Cellular and Molecular Life SciencesDevelopmental BiologyPeer-reviewed journal article
Dissecting the initiation of female meiosis in the mouse at single-cell resolution
Wei GeiD, Junjie WangiD, Ruiqian ZhangiD, Shao‐Jing TaniD, Fa‐Li ZhangiD, Wen‐Xiang LiuiD, Lan LiiD, Xiao-Feng SuniD, Shun‐Feng ChengiD, Paul William DyceiD, Massimo De FeliciiD, Wei SheniD
Abstract
Germ cell meiosis is one of the most finely orchestrated events during gametogenesis with distinct developmental patterns in males and females. However, in mammals, the molecular mechanisms involved in this process remain not well known. Here, we report detailed transcriptome analyses of cell populations present in the mouse female gonadal ridges (E11.5) and the embryonic ovaries from E12.5 to E14.5 using single cell RNA sequencing (scRNA seq). These periods correspond with the initiation and progression of meiosis throughout the first stage of prophase I. We identified 13 transcriptionally distinct cell populations and 7 transcriptionally distinct germ cell subclusters that correspond to mitotic (3 clusters) and meiotic (4 clusters) germ cells. By comparing the signature gene expression pattern of 4 meiotic germ cell clusters, we found that the 4 cell clusters correspond to different cell status en route to meiosis progression, and therefore, our research here characterized detailed transcriptome dynamics during meiotic prophase I. Reconstructing the progression of meiosis along pseudotime, we identified several new genes and molecular pathways with potential critical roles in the mitosis/meiosis transition and early meiotic progression. Last, the heterogeneity within somatic cell populations was also discussed and different cellular states were identified. Our scRNA seq analysis here represents a new important resource for deciphering the molecular pathways driving meiosis initiation and progression in female germ cells and ovarian somatic cells.
- organism: mouse
- assay: scRNA-seq
- research focus: clustering
- research focus: differential expression
- research focus: database / resource
- approach: statistical model
- Citations
- 58
- FWCI
- 6.74
- Field percentile
- top 2%
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