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Integration and gene co-expression network analysis of scRNA-seq transcriptomes reveal heterogeneity and key functional genes in human spermatogenesis.

ABSTRACT: Spermatogenesis is a complex process of cellular division and differentiation that begins with spermatogonia stem cells and leads to functional spermatozoa production. However, many of the molecular mechanisms underlying this process remain unclear. Single-cell RNA sequencing (scRNA-seq) is used to sequence the entire transcriptome at the single-cell level to assess cell-to-cell variability. In this study, more than 33,000 testicular cells from different scRNA-seq datasets with normal spermatogenesis were integrated to identify single-cell heterogeneity on a more comprehensive scale. Clustering, cell type assignments, differential expressed genes and pseudotime analysis characterized 5 spermatogonia, 4 spermatocyte, and 4 spermatid cell types during the spermatogenesis process. The UTF1 and ID4 genes were introduced as the most specific markers that can differentiate two undifferentiated spermatogonia stem cell sub-cellules. The C7orf61 and TNP can differentiate two round spermatid sub-cellules. The topological analysis of the weighted gene co-expression network along with the integrated scRNA-seq data revealed some bridge genes between spermatogenesis's main stages such as DNAJC5B, C1orf194, HSP90AB1, BST2, EEF1A1, CRISP2, PTMS, NFKBIA, CDKN3, and HLA-DRA. The importance of these key genes is confirmed by their role in male infertility in previous studies. It can be stated that, this integrated scRNA-seq of spermatogenic cells offers novel insights into cell-to-cell heterogeneity and suggests a list of key players with a pivotal role in male infertility from the fertile spermatogenesis datasets. These key functional genes can be introduced as candidates for filtering and prioritizing genotype-to-phenotype association in male infertility.

SUBMITTER: Salehi N

PROVIDER: S-EPMC8476490 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundConsiderable evidence suggests that the heterogeneity of ovarian cancer (OC) is a major cause of treatment failure. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to analyse the heterogeneity of the tumour at the single-cell level, leading to a better understanding of cell function at the genetic and cellular levels.MethodsOC scRNA-seq data were extracted from the Gene Expression Omnibus (GEO) database and the FindCluster () package used for cell cluster analysis. The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a Hallmark gene set score and bulk RNA-seq data were used to analyse the key genes of OC-associated immune cell subsets. CIBERSORT was used to identify immune scores of cells and the "WGCNA" package for the weighted correlation network analysis (WGCNA). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses of subtype groups were performed by GSEA. Then, univariate Cox and lasso regression were performed to further establish a signature. Finally, qPCR and immunohistochemistry staining were used to evaluate the expression of signature genes in OC.ResultsTwo scRNA-seq (GSE154600 and GES158937) datasets were integrated to obtain 20 cell clusters. T cells or NK cells (cluster 5, 6, 7, 11), B cells (cluster 16, 19, 20) and myeloid cells (cluster 4, 9, 10) were clustered according to immune cell markers. The ssGSEA revealed that M1- and M2-like myeloid cell-related genes were significantly upregulated in P3 and P4 patients in the GSE154600 data. Immune cell analysis in TCGA-OC showed that a high abundance of M1-like tumour-associated macrophages (TAMS) predicts better survival. WGCNA, univariate Cox and lasso Cox regression established a two-gene signature (RiskScore=-0.059*CXCL13-0.034*IL26). Next, the TCGA-test and TCGA-OC were used to test the risk prediction ability of the signature, showing a good effect in the datasets. Moreover, the qPCR and immunohistochemistry staining revealed that the expression of CXCL13 and IL26 was reduced in OC tissues.ConclusionA two-gene signature prognostic stratification system (CXCL13 and IL26) was developed based on the heterogeneity of OC immune cells to accurately evaluate the prognostic risk.

Project description:BackgroundNumerous studies support that Human papillomavirus (HPV) can cause cervical cancer. However, few studies have surveyed the heterogeneity of HPV infected or uninfected (HPV+ and HPV-) cervical cancer (CESC) patients. Integration of scRNA-seq and TCGA data to analyze the heterogeneity of HPV+ and HPV- cervical cancer patients on a single-cell level could improve understanding of the cellular mechanisms during HPV-induced cervical cancer.MethodsCESC scRNA-seq data obtained from the Gene Expression Omnibus (GEO) database and the Seurat, Monocle3 package were used for scRNA-seq data analysis. The ESTIMATE package was used for single-sample gene immune score, CIBERSORT package was used to identify immune scores of cells, and the "WGCNA" package for the weighted correlation network analysis. Univariate Cox and LASSO regression were performed to establish survival and relapse signatures. KEGG and GO analyses were performed for the signature gene. Gene Expression Profiling Interactive Analysis was used for Pan-cancer analysis.ResultsIn the HPV+ CESC group, CD8+ T cells and B cells were down-regulated, whereas T reg cells, CD4+ T cells, and epithelial cells were up-regulated according to scRNA-seq data. Survival analysis of TCGA-CESC revealed that increased expression of naive B cells or CD8+ T cells favors the survival probability of CESC patients. WGCNA, univariate Cox, and LASSO Cox regression established a 9-genes survival signature and a 7-gene relapse model. Pan-cancer analysis identified IKZF3, FOXP3, and JAK3 had a similar distribution and effects in HPV-associated HNSC.ConclusionAnalysis of scRNA-seq and bulk RNA-seq of HPV+ and HPV- CESC samples revealed heterogeneity from transcriptional state to immune infiltration. Survival and relapse models were adjusted according to the heterogeneity of HPV+ and HPV- CESC immune cells to assess the prognostic risk accurately. Hub genes represent similar protection in HPV- associated HNSC while showing irrelevant to other potential HPV-related cancers.

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Integration and gene co-expression network analysis of scRNA-seq transcriptomes reveal heterogeneity and key functional genes in human spermatogenesis.

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