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Russo, D. D.

Publications and source records attributed to Russo, D. D..

2 recordsLinked to original sources

Transcriptomic analysis of whole staged ovarian follicles reveals stage-specific folliculogenesis signatures in mice

Activation and maturation of ovarian follicles are essential for female reproduction, yet the underlying molecular and transcriptional mechanisms that govern these processes remain poorly understood. In this study, we used single follicle RNA-sequencing (RNA-seq) to identify transcriptional signatures of staged ovarian follicles, from primordial to secondary stages, to uncover the genes and pathways involved in early folliculogenesis. Our findings demonstrate that primordial follicles are transcriptionally distinct from growing follicles, with enrichment in DNA integrity and RNA processing pathways, which may play a role in preserving oocyte genomic stability and cell state during dormancy. Additionally, our analysis reveals minimal transcriptomic differences between primary and secondary follicles using traditional differential expression analysis. To better distinguish growing follicle stages, we introduce unsupervised approaches, including discrete-variable predictors of follicle stage and weighted gene co-expression analysis. We identified pathways involved in DNA integrity, meiotic arrest, and cellular metabolism that drive the transition from dormant to active follicle states, as well as pathways related to cellular growth, ECM organization, and biosynthesis in growing follicle stages. Our study offers novel insights into the molecular mechanisms governing early follicle activation and growth, providing a foundation for future research with applications in reproductive biology, contraception, and fertility preservation. Author SummaryThe development of ovarian follicles is essential for female fertility, but the molecular signals that control their growth remain unclear. In this study, we used advanced gene sequencing techniques to analyze the genetic activity of individual ovarian follicles at different stages of early development. We found that dormant follicles have unique gene expression patterns that help protect the genetic material of the egg and maintain their inactive state. In contrast, follicles that have begun to grow show increased activity in genes related to cell growth, communication, and structural changes. Interestingly, we observed that early growing follicles are more similar to each other than previously thought, prompting us to apply new analytical methods to better distinguish their developmental stages. Our findings highlight key biological pathways that regulate the transition from dormant to active follicles and uncover new genes that may play a role in this process. Understanding these mechanisms provides valuable insights into ovarian biology and could inform future research on fertility treatments, contraception, and reproductive health.

bioinformatics↗

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↗