Mots-Clés
single-cell transcriptomics
spatial transcriptomics
gene regulatory network inference
Description
Reconstruction of Gene Regulatory Networks and Cellular Interaction Networks to Predict Germ Cell Trajectories within the Ovarian Follicle
Ovarian follicles are complex multicellular structures that house mammalian female germ cells. In each ovarian cycle, a cohort of follicles grows; only a limited number reach ovulation, while the majority degenerate through a process known as atresia. The molecular and cellular mechanisms dictating these divergent trajectories remain largely unknown [1].
In mice, the ovarian follicle displays notable variations in size and cellular dynamics, with somatic cell populations ranging from a few hundred (secondary stage) to hundreds of thousands (ovulatory stage), and diameters increasing from ∼150 μm to ∼500 μm. Two main fates are observed: selection for ovulation or atresia. These can be distinguished by different cellular states, which are often identified only at late stages by morphological cues (size, cell number, nuclear pyknosis).
Recent advances in single-cell and spatial omics approaches have enabled fine molecular characterization of the follicle at different stages, revealing intermediate states and differentiation trajectories. Nonetheless, early identification of dominant follicles remains impossible, and the mechanisms regulating somatic cell populations are still unclear.
This internship aims to reconstruct gene regulatory networks (GRN) and cellular interaction networks (ligand-receptor networks, LRN) explaining the different trajectories of ovarian follicles, using scRNA-seq and spatial transcriptomic data from four recent studies [2, 3, 4, 5]. The molecular states of ovarian follicles will be analyzed at multiple stages of ovulatory or atretic trajectories, enabling the inference of bifurcation points and early predictive markers.
Methodology and Approaches
Highly motivated students are required, with skills in mathematical modelling, scientific computing, statistics and/or bioinformatic, to successively address the following methodologies:
• Review, collection and integration of published datasets (single-cell atlas, spatial transcriptomics, temporal trajectories) [2, 3, 4, 5].
• Preprocessing and fine annotation of cell states (marker identification, cell classification) using tools from the scverse ecosystem (Python).
• Inference of gene regulatory networks (GRN) using generative models [6], leveraging expression variability and transcription dynamics.
• Inference of ligand–receptor interaction networks (LRN) from spatial and gene expression profiles [7].
• Trajectory analysis [8] and identification of key signals for follicle selection.
• Proposal of early predictive markers for the follicular trajectory.
[1] Findlay, J. K.; Dunning, K. R.; Gilchrist, R. B.; Hutt, K. J.; Russell, D. L.; Walters, K. A. Chapter 1 - Follicle Selection in Mammalian Ovaries. In The Ovary (Third Edition); Leung, P. C. K., Adashi, E. Y., Eds.; Academic Press, 2019; pp 3–21.
https://doi.org/10.1016/B978-0-12-813209-8.00001-7.
[2] Morris, M. E.; Meinsohn, M.-C.; Chauvin, M.; et al., A Single-Cell Atlas of the Cycling Murine Ovary, eLife, 2022, 11, e77239. https://doi.org/10.7554/eLife.77239
[3] Mantri, M.; Zhang, H. H.; Spanos, E.; et al., A Spatiotemporal Molecular Atlas of the Ovulating Mouse Ovary, PNAS, 2024, 121(5), e2317418121. https://doi.org/10.1073/pnas.2317418121
[4] Huang, R.; Kratka, C. E.; Pea, J.; et al., Single-Cell and Spatiotemporal Profile of Ovulation in the Mouse Ovary, PLOS Biology, 23(6):e3003193 2025. https://doi.org/10.1371/journal.pbio.3003193
[5] Zhao, Z.-H.; Meng, T.-G.; Gao, F.; et al., Spatiotemporal and Single-Cell Atlases to Dissect Cell Lineage Differentiation and Regional Specific Cell Types in Mouse Ovary Morphogenesis, Communications Biology, 8(1) 2025.
https://doi.org/10.1038/s42003-025-08277-4
[6] Ventre, E.; Herbach, U.; Espinasse, T.; Benoit, G.; Gandrillon, O. One Model Fits All: Combining Inference and Simulation of Gene Regulatory Networks. PLOS Comput. Biol. 2023, 19 (3), e1010962. https://doi.org/10.1371/journal.pcbi.1010962.
[7] Kyaw, W.; Chai, R. C.; Khoo, W. H.; Goldstein, L. D.; Croucher, P. I.; Murray, J. M.; Phan, T. G. ENTRAIN: Integrating Trajectory Inference and Gene Regulatory Networks with Spatial Data to Co-Localize the Receptor-Ligand Interactions That Specify Cell Fate. Bioinforma. Oxf. Engl. 2023, 39 (12), btad765. https://doi.org/10.1093/bioinformatics/btad765.
[8] Huizing, G.-J.; Samaran, J.; Capocefalo, D.; Audit, A.; Peyré, G.; Cantini, L.; STORIES: learning cell fate landscapes from spatial transcriptomics, bioRxiv 2025 https://www.biorxiv.org/content/10.1101/2024.07.26.605241v2.