Abstract
Primordial follicles are quiescent ovarian structures comprised of a single oocyte surrounded by a layer of somatic supporting pregranulosa cells. Primordial follicle activation is the first step towards oocyte maturation and, ultimately, ovulation. As the number of quiescent primordial follicles is finite, their rate of activation is a critical parameter of the duration of the female reproductive lifespan. Activation status is established by the presence of cuboidal and proliferative granulosa cells in primary follicles, rather than squamous and quiescent pregranulosa cells in primordial follicles. Here, using a continuous Entropy Sort Feature Weighting approach on single-cell RNA sequencing data, we identify a distinct transcriptomic signature of activating pregranulosa cells in neonatal wildtype mice. This signature contains several genes previously linked with mature granulosa cells as well several novel candidates: Slc18a2, Tnni3, Fam13a and Myo1e. We confirm expression of Slc18a2 and TNNI3 in the granulosa cells of activating follicles in embryonic, neonatal and adult mouse ovaries. Perturbation of cell cycle inhibitor p27kip1 in Cdkn1b-/- mice results in complete activation of all primordial follicles during this neonatal period. Contrary to previous reports on this established mouse model, we find substantial transcriptomic changes in embryonic Cdkn1b-/- ovaries. Upon loss of cell-cycle inhibition we find increased expression of our signature of pregranulosa cell activation, particularly that of cardiac troponin I (Tnni3). We conclude that pregranulosa cells engage a distinct transcriptional programme prior to cell-cycle dependent primordial follicle activation.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This manuscript was deposited in BioRxiv as a preprint (https://doi.org/10.1101/2022.10.24.513438). The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
This manuscript has been revised to focus on the transcriptional changes in pregranulosa cells, using a new bioinformatic method. The manuscript also includes new data including Figure 3 and Supplemental Figures 2 and 3.
