This update from the π-HuB Secretariat brings you a notable proteomics advance led jointly by Professor Fuchu He’s team, titled Integrated Single-cell Proteomic and Transcriptomic Landscape of Mouse Folliculogenesis, published online on 23 July 2026 in Genomics, Proteomics & Bioinformatics.

Folliculogenesis is essential for female fertility and depends on precisely coordinated interactions between the oocyte and its surrounding granulosa cells (GCs). Although transcriptional regulation of this process has been extensively studied, the corresponding proteomic landscape has remained incompletely characterized. By profiling matched single-oocyte and mini-bulk GC samples from the same follicles across four consecutive developmental stages, this study establishes the first integrated single-cell proteomic and transcriptomic atlas of folliculogenesis. Importantly, the paired dual-omics analysis identifies developmental regulatory programs whose dynamics cannot be reliably inferred from transcript abundance alone.

Research Strategy

The team established an integrated framework combining Stage-resolved paired sampling, ultrasensitive proteomics and transcriptomics, computational reconstruction of developmental trajectories and transcription-factor regulatory networks, inference of oocyte-GC communication, and histological validation using immunohistochemistry and immunofluorescence:

1.      Stage-resolved paired sampling: Individual follicles were collected at the secondary, early antral, antral, and preovulatory stages. From each follicle, the oocyte and its surrounding GCs were isolated as a matched biological unit. After quality control, the dataset comprised 37 paired oocyte-GC samples for proteomics and 32 pairs for transcriptomics; at the preovulatory stage, GC profiling focused on cumulus cells.

2.      Ultrasensitive dual-omics profiling: A vial-based one-step protein preparation protocol minimized sample loss, followed by timsTOF SCP analysis in diaPASEF mode. Full-length transcriptomes were generated using Smart-seq2, enabling direct comparison of protein and RNA abundance within matched developmental stages and cell types.

3.      Integrative analysis of developmental trajectories and regulatory interactions: UMAP was used to visualize cell-type- and stage-dependent variation, while Mfuzz clustering and pathway-activity analysis characterized stage-associated expression patterns and pathway activities. ARACNe-VIPER was applied to infer transcription-factor regulon activity, whereas PhyloVelo and TimeTalk were used to reconstruct single cell proteome-based developmental trajectories and infer dynamic ligand-receptor communication between oocytes and GCs, respectively. Selected protein-expression patterns were further assessed in ovarian tissue by immunohistochemistry and multiplex immunofluorescence.

Core Research Results

1.      A deep, high-confidence dual-omics atlas was established. The study identified 12,542 protein groups and 45,188 transcripts across oocytes and GCs. Protein abundance spanned eight orders of magnitude, compared with four orders for transcripts. The distribution of protein abundance in oocytes was markedly uneven: only 30 proteins accounted for 25% of total protein mass, whereas 2,103 transcripts were required to reach the same fraction of total RNA abundance.

2.      Developmental changes in transcript and protein abundance showed limited concordance. Cross-gene protein-RNA correlations were moderate (Spearman's rho = 0.42 in oocytes and 0.56 in GCs), and many developmental changes were detected at only one molecular layer. Time-series analysis likewise revealed limited concordance between protein and transcript trajectories. Examples such as WTAP and NPM1 suggested maternal mRNA storage without parallel protein accumulation, whereas BOD1 and SARAF increased at the protein level without corresponding RNA changes, highlighting translational and post-translational regulation during oocyte growth.

3.      Oocytes and GCs displayed coordinated but distinct metabolic programs. From the secondary to the preovulatory stage, oocytes shifted toward triglyceride synthesis and away from fatty-acid beta-oxidation, consistent with energy storage before meiotic maturation and ovulation. In parallel, GCs increased cholesterol biosynthesis and glycolytic activity, supporting steroidogenesis and energy production. Most of these pathway changes were significant primarily at the protein level. The study also observed stable maternal-effect protein abundance despite declining transcripts, together with increased ubiquitin, RHOT1, and MGARP, linking proteostasis and mitochondrial reorganization to oocyte maturation.

4.      Integrated analysis identified candidate transcriptional regulators of folliculogenesis. Proteomic profiling detected 520 TFs, including ZBED6 and MIDEAS, for which corresponding transcripts were not detected. TF protein abundance, transcript abundance, and ARACNe-VIPER-inferred regulon activity were then jointly evaluated across developmental stages to prioritize candidate regulators in oocytes and GCs. SATB1 showed concordant increases in protein abundance, transcript abundance, and inferred regulon activity, with its increased protein expression further supported by immunohistochemistry. The inferred SATB1 regulon included Fgfr2 as a putative target, suggesting that SATB1 may contribute to preparatory chromatin remodeling before zygotic genome activation.

5.      Proteome-based trajectories revealed progressively strengthened oocyte-GC communication. PhyloVelo reconstructed developmental trajectories consistent with the known sequence of follicle maturation, while TimeTalk inferred an overall increase in bidirectional ligand-receptor activity. BMP15-BMPR1B, GDF9-BMPR2, and JAG1/JAG2-NOTCH2 interactions increased toward the preovulatory stage. The progressive co-presence of GDF9 in oocytes and BMPR2 in GCs was supported by immunofluorescence, and 62 candidate TFs, including NR2C1 and TFDP1, were linked computationally to dynamic intercellular signaling.

Study Summary

This study moves beyond a transcript-centered view of folliculogenesis by directly measuring proteins in paired oocytes and their supporting GCs across a continuous developmental sequence. It reveals a coordinated division of metabolic labor, dynamic proteostasis and mitochondrial remodeling, candidate TF regulatory networks, and progressively strengthened intercellular communication. The resulting atlas is an important reference for investigating oocyte development, follicle growth, and female reproductive biology.

Relevance for the Scientific Mission of π-HuB

This research offers several valuable insights for the implementation of π-HuB: Direct proteomic measurement at cellular resolution reveals developmental regulation that cannot be reliably inferred from transcript abundance alone; paired profiling of the oocyte and surrounding GCs preserves the ovarian follicle as a functional anatomical unit rather than analyzing dissociated cell types in isolation; proteome-based trajectory and communication analyses demonstrate how cross-sectional molecular measurements can be organized into dynamic state transitions; and the integration of open proteomic, transcriptomic, imaging, and computational resources provides a useful model for constructing comparable, mechanism-oriented reference maps. Together, the work illustrates how anatomy-aware and cell-type-resolved proteomics can connect molecular composition, developmental state, and intercellular coordination.

We warmly congratulate Professors Fuchu He, Xiaowen Wang, Yang Li, and Jianming Ying; the co-first authors Hongchao Li, Xinshuai Zhang, Huimin Kang, and Yun Yang; and all collaborators on this important achievement. Through the π-HuB Research Highlights channel, we welcome all Council Members and international partners to share frontier proteomics advances from your teams, strengthen academic exchange, and explore potential collaborative opportunities within our community.

Reference

Li H, Zhang X, Kang H, et al. Integrated single-cell proteomic and transcriptomic landscape of mouse folliculogenesis. Genomics Proteomics Bioinformatics. Published online July 23, 2026. doi:10.1093/gpbjnl/qzag068