A method for screening key functional proteins of different cells in bovine testis and its application

Abstract

The present invention discloses a method for screening key functional proteins of different cells in bovine testes and its application. The method is based on single-cell RNA sequencing technology, which details five different germ cell types and eight somatic cell types in bovine testicular cells, and provides gene markers specific to testicular cell types, namely key functional protein genes. Through the expression of these proteins, cell types can be quickly and accurately identified. In addition, these proteins can also help us develop targeted intervention measures to improve the fertility of livestock.

Description

Technical Field

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

[0002] Reproductive bulls have a genetic impact on dairy cattle populations exceeding 75%, highlighting the crucial role of bull semen in animal reproduction. The quality of bull semen significantly affects livestock reproductive efficiency and genetic improvement. The testes of cattle are a key reproductive organ and play a vital role in sperm production. Spermatogenesis is a complex process that requires coordination between different types of testicular cells. Testicular cells can be divided into three main stages: proliferation, meiosis, and differentiation. Some spermatogonial stem cells (SSCs) undergo mitosis to maintain the population during the proliferation stage, while others enter the meiosis stage. Spermatocytes undergo two rounds of cell division during the meiosis stage. During the differentiation process, spermatids produced during meiosis undergo a series of complex changes to mature into functional sperm cells, 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 spermatogenesis in bull testes, the cellular composition of bovine testes, and the underlying biology remains limited.

[0003] Although traditional bulk analysis methods provide valuable guidance, they often obscure the inherent cellular heterogeneity in tissues such as the testes, thereby limiting the ability to capture the subtle dynamics of individual cell types. How to rapidly classify and identify different cells in bovine testes, and how to discover the key functional proteins of different testicular cells for further research are still in the primary stage. As a result, it is impossible to intervene in bull gene enhancement, affecting the improvement of bull fertility and reproductive ability. Summary of the Invention

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

[0005] The technical solution of the present invention is described in detail as follows:

[0006] In the first aspect, the present invention provides a method for screening key functional proteins of different cells in bovine testes, including the following steps:

[0007] (1) Isolate bovine testicular cells and generate a single-cell library through the 10×Genomics platform. After performing standard quality control, perform single-cell RNA sequencing to obtain the raw sequencing data file;

[0008] (2)The raw data files were deduplicated by 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 the output files;

[0009] (3)The output files were imported into the Seurat package to standardize the gene expression values in different samples. By the method of canonical correlation analysis (CCA), the top 1000 highly differentially variable genes were determined to reduce the data dimension and obtain the merged dataset;

[0010] The merged dataset was subjected to clustering and UMAP (Uniform Manifold Approximation and Projection) analysis. The first 1 - 12 canonical components (CCs) were used. Using the shared nearest neighbor (SNN) graph - based clustering method, the differentially expressed genes in different cell clusters were found under the default parameters of the FindAllMarkers function in the Seurat package. Genes with an expression level exceeding 20% and a log fold - change greater than 0.25 were identified and screened to obtain different cell clusters and the 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 their respective cell clusters.

[0011] The 10× Genomics platform is an integrated single-cell isolation and library construction platform. The high-throughput single-cell RNA-seq technology of the 10× Genomics platform can fully interface with Illumina sequencers to obtain raw sequencing data files. This raw data is in BCL format (Base Call Library), and data in this format needs to be processed by the cellranger mkfastq tool in the sequencer program to be converted into FASTQ format files for subsequent bioinformatics analysis. Seurat is an R package for quality control, analysis, and exploration of single-cell RNA-seq data, developed by the Satija Lab at 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 non-linear dimensionality reduction algorithm based on topology and manifold learning, and the canonical components (CCs) are the low-dimensional embedding coordinates calculated by the UMAP algorithm.

[0012] 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.

[0013] In a second aspect, the present invention provides the application of any of the above methods in bull breeding for bull gene enhancement.

[0014] In a third aspect, the present invention provides the application of the key functional protein obtained by any of the above methods in identifying different types of cells in bovine testes.

