Zaniker, E. J. | bioRxiv Science

bioRxiv2024

Self-organizing ovarian somatic organoids preserve cellular heterogeneity and reveal cellular contributions to ovarian aging

Ovarian somatic cells are essential for reproductive function, but no existing ex vivo models recapitulate the cellular heterogeneity or interactions within this compartment. We engineered a novel ovarian somatic organoid model by culturing a stroma-enriched fraction of mouse ovaries in scaffold-free agarose micromolds. Ovarian somatic organoids self-organized, maintained diverse cell populations, produced extracellular matrix, and secreted hormones. Organoids generated from reproductively old mice exhibited reduced aggregation and growth compared to young counterparts, as well as differences in cellular composition. Interestingly, matrix fibroblasts from old mice demonstrated upregulation of pathways associated with the actin cytoskeleton and downregulation of cell adhesion pathways, indicative of increased cellular stiffness which may impair organoid aggregation. Cellular morphology, which is regulated by the cytoskeleton, significantly changed with age and in response to actin depolymerization. Moreover, actin depolymerization rescued age-associated organoid aggregation deficiency. Overall, ovarian somatic organoids have advanced fundamental knowledge of cellular contributions to ovarian aging.

cell biology↗

bioRxiv2024

Single-cell and spatiotemporal profile of ovulation in the mouse ovary

Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.

bioinformatics↗