) was performed to identify the top 1,000 highly differentially changed genes and reduce the dimensionality of the data to obtain a merged dataset. Subsequently, the merged dataset was clustered and UMAP analyzed using the top 1-12 canonical components (CCs) and a clustering method based on weighted shared nearest neighbor (SNN) graphs. Marker genes for each cluster were determined using the Wilcoxon rank-sum test and default parameters were applied by the FindAllMarkers function in Seurat. This selection process targeted marker genes expressed in more than 20% of the cell clusters with a log fold change greater than 0.25.

[0026] Results: Testicular tissues from five Holstein bulls aged 2, 4, 4, 5, and 5 years old (named A1, D1, E1, F1, and G1, respectively) were used for scRNA-seq. Testicular cells were isolated using a two-step enzymatic digestion method, resulting in a survival rate of more than 80% for each sample. Subsequently, the isolated single cells were used to generate single-cell libraries using the 10×Genomics platform. The process is as follows: Figure 1As shown in A. A total of 40,975 cells were captured, of which 26,889 cells met the standard quality control (QC) criteria for further analysis. These libraries generated 3,117 Mb of sequencing fragments (reads) with an average sequencing saturation rate of 56.94%. Each cell showed an average of 75.49 K sequencing fragments, and an average of approximately 1,612 genes were identified (see Table 1 below). In addition, the tissue morphology and some cell types of bovine testicular seminiferous tubules were detected by hematoxylin-eosin (H&E) staining, as shown in Figure 1. Figure 1 As shown in B.

[0027] Table 1 Sequencing analysis results of single-cell libraries obtained from different bull testicular cells The vst dimensionality reduction method in principal component analysis (PCA) was used to identify genes with high differential changes. Subsequently, the top principal component and graph-based methods were used to implement the UMAP (Uniform Manifold Approximation and Projection) algorithm (for specific methods, please refer to the reference Macosko EZ, Basu A, Satija R, et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using NanoliterDroplets. [J]. Cell, 2015, 161(5): 1202), thereby visualizing 13 different cell clusters in two-dimensional space through UMAP, such as Figure 1 C. Importantly, all identified cell clusters were consistently present in each testis sample, as shown in Figure 1 As shown in D.

[0028] Using unsupervised clustering, a total of 13 discrete cell clusters were identified in the dataset, consisting of 5 germ cells and 8 somatic cells in bovine testes, such as Figure 2 Center A shows the results of UMAP analysis of the main testicular cell types corresponding to bovine testicular cell clusters. DDX4 and VIM The markers accurately distinguish germ cells from somatic cells. Please refer to Figure 7 , detected in 5 germ cells DDX4 High expression, detected in 8 somatic cells VIM High expression, thus verifying the accuracy of our clustering results ( DDX4 It is a marker gene for spermatogonia, spermatocytes and round spermatids. VIM is a somatic cell marker gene).

[0029] Established marker genes for specific cell types were used to identify and examine cell clusters in bovine testes. Germ cell types can be further divided into spermatogonia ( DAZL , DMRT1 , UCHL1 , SYCP3 , STRA8 )、spermatocytes( GKAP1 , TBPL1 , UBE2C , EFHD1 ), round spermatids ( TMEM190 , ACRV1 ), elongated spermatids ( OTUB2 , TNP1 , PRM2 , KIF5C ) and sperm ( CA2 , TSSK6 Somatic cell types include supporting cells ( FATE1 , CITED1 ), myoid cells ( MYH11 ), lymphatic endothelial cells ( MMRN1 ), testicular endothelial cells ( PRSS23 , CD34 , VWF , TIE1 ), interstitial cells ( IGF1 , DCN , SFRP2 ), T cells ( CD52 , CD69 ), macrophages ( Q1Q , Q1Q , Q1C , CD14 , CD163 , CD74 , CSF1R , LYZ ) and fibroblasts ( CXCL12 ),like Figure 2 Medium B and Figure 8 As shown in Figure 2, the marker genes of the above different cell types are all within the range of marker genes screened by the present invention. As expected and supported by subsequent analysis, the main cell types identified in this study are consistent with the cell types observed in the testis of other mammals. In order to confirm the clustering results, the expression analysis of cluster-specific marker genes was performed using Loupe software, as shown in Figure 2. Figure 2 As shown in C, the expression level of each marker gene is increased in a specific cell type.