[0015] In a fourth aspect, the present invention provides a method for identifying different types of cells in bovine testes, which is to identify cells by detecting the expression levels of different genes in the cells. One or more of the following genes have higher expression levels in the corresponding different types of cells than in other types of cells:

[0016] Spermatogonia: TKTL1 、 FMR1NB 、 ESX1 、 WDHD1 、 RBBP8 ;

[0017] Sertoli cells: DEFB119 、 INHA 、 OXT 、 AQP8 、 WFDC15B 、AARD ;

[0018] Interstitial cells: GSN , APOD , OGN , COL1A2 , APOE , COL1A1 , GSTM4 , LAMA1 , C4A ;

[0019] Fibroblasts: BMX .

[0020] 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 for cell type identification:

[0021] Spermatogonia: DAZL , DMRT1 , UCHL1 , SYCP3 , STRA8 ;

[0022] Sertoli cells: FATE1 , CITED1 ;

[0023] Interstitial cells: IGF1 , DCN , SFRP2 ;

[0024] Fibroblasts: CXCL12 .

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] Based on the genomics single-cell RNA sequencing (scRNA-seq) technology, the present invention provides a simple and effective method for screening key functional proteins in bovine testicular cells. A total of 60 key functional proteins have been screened out in bovine testicular cells, belonging to five different germ cells and eight somatic cells. These proteins help us elucidate key regulatory elements and signaling pathways by depicting the unique characteristics and gene expression patterns of each cell type, thus providing an in-depth understanding of the complex cellular processes underlying bovine testicular function. Through the expression of these proteins, cell types can also be quickly and accurately identified. In addition, these proteins can also help us develop targeted intervention measures to improve the fertility of livestock. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1Cell clustering analysis results of single-cell RNA sequencing of bovine testis. Among them, A is the experimental workflow diagram of sample collection and analysis; B is the HE staining result diagram of bovine testis tissue; C is the UMAP diagram of cell clustering analysis of single-cell transcriptome data of bovine testis. Each point in the figure represents a cell and is colored according to cell clusters. 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 testis tissue.

[0028] Figure 2 Results of cell types and specific marker genes identified in the clustering and UMAP analysis of the merged dataset in single-cell RNA sequencing. Among them, A is the analysis result diagram of the main testicular cell types corresponding to the bovine testis cell clusters obtained by UMAP analysis; B is a heatmap showing the relative expression of representative cell-specific marker genes in 13 main cell types of the dataset. The color bar indicates the relative expression level of the selected genes; C is the expression pattern diagram of the selected markers used to assign cell types projected on the UMAP diagram. The figure shows a cell marker gene for each cell type, and red (or gray) indicates high (or low) expression levels; D is a comparative statistical chart of the distribution of cell attributes in each of the 13 cell types. nGene represents the number of genes detected in each cell, nUMI represents the number of unique molecular identifiers (UMIs) in each cell, and nCell represents the number of cells in each cell type.

[0029] Figure 3 Statistical chart of gene expression results of different types of cells in the testis in the cell clustering analysis results of single-cell RNA sequencing of bovine testis, showing significant differences in gene expression.

[0030] Figure 4 Statistical results of the enriched GO terms shown in each cell type in the cell clustering analysis results of single-cell RNA sequencing of bovine testis. The top 5 upregulated GO terms in the results are given according to the differentially expressed genes corresponding to the cell types.

[0031] Figure 5 Analysis result diagram of the developmental trajectory of bovine testicular germ cells. Among them, A is the pseudotime trajectory order of bovine testicular germ cells. Each point represents an individual cell, and the color bar from dark to bright indicates the start and end of the trajectory; B is the continuous developmental trajectory of testicular germ cells. The cells are colored according to their predicted positions along the pseudotime trajectory, showing three discrete cell states (pre-branching, cell fate 1, cell fate 2) during the development of germ cells; C is the expression profile of selected genes involved in cell states along the pseudotime trajectory. The black line represents the expression trend of germ cells.

