SRSF10 is essential for progenitor spermatogonia expansion by regulating alternative splicing.
Wenbo LiuXukun LuZheng-Hui ZhaoRuibao SuQian-Nan Li LiYue XueZheng GaoSi-Min Sun SunWen-Long LeiLei LiGeng AnHanyan LiuZhiming HanYing-Chun OuyangYi HouZhen-Bo WangQian-Qian ShaJianqiao LiuPublished in: eLife (2022)
Alternative splicing expands the transcriptome and proteome complexity and plays essential roles in tissue development and human diseases. However, how alternative splicing regulates spermatogenesis remains largely unknown. Here, using a germ cell-specific knockout mouse model, we demonstrated that the splicing factor <i>Srsf10</i> is essential for spermatogenesis and male fertility. In the absence of SRSF10, spermatogonial stem cells can be formed, but the expansion of Promyelocytic Leukemia Zinc Finger (PLZF)-positive undifferentiated progenitors was impaired, followed by the failure of spermatogonia differentiation (marked by KIT expression) and meiosis initiation. This was further evidenced by the decreased expression of progenitor cell markers in bulk RNA-seq, and much less progenitor and differentiating spermatogonia in single-cell RNA-seq data. Notably, SRSF10 directly binds thousands of genes in isolated THY<sup>+</sup> spermatogonia, and <i>Srsf10</i> depletion disturbed the alternative splicing of genes that are preferentially associated with germ cell development, cell cycle, and chromosome segregation, including <i>Nasp</i>, <i>Bclaf1</i>, <i>Rif1</i>, <i>Dazl</i>, <i>Kit</i>, <i>Ret,</i> and <i>Sycp1</i>. These data suggest that SRSF10 is critical for the expansion of undifferentiated progenitors by regulating alternative splicing, expanding our understanding of the mechanism underlying spermatogenesis.
Keyphrases
- rna seq
- single cell
- germ cell
- cell cycle
- stem cells
- poor prognosis
- mouse model
- high throughput
- genome wide
- cell proliferation
- electronic health record
- big data
- bone marrow
- binding protein
- bioinformatics analysis
- copy number
- computed tomography
- genome wide identification
- transcription factor
- mycobacterium tuberculosis
- deep learning
- pulmonary tuberculosis
- cell fate
- artificial intelligence