bioRxivDevelopmental BiologyPreprint
Transcriptome Landscape Reveals Underlying Mechanisms of Ovarian Cell Fate Differentiation and Primordial Follicle Assembly
Junjie WangiD, Wei GeiD, Qiu‐Yue ZhaiiD, Jing‐Cai LiuiD, Xiaowen SuniD, Wen‐Xiang LiuiD, Lan LiiD, Chuzhao LeiiD, Paul William DyceiD, Massimo De FeliciiD, Wei SheniD
Abstract
Primordial follicle assembly in mammals occurs at perinatal ages and largely determines the ovarian reserve available to support the reproductive lifespan. The primordial follicle structure is generated by a complex network of interactions between oocytes and ovarian somatic cells that remain poorly understood. In the present research, using single-cell RNA sequencing performed over a time-series on mouse ovaries coupled with several bioinformatics analyses, the complete dynamic genetic programs of germ and granulosa cells from E16.5 to PD3 are reported for the first time. The time frame of analysis comprises the breakdown of germ cell cysts and the assembly of primordial follicles. Confirming the previously reported expression of genes by germ cells and granulosa cells, our analyses identified ten distinct gene clusters associated to germ cells and eight to granulosa cells. Consequently, several new genes expressed at significant levels at each investigated stage were assigned. Building single-cell pseudo temporal trajectories five states and two branch points of fate transition for the germ cells, and three states and one branch point for the granulosa cells were revealed. Moreover, GO and ClueGO term enrichment enabled identifying biological processes, molecular functions and cellular components more represented in germ cells and granulosa cells or common to both cell types at each specific stage. Finally, by SCENIC algorithm, we were able to establish a network of regulons that can be postulated as likely candidates for sustaining germ cell specific transcription programs throughout the investigated period.
- organism: mouse
- assay: scRNA-seq
- research focus: clustering
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
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