[0032] Figure 6Heatmap (left) of significantly differentially expressed genes and results of GO term enrichment analysis in the developmental trajectory analysis of bovine testicular germ cells. Heatmap analysis showed that DEGs were significantly enriched in three gene clusters, and the GO terms associated with each gene cluster are shown on the corresponding right side. The genes in the heatmap are sorted from top to bottom in turn

[0033] Figure 7 Statistical results of the expression of two genes in different cell types. On the left is the statistical result of DDX4 gene expression in germ cells, and on the right is the statistical result of VIM expression in somatic cells.

[0034] Figure 8 Statistical results of the expression of marker genes in different types of cells. Detailed implementation manner

[0035] To enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe this application in combination with the embodiments and the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application. The instruments and reagents used in the embodiments are all from commercial channels unless otherwise specified.

[0036] Example 1 Screening and verification of new key functional proteins in different cells of bovine testis

[0037] 1. Animal samples and ethical statement

[0038] All experimental procedures involving animals have obtained ethical approval from the Institutional Animal Care and Use Committee of the Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences (IACC20060101). Five adult testes of Chinese Holstein cows were obtained from a bull station in Jinan, Shandong Province, China. Bovine testicular tissues were quickly removed and transported to the laboratory on ice in Hank's balanced salt solution (HBSS) within two hours. Each testis was immediately cut into small pieces and stored frozen in liquid nitrogen. A part of the testicular tissue was fixed in Bouin's fluid and 4% paraformaldehyde for future analysis, while the remaining tissue was used for cell isolation.

[0039] 2. Cell isolation and single-cell RNA sequencing

[0040] Preparation of single-cell suspension of testicular tissue using a two-step enzymatic digestion method: After washing the testicular tissue with 1× phosphate-buffered saline (PBS) containing penicillin / streptomycin, 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), and gently stirred at 25 °C for 10 minutes to dissociate the seminiferous tubules of bovine testis. 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 oscillation. The reaction was terminated by adding DMEM / F12 containing 10% FBS. The mixture was filtered through a 40 μm filter, then centrifuged at 500 g / min for 10 minutes, and washed with 1× PBS to obtain single testicular cells. The obtained cells were resuspended in 1× PBS containing 0.04% bovine serum albumin (BSA) for the next step of processing. The cell viability confirmed by trypan blue staining needed to be higher than 80%, and the cell count was determined using a hemocytometer.

[0041] According to the manufacturer's operating instructions, the single-cell suspension was loaded into each channel of the 10× Genomics platform, and more than 5,000 single cells were captured in total. According to the standard protocol, the Chromium Single Cell 3’ Reagent Kits (V3) (10× Genomics, PN-1000092) were used for single-cell barcoding (a technique to identify individual cells using DNA fragments within the genome), cDNA amplification, and library construction. Subsequently, sequencing was performed by running paired-end sequencing using the Illumina HiSeq 2500 high-throughput sequencing platform to construct a single-cell library.

[0042] 3. Single-cell RNA sequencing and cell clustering analysis

[0043] The single-cell data obtained by docking the 10× Genomics platform with the Illumina sequencing platform is processed by a set of analysis pipelines in the data analysis software cell Ranger (v7.0) (a product of 10× Genomics). cellrangermkfastq is used to deduplicate the raw base call files of the Illumina sequencing platform to generate FASTQ files. These FASTQ files are aligned with the bovine UMD3.1 genome, filtered, barcode counted, and UMI (Unique Molecular Indentifier) counted using cellranger count. Then, the output files of cellranger are imported into Seurat (v4.0) for dimensionality reduction, clustering, and scRNA-seq data analysis.

[0044] A total of 26,889 single cells from the testes of five bulls 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 4000, 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. Through Canonical Correlation Analysis (CCA), the top 1000 highly variable genes were determined and the data was dimensionally reduced to obtain a merged dataset. Subsequently, the first 1 - 12 canonical components (CCs) were used, and a clustering method based on a weighted shared nearest neighbor (SNN) graph was used to perform clustering and UMAP analysis on the merged dataset. The Wilcoxon rank-sum test was used to determine the marker genes for each cluster, and the default parameters were applied through 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.

