bioRxivCell BiologyPreprint
Scalable primordial germ cell-like-cell platform for functional genomics identifies epigenetic fertility modifiers
Abstract
Primordial germ cells (PGCs) are the founder cells of the germline. The ability to generate PGC-like cells (PGCLCs) from pluripotent stem cells has advanced our knowledge of gametogenesis and holds promise for developing infertility treatments. However, generating an ample supply of PGCLCs for demanding applications such as large-scale genetic screens has been a limitation. Here, we demonstrated that simultaneous overexpression of 4 transcriptional factors - Nanog and three PGC master regulators Prdm1, Prdm14 and Tfap2c - in suspended mouse epiblast like cells (EpiLCs) and formative embryonic stem cells (ESCs) results in efficient and cost-effective production of PGCLCs. Nanog overexpression enhanced the PGC regulatory network, suppressed differentiation of somatic lineages, and maintained PGC fate. PGCLCs generated in this manner have transcriptomes similar to in vivo PGCs and are more advanced than cytokine-induced PGCLCs. These differentiated PGCLCs could be sustained over prolonged periods of culture and could differentiate into spermatogonia-like cells in vitro. We exploited this platform to conduct a bulk epigenome-wide CRISPRi screen to identify genes that decreased the efficiency of PGCLC formation when downregulated. One of the identified candidates was Ncor2, a transcriptional repressor that acts via recruitment of Class I and Class IIa histone deacetylases (HDACs). Consistently with this finding, the HDAC inhibitors valproic acid (VPA; an anti-convulsant) and sodium butyrate (SB; a widely-used dietary supplement) suppressed PGCLC differentiation. Furthermore, exposure of developing mouse embryos to SB or VPA caused hypospermatogenesis. This work demonstrates the feasibility of this platform to efficiently conduct large-scale functional screens of genes, chemicals, or other agents that may impact germline development.
- organism: mouse
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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