Transcriptomics

Dataset Information

0

Transcriptomic profiling of neonatal mouse granulosa cells reveals new insights into primordial follicle activation


ABSTRACT: The dormant population of ovarian primordial follicles is determined at birth and serves as the reservoir for future female fertility. Of equal importance to fertility is the rate that primordial follicles activate and enter folliculogenesis, yet our understanding of the molecular, biochemical, and cellular processes underpinning primordial follicle activation remains limited. The survival of primordial follicles relies on the correct complement and morphology of granulosa cells, which provide signalling factors essential for oocyte and follicular survival. To investigate the contribution of granulosa cells in the primordial-to-primary follicle transition, ovaries from C57Bl/6 mice were enzymatically dissociated at two time points spanning the initial wave of primordial follicle activation. Post-natal day (PND) 1 ovaries yielded primordial granulosa cells, and PND4 ovaries yielded a mixed population of both primordial and primary granulosa cells. The comparative transcriptome of granulosa cells at these time points was generated via the Illumina NextSeq 500 system which identified 132 significantly differentially expressed transcripts. This transcriptomic dataset confirmed the expression of factors known to be involved in primordial follicle activation belonging to TGF-B and EIF4E signalling pathways, as well as novel factors linked to pathways involved in primordial follicle activation (ZFX, POD1, FRZB). This study highlights the dynamic changes in gene expression of granulosa cells during primordial follicle activation and provides evidence for a renewed focus into the Wnt signalling pathway’s role in primordial follicle activation.

ORGANISM(S): Mus musculus

PROVIDER: GSE162927 | GEO | 2020/12/10

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2008-06-11 | E-GEOD-11133 | biostudies-arrayexpress
2008-04-11 | GSE11133 | GEO
2022-06-29 | GSE206681 | GEO
2024-05-30 | GSE263843 | GEO
2010-02-21 | E-GEOD-20358 | biostudies-arrayexpress
2008-06-16 | E-GEOD-9300 | biostudies-arrayexpress
2023-05-27 | GSE232350 | GEO
2008-11-04 | E-GEOD-11495 | biostudies-arrayexpress
2024-04-30 | GSE253194 | GEO
2024-09-01 | GSE263743 | GEO