[0045] Results: Testicular tissues from a total of 5 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 enzyme digestion method, with the survival rate of each sample exceeding 80%. Subsequently, single-cell libraries were generated using the isolated single cells through the 10× Genomics platform, and the process was as Figure 1As shown in A. A total of 40,975 cells were captured, and 26,889 cells met the standard quality control (QC) criteria for further analysis. These libraries generated 3117 Mb of sequencing reads, with an average sequencing saturation rate of 56.94%. Each cell showed an average of 75.49 K sequencing reads and approximately 1612 genes were identified on average (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, such as Figure 1 as shown in B.

[0046] Table 1 Sequencing analysis results of single-cell libraries obtained from different bull testicular cells

[0047]

[0048] The vst dimensionality reduction method in principal component analysis (PCA) was used to identify highly differentially expressed genes. Subsequently, the UMAP (Uniform Manifold Approximation and Projection) algorithm was implemented using the top principal components and graph-based methods (for specific methods, see reference Macosko E Z, Basu A, Satija R, et al. HighlyParallel 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 by UMAP, such as Figure 1 as shown in C. Importantly, all identified cell clusters were consistently present in each testicular sample, such as Figure 1 as shown in D.

[0049] 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 testis, such as Figure 2 shown in A, which shows the analysis results of the main testicular cell types corresponding to the bovine testicular cell clusters obtained by UMAP analysis. Based on DDX4 and VIM markers, the distinction between germ cells and somatic cells was accurately achieved. Please refer to Figure 7 , DDX4 high expression was detected in 5 germ cells, and VIM high expression was detected in 8 somatic cells, thus verifying the accuracy of our clustering results ( DDX4 are marker genes for spermatogonia, spermatocytes, and round spermatids, VIM is a somatic cell marker gene).

[0050] Marker genes of established specific cell types are 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 spermatozoa ( CA2 , TSSK6 ). Somatic cell types include Sertoli 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 ( C1QA , C1QB , C1QC , CD14 , CD163 , CD74 , CSF1R , LYZ ), and fibroblasts ( CXCL12 ), as shown in Figure 2 for B and Figure 8 for, the marker genes of the above different cell types are all within the range of the marker genes screened in the present invention. As expected and supported by subsequent analysis, the major cell types identified in this study are consistent with the cell types observed in the testes of other mammals. To confirm the clustering results, expression analysis of the cluster-specific marker genes was performed using Loupe software, as shown in C in Figure 2 , and the expression levels of each marker gene were elevated in specific cell types.

[0051] In addition, statistical analysis was performed on the number of genes, total transcripts, and cell numbers of each cell type in bovine testes, and the results of the statistical analysis are shown in Figure 2As shown in D. The research results show that compared with somatic cells, except for elongated spermatids, germ cells show a lower number of genes detected per cell and the number of unique molecular identifiers (UMIs). In addition, it was observed that spermatogonia, macrophages, and myoid cells showed a lower cell count relative to other cell types. Moreover, the average number of genes detected in somatic cells in bovine testes was 2K, which is significantly higher compared to other species, indicating a unique gene expression profile and robust cell conditions.

[0052] 4. Enrichment analysis of differentially expressed genes

[0053] Each type of cell was compared with other types of cells to identify differentially expressed genes (DEGs). The criteria used to identify genes with differences in expression levels were an average log2 fold change ≥ 1 and an adjusted P-value ≤ 0.01. The online tool David was used to perform GO term enrichment analysis on the DEGs in each cell type. The upregulated DEGs in each cell type were uploaded as a gene list, and common 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.

