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<p>Spermatogenesis—the process of sperm cell development—is highly dependent on precise and dynamic regulation of gene expression, much of which is controlled by Regulatory networks and hub genes governing spermatogonial stem cells (SSC) identity, including components involved in post-transcriptional regulations. During this complex process, a wide range of RNA-binding proteins (RBPs) and RNA processing enzymes coordinate the transcription, splicing, transport, storage, and translation of mRNAs required for germ cell development. Raw sequencing data were processed and normalized using standard bioinformatics pipelines (e.g., STAR, DESeq2). To identify key Regulatory networks and hub genes governing SSC identity, including components involved in post-transcriptional regulations, we applied integrative omics approaches by combining transcriptomic data with publicly available proteomic and interactome databases. Hub proteins were determined through weighted gene co-expression network analysis (WGCNA) and centrality scoring in protein-protein interaction (PPI) networks. Machine learning models, including random forest and support vector machine (SVM), were trained to classify critical regulators based on expression features and metadata. Additionally, cell-cell communication was inferred using ligand-receptor interaction analysis via CellChat and NicheNet to explore the microenvironmental impact on RNA metabolic processes. All findings were validated across culture conditions and biological replicates to ensure robustness. Microarray analysis revealed 92 upregulated and 126 downregulated genes in SSCs versus htFib, with enrichment in motile cilium assembly, spermatid development, and gamete generation. DEGs were mainly extracellular matrix proteins, transporters, and adhesion molecules. PPI network and KEGG analyses identified key hub genes (e.g., MMP3, CAV1, TGFBR2) involved in cell cycle and meiosis pathways. Single-cell RNA-seq of human testicular cells identified 17 clusters, including germ and somatic cell types. Germ cell re-clustering defined SSC subpopulations marked by genes such as FAM74F1, SMCP, and ADAD1. GSEA indicated metabolic shifts, especially in oxidative phosphorylation, during SSC differentiation. Ligand–receptor analysis revealed active cell-cell signaling, particularly involving fibroblasts and macrophages. These findings enhance the understanding of human spermatogonia culture and gene expression, providing insights into SSC biology and potential applications in reproductive medicine.</p>

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Machine learning, whole-transcriptome and integrative omics analysis reveals key regulatory networks governing human spermatogonial stem cells

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Danial Hashemi Karoii,
  • Maryam Osanloo,
  • Hossein Azizi,
  • Thomas Skutella

摘要

Spermatogenesis—the process of sperm cell development—is highly dependent on precise and dynamic regulation of gene expression, much of which is controlled by Regulatory networks and hub genes governing spermatogonial stem cells (SSC) identity, including components involved in post-transcriptional regulations. During this complex process, a wide range of RNA-binding proteins (RBPs) and RNA processing enzymes coordinate the transcription, splicing, transport, storage, and translation of mRNAs required for germ cell development. Raw sequencing data were processed and normalized using standard bioinformatics pipelines (e.g., STAR, DESeq2). To identify key Regulatory networks and hub genes governing SSC identity, including components involved in post-transcriptional regulations, we applied integrative omics approaches by combining transcriptomic data with publicly available proteomic and interactome databases. Hub proteins were determined through weighted gene co-expression network analysis (WGCNA) and centrality scoring in protein-protein interaction (PPI) networks. Machine learning models, including random forest and support vector machine (SVM), were trained to classify critical regulators based on expression features and metadata. Additionally, cell-cell communication was inferred using ligand-receptor interaction analysis via CellChat and NicheNet to explore the microenvironmental impact on RNA metabolic processes. All findings were validated across culture conditions and biological replicates to ensure robustness. Microarray analysis revealed 92 upregulated and 126 downregulated genes in SSCs versus htFib, with enrichment in motile cilium assembly, spermatid development, and gamete generation. DEGs were mainly extracellular matrix proteins, transporters, and adhesion molecules. PPI network and KEGG analyses identified key hub genes (e.g., MMP3, CAV1, TGFBR2) involved in cell cycle and meiosis pathways. Single-cell RNA-seq of human testicular cells identified 17 clusters, including germ and somatic cell types. Germ cell re-clustering defined SSC subpopulations marked by genes such as FAM74F1, SMCP, and ADAD1. GSEA indicated metabolic shifts, especially in oxidative phosphorylation, during SSC differentiation. Ligand–receptor analysis revealed active cell-cell signaling, particularly involving fibroblasts and macrophages. These findings enhance the understanding of human spermatogonia culture and gene expression, providing insights into SSC biology and potential applications in reproductive medicine.