[0030] In addition, statistical analysis of the number of genes, total transcripts, and number of cells in each cell type in bovine testes was performed, and the results of the statistical analysis are shown in Figure 2Figure 3D. The results showed that germ cells, with the exception of elongating spermatids, showed a lower number of genes detected per cell and a lower number of unique molecular identifiers (UMIs) compared to somatic cells. In addition, spermatogonia, macrophages, and myoid cells were observed to show lower numbers of UMIs relative to cells of other cell types. Furthermore, the number of genes detected in somatic cells in bovine testes averaged 2K, which was significantly higher compared to other species, indicating a unique gene expression profile and robust cellular conditions.

[0031] 4. Enrichment analysis of differentially expressed genes Each cell type was compared with the other cell types to identify differentially expressed genes (DEGs). The criteria used to identify genes with differences in expression levels were mean log2 fold change ≥ 1 and adjusted P value ≤ 0.01. GO term enrichment analysis of DEGs in each cell type was performed using the online tool David. The upregulated DEGs in each cell type were uploaded as a gene list, and normal cattle were selected as the analysis control. GO terms that met the thresholds of gene count ≥ 3 and P value ≤ 0.01 were considered significant.

[0032] Gene expression patterns determine cell function, therefore, we performed a comparative analysis of gene expression between cell groups. This analysis identified highly differentially altered genes (DEGs) between the various testicular cell types in the dataset, and the results are shown in Figure 3 , showing significant differences in gene expression in different cell types. Specifically, the number of genes identified in each different cell type was: 4066 in fibroblasts, 4733 in spermatids, 4466 in elongating spermatids, 2171 in supporting cells, 3948 in lymphatic endothelial cells, 4112 in interstitial cells, 3656 in T cells, 4628 in spermatogonia, and 3768 in macrophages. These findings suggest the existence of potential novel cell type-specific marker genes that may serve as distinguishing factors between different cell types in bovine testis. We identified TKTL1 , FMR1NB , ESX1 , WDHD1 and RBBP8 As a classification marker gene for spermatogonia, GSN , APOD , OGN , COL1A2 , APOE , COL1A1 , GSTM4 , LAMA1 and C4A As a classification marker gene for mesenchymal cells, DEFB119 , INHA , OXT , AQP8 , WFDC15B and AARD As a classification marker gene for supporting cells, BMX As a classification marker gene for fibroblasts. It is worth noting that TKTL1 It has also been identified as a marker gene for bovine testicular spermatogonia. When using this gene to identify cell types, it should be detected together with other genes to improve the accuracy of identification. Therefore, our analysis confirmed the known marker genes in mammalian testicular cells and provided candidate key functional protein genes with potential functions in the development of bovine spermatogonia.

[0033] To explore the functional enrichment of individual testicular cell types in the single-cell sequencing dataset, we performed GO (Gene Ontology) analysis of the upregulated differentially expressed genes (DEGs) using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Our study revealed unique functional enrichment signatures for various germ and somatic cell types and highlighted the top five upregulated GO terms for differentially expressed genes associated with each cell type, such as Figure 4 As shown. For example, germ cells showed significant enrichment for genes related to spermatogenesis, especially those related to flagellar sperm motility and sperm flagellum. In contrast, highly enriched genes in spermatogonia mainly play a role in tissue protein binding and RNA binding. Support cells showed enrichment for genes related to mitochondrial structure and function, while Leydig cells showed enrichment for genes related to extracellular matrix organization and integrin binding. In addition, enrichment of ribosome-related genes was observed in testicular endothelial cells, lymphatic endothelial cells, and T cells. Subsequent analysis of gene expression patterns in fibroblasts and myoid cells revealed a unique set of genes related to protein binding and actin binding. In addition, a small number of macrophage populations showed high enrichment for genes related to immune response. The enrichment of these GO terms in different testicular cell types has important implications in the biological process of spermatogenesis.