[0054] Gene expression patterns determine cell functions. Therefore, we performed a comparative analysis of gene expression between cell groups. Through this analysis, highly differentially changing genes (DEGs) were identified among various testicular cell types in the dataset, and the results are shown in Figure 3 , showing significant differences in gene expression among different types of cells. Specifically, the number of genes identified in each different type of cell: 4066 in fibroblasts, 4733 in sperm, 4466 in elongated spermatids, 2171 in Sertoli cells, 3948 in lymphatic endothelial cells, 4112 in interstitial cells, 3656 in T cells, 4628 in spermatogonia, 3768 in macrophages. These findings indicate that there are potential novel cell type-specific marker genes, which may become differentiating factors for different cell types in bovine testes. We identified TKTL1 , FMR1NB , ESX1 , WDHD1 and RBBP8 as classification marker genes for spermatogonia, GSN , APOD , OGN , COL1A2 , APOE , COL1A1 , GSTM4 , LAMA1 and C4A as classification marker genes for interstitial cells, DEFB119 , INHA , OXT ,AQP8 , WFDC15B and AARD serve as classification marker genes for Sertoli cells, BMX and as classification marker genes for fibroblasts. It is worth noting that TKTL1 has also been identified as a marker gene for bovine testicular spermatogonia. When using this gene to identify cell types, other genes should be detected together 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 bovine spermatogonia development.

[0055] To explore the functional enrichment of individual testicular cell types in single-cell sequencing datasets, we performed Gene Ontology (GO) analysis on upregulated differentially expressed genes (DEGs) using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Our study revealed unique functional enrichment characteristics of various germ and somatic cell types and highlighted the top five upregulated GO terms of differentially expressed genes associated with each cell type, as Figure 4 shown. For example, germ cells showed significant enrichment of genes related to spermatogenesis, especially those related to flagellar sperm motility and sperm flagella. In contrast, genes highly enriched in spermatogonia mainly play roles in histone binding and RNA binding. Sertoli cells showed enrichment of genes related to mitochondrial structure and function, while interstitial cells showed enrichment of 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 population of macrophages showed high enrichment of genes related to immune responses. The enrichment of these GO terms in different testicular cell types is of great significance in the biological process of spermatogenesis.

[0056] Analysis of the Developmental Trajectory of Bovine Testicular Germ Cells

[0057] To study the differentiation pathways of germ cells in the testis, trajectory analysis was performed using the Monocle 2 software package (for the specific analysis method, refer to Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single-Cell mRNA 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 the pseudotime axis according to their gene expression profiles. After that, the Monocle 2 algorithm was used to identify branch points and infer the differentiation pathways within bovine testicular germ cells. Our pseudotime analysis revealed an ordered progression from spermatogonia to spermatocytes, round spermatids, elongated spermatids, and finally to spermatozoa, thus providing insights into the spatial distribution of various germ cell types within the testis, such as Figure 5 shown in A and B. During the developmental trajectory of germ cells, we observed various gene expression changes, such as enhanced expression of HSPA8 and HNRNPA2B1 in spermatogonia, and the gradual upregulation of protamine genes ( PRM1 , PRM2 ) that are crucial for sperm chromosome condensation, as Figure 5 shown in C.

[0058] In addition, heatmaps were generated, as Figure 6 shown, to visually represent the hierarchical relationships between gene clusters showing differential expression over pseudotime in bovine testicular germ cells. The top 20 GO terms of gene clusters 1, 2, and 3 are emphasized in bold on the right side of the heatmap. Cluster 1 consists of pre-branching spermatogonia and spermatocytes and shows enrichment of ribosome and RNA processing pathways. Cluster 2 shows enrichment of genes related to sperm biology, while cluster 3 shows enrichment of genes related to acrosome biogenesis.

[0059] In this article, specific examples are applied to elaborate on the inventive concept in detail. The description of the above embodiments is only used to help understand the core idea of the present invention. It should be noted that for those of ordinary skill in the art, any obvious modifications, equivalent replacements, or other improvements made without departing from the inventive concept shall be included within the protection scope of the present invention.

Claims

1. 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 corresponding gene expression levels of the following different types of cells are higher than those of 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 .

2. The method according to claim 1, characterized in that The corresponding genes in 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 .

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