bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.02.08.579446

Transcriptomic Signatures of WNT-Driven Pathways and Granulosa Cell-Oocyte Interactions during Primordial Follicle Activation

Takase, H. M.·Mishina, T.·Hayashi, T.·Yoshimura, M.·Kuse, M.·Nikaido, I.·Kitajima, T. S.
View original source ↗Export metadata ↓

Abstract

Primordial follicle activation (PFA) is a pivotal event in female reproductive biology, coordinating the transition from quiescent to growing follicles. This study employed comprehensive single-cell RNA sequencing to gain insights into the detailed regulatory mechanisms governing the synchronized dormancy and activation between granulosa cells (GCs) and oocytes with the progression of the PFA process. Wntless (Wls) conditional knockout (cKO) mice served as a unique model, suppressing the transition from pre-GCs to GCs, and disrupting somatic cell-derived WNT signaling in the ovary. Our data revealed immediate transcriptomic changes in GCs post-PFA in Wls cKO mice, leading to a divergent trajectory, while oocytes exhibited modest transcriptomic alterations. Subpopulation analysis identified the molecular pathways affected by WNT signaling on GC maturation, along with specific gene signatures linked to dormant and activated oocytes. Despite minimal evidence of continuous up-regulation of dormancy-related genes in oocytes, the loss of WNT signaling in (pre-)GCs impacted gene expression in oocytes even before PFA, subsequently influencing them globally. The infertility observed in Wls cKO mice was attributed to compromised GC-oocyte molecular crosstalk and the microenvironment for oocytes. Our study highlights the pivotal role of the WNT-signaling pathway and its molecular signature, emphasizing the importance of intercellular crosstalk between (pre-)GCs and oocytes in orchestrating folliculogenesis. Author summaryIn mammalian ovaries, primordial follicles protect dormant oocytes from wasteful consumption. We explored the complex molecular dynamics of the critical process of primordial follicle activation (PFA) that underlies female reproductive health, with a focus on the role of WNT signaling. By utilizing a unique mouse model with disrupted WNT signaling, our study reveals novel insights into the regulatory dynamics of granulosa cells (GCs) and oocytes during PFA. Single-cell transcriptome profiling uncovered distinct cell populations and highlighted the impact of WNT signaling on ovarian cell types. This study reveals distinct transcriptomic changes in GCs post-PFA, which affects their differentiation trajectories and highlights the critical role of WNT signaling in establishing GC function. We further examined the consequences of disrupted microenvironments on oocytes and GC-oocyte interactions, which elucidated altered oocyte subclusters and potential links to infertility. These findings provide a foundation for understanding the connections between WNT signaling and ovarian function. Our comprehensive analysis contributes valuable knowledge to reproductive biology, emphasizing the clinical relevance of WNT signaling in female fertility.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Takase, H. M., Mishina, T., Hayashi, T., Yoshimura, M., Kuse, M., Nikaido, I., Kitajima, T. S.. 2024-02-11. Transcriptomic Signatures of WNT-Driven Pathways and Granulosa Cell-Oocyte Interactions during Primordial Follicle Activation. https://doi.org/10.1101/2024.02.08.579446

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