[0034] Analysis of the developmental trajectory of bovine testicular germ cells To investigate the differentiation pathways of germ cells in the testis, trajectory analysis was performed using the Monocle 2 software package (for specific analysis methods, refer to Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single-CellmRNA Quantification and Differential Analysis With Census. Nat Methods (2017)14(3):309–315). A subset of 3000 germ cells was selected from the single-cell RNA sequencing dataset, and the top 1000 genes showing significant differential expression were selected for further study. Initially, the developmental trajectory of germ cells was reconstructed by arranging them along a pseudo-time axis according to their gene expression profiles. Following this, the Monocle 2 algorithm was used to identify branch points and infer differentiation pathways within bovine testicular germ cells. Our pseudo-time analysis revealed a sequential progression from spermatogonia to spermatocytes, round spermatids, elongating spermatids, and finally to sperm, providing insight into the spatial distribution of various germ cell types within the testis, such as Figure 5 As shown in A and B. During the developmental trajectory of germ cells, we observed multiple gene expression changes, such as in spermatogonia HSPA8 and HNRNPA2B1 The expression of protamine genes, which are essential for sperm chromosome condensation, was enhanced. PRM1 , PRM2 ) is gradually increased, such as Figure 5 As shown in C.

[0035] In addition, heat maps were generated, such as Figure 6 Figure 2 shows a visual representation of the hierarchical relationships between gene clusters showing differential expression during pseudotime in bovine testicular germ cells. The top 20 GO terms for clusters 1, 2, and 3 are highlighted in bold on the right side of the heatmap. Cluster 1 consists of pre-branching spermatogonia and spermatocytes, showing enrichment for ribosome and RNA processing pathways. Cluster 2 shows enrichment for genes related to sperm biology, while cluster 3 shows enrichment for genes related to acrosome generation.

[0036] The invention concept is described in detail using specific examples herein, and the description of the above embodiments is only used to help understand the core idea of ​​the invention. It should be pointed out that any obvious modification, equivalent substitution or other improvement made by a person of ordinary skill in the art without departing from the invention concept should be included in the protection scope of the present invention.

Claims

1. A method for screening key functional proteins in different cells of bovine testis, characterized in that: The following steps are involved: (1) Isolate bovine testicular cells and generate single-cell libraries using the 10×Genomics platform. Perform single-cell RNA sequencing after standard quality control to obtain sequencing raw data files; (2) The raw data files were deduplicated using the cell Ranger mkfastq program to generate FASTQ files. The FASTQ files were aligned, filtered, barcode counted, and UMI counted on the bovine UMD3.1 genome, and processed using the count tool of the cell Ranger software to obtain output files. (3) The output file was imported into the Seurat package to standardize the gene expression values ​​in different samples. The top 1000 genes with high differential changes were determined by canonical correlation analysis, the data dimension was reduced, and a merged data set was obtained. Clustering and UMAP analysis were performed on the merged data set. The first 1 to 12 canonical components were used, and the weighted shared nearest neighbor graph-based clustering method was used. The default parameters of the FindAllMarkers function of the Seurat package were used to find the differentially expressed genes in different cell clusters. Genes with an expression level of more than 20% and a logarithmic change greater than 0.25 were identified and screened to obtain different cell clusters and marker genes for each cell cluster. The marker genes for each cluster were determined by the Wilcoxon test. The marker genes express the key functional proteins of the cell clusters in which they are located.

2. The method according to claim 1, characterized in that The standard quality control conditions described in step (1) are: all genes are expressed in more than 3 cells, the number of genes detected in each cell is between 200 and 4000, and the percentage of mitochondrial gene expression is less than 5%.

3. Application of the method according to claim 1 or 2 in bull breeding, characterized in that: Used for genetic enhancement of bulls.

4. Application of the key functional protein obtained by the method of claim 1 or 2 in identifying different types of bovine testicular cells.

5. A method for identifying different types of cells in bovine testicles, characterized in that: Cells are identified by detecting the expression levels of different genes in cells. The expression of one or more genes in the following different types of cells is higher than that in other types of cells: Spermatogonia: TKTL1 , FMR1NB , ESX1 , WDHD1 , RBBP8 ; Support cells: DEFB119 , INHA , OXT , AQP8 , WFDC15B , AARD ; Interstitial cells: GSN , APOD , OGN , COL1A2 , APOE , COL1A1 , GSTM4 , LAMA1 , C4A ; Fibroblasts: BMX .

6. The method according to claim 5, characterized in that The one or more genes corresponding to the different types of cells are also combined with any of the following genes: Spermatogonia: DAZL , DMRT1 , UCHL1 , SYCP3 , STRA8 ; Support cells: FATE1 , CITED1 ; Interstitial cells: IGF1 , DCN , SFRP2 ; Fibroblasts: CXCL12 .

Images