Integrated Single-cell Proteomic and Transcriptomic Landscape of Mouse Folliculogenesis
- Journal
- Genomics, proteomics & bioinformatics (Q1)
- Published
- 23 July 2026
- Study design
- Unclassified
- Evidence level
- Level 5, Expert Opinion (CEBM 5)
- Authors
- Hongchao Li, Xinshuai Zhang, Huimin Kang, Yun Yang, Xinyu Xiao, Yan Wang, et al.
- PMID
- 42489541
- DOI
- 10.1093/gpbjnl/qzag068
Why clinicians should know about it
- Picked for Biochemistry (medical) (paper of the day, 24 July 2026).
Abstract
Folliculogenesis is a complex process essential to female fertility, characterized by multifaceted communication between oocytes and granulosa cells (GCs). While transcriptional regulation during folliculogenesis has been extensively studied, the proteomic landscape remains largely unexplored. Here, we profiled both the proteomic and transcriptomic landscapes of single oocytes and their surrounding mini-bulk GCs across four consecutive stages, from secondary to preovulatory follicles. Integrated dual-omics analysis provided a high-resolution characterization of cell type-specific transcriptional and proteomic changes. Proteomic profiling revealed coordinated metabolic programs, in which oocytes shift toward lipid storage while GCs enhance energy production and steroidogenic metabolism to support oocyte maturation. These metabolic changes in oocytes were accompanied by dynamic remodeling of mitochondrial organization. In addition, we identified novel transcription factors involved in regulating folliculogenesis, as well as a SATB1-centered regulatory network that may reflect preparatory chromatin remodeling preceding zygotic genome activation. Furthermore, GDF9-BMPR2 signaling progressively increased from the secondary stage to the preovulatory stage, indicating strengthened intercellular communication between oocytes and GCs. Together, these findings provide mechanistic insights into oocyte development and follicle growth, with potential implications for novel fertility treatments and diagnostic strategies.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.