Biology subjects

Moullet, M.

Publications and source records attributed to Moullet, M..

7 recordsLinked to original sources

bioRxiv2026

An integrated multimodal pan-organ atlas of the female reproductive system across the lifespan contextualises gynaecological pathologies

Single cell transcriptomics has transformed our knowledge of reproductive tissues, yet studies remain largely organ-specific and temporally limited, leaving an incomplete picture of how cell types are distributed across the reproductive system over a lifetime. Gynaecological conditions affect more than one in four females and frequently span multiple organs and life stages. To advance our understanding and treatment of these conditions, an integrated cellular reference is essential. Here we present the Human Female Reproductive System Cell Atlas v1: a single-cell transcriptomic resource integrating more than 2M cells across the ovary, fallopian tube, uterus, cervix and vagina over the lifespan and menstrual cycle, further integrated with spatial transcriptomics and chromatin accessibility profiling to define 210 cell types through community-based annotation. Cross-organ integration resolves shared and organ-specific cellular states, identifying uterine-specific perivascular populations lining uterine spiral arteries, hypoxia-sensing type 3 innate lymphoid cells (ILC3s) enriched in the uterus, and lipid-associated macrophages with distinct subsets in each reproductive organ, including a previously undescribed population shared between the uterus and fallopian tube. Cross-organ integration enables detection of ectopic epithelial populations in otherwise healthy donors, including endometrial-like cells within a paediatric ovary consistent with early endometriosis. Integration with genome-wide association studies (GWAS) reveals that risk variants for major gynecological conditions act in mesenchymal cell states defined by specific transcriptional programmes and spatial or temporal context - for instance, heavy menstrual bleeding risk is enriched in basal fibroblasts (SFRP5) of the regenerative endometrial compartment. An integrated chromatin accessibility atlas provides peak-to-gene maps across reproductive cell types, enabling nomination of disease effector genes and providing the first regulatory evidence linking a Polyendocrine Metabolic Ovarian Syndrome (PMOS) risk locus to INHBB in granulosa cells. Together, this resource establishes a cellular and molecular framework for reproductive biology and the pathogenesis of neglected gynaecological conditions.

Cell Biology↗

bioRxiv2026

Full-thickness spatial transcriptomics of the human uterus reveals basalis niche architecture and regeneration gradients during menstrual breakdown

The human endometrium (uterine lining) undergoes cyclical breakdown and scarless regeneration during each menstrual cycle, representing an exceptional model of adult tissue renewal. Regeneration is driven primarily by progenitor cells retained within the deep, basalis compartment during menstruation, yet the full-depth spatiotemporal dynamics of this process have remained understudied due to anatomical and technical limitations. Here, we map spatial gene-expression gradients across the full thickness of the human endometrium, from the myometrial-endometrial boundary to the luminal surface, using high-resolution spatial transcriptomics integrated with single-cell transcriptomics. We profile more than ten million cells from biopsies, hysterectomy samples and menstrual fluid, enriching for the menstrual and proliferative phases, which are underrepresented in previous studies. We show that endometrial breakdown, regeneration and rapid luminal re-epithelialisation are concurrent rather than temporally separated, organised across distinct tissue compartments, revealing a mode of tissue renewal in which shedding and repair operate simultaneously. Continuous basalis-to-luminal transcriptional gradients link progenitor identity, niche signalling, and tissue remodelling, defining a coordinated regenerative axis spanning the full tissue depth. We resolve the basalis epithelial niche at unprecedented molecular resolution, identifying for the first time a discrete, predominantly quiescent progenitor-like epithelial subset and specialised supporting SFRP5+ fibroblasts, both characterised by WNT inhibition, alongside lymphoid aggregates, forming a multi-component architecture that persists after menopause, consistent with a long-lived regenerative reservoir. Together, these findings establish spatial transcriptional gradients as a central organising principle of endometrial renewal, providing a molecular framework for understanding disorders of menstruation, implantation failure, and impaired tissue repair.

cell biology↗

bioRxiv2026

Self-supervised learning for a gene program-centric view of cell states

Single-cell omics has extended the biological interrogation of cell state from examining the expression of individual genes to unbiased profiling of tens of thousands of genes at once. However, extracting biological insights from such high-dimensional data remains challenging. To enable downstream analyses, many computational approaches compress cell state into a single latent representation. This can obscure the structure of underlying gene programs (GP), defined as coordinated sets of biologically related genes, such as signalling pathway response modules or transcription factor targets. Here, we present Tripso, a self-supervised transformer deep learning model which learns multiple GP-specific embeddings from predefined GPs, while also enabling the discovery of novel, data-driven GPs. Tripso facilitates principled comparisons across development, disease, and experimental systems. Firstly, in a dataset of human hematopoietic cells spanning prenatal development through adulthood and aging and including newly generated data, Tripso resolved age-specific GP patterns, including elevated JAK-STAT activity in pediatric hematopoietic cells and postnatal shifts in IKZF1 GP activity during B cell differentiation. Secondly, leveraging Tripso GP embeddings and comparing in vivo to in vitro data, we hypothesized and experimentally validated that inhibition of the SEC61 translocon improved maintenance of hematopoietic stem cells in culture. Finally, Tripsos capacity for data-driven GP discovery revealed a previously uncharacterized tissue-resident memory T cell GP with increased activity in atopic dermatitis. Its spatial co-localization with sebaceous gland-associated immune niches was demonstrated in spatial transcriptomic and proteomic data. Thus, by moving beyond single embeddings of cellular states, Tripso enables interpretable and actionable discoveries, demonstrating how GP-centric modelling can generate hypotheses with substantial biomedical relevance. By anchoring cellular representations in meaningful GPs, Tripso establishes a principled and biologically grounded framework towards the development of interpretable virtual cell models.

bioinformatics↗

bioRxiv2026

Predicting how perturbations reshape cellular trajectories with PerturbGen

A major challenge in biology is predicting how cells transition between states over time and how perturbations disrupt these transitions. Understanding such dynamics is critical for identifying interventions that reverse pathological programs or reprogram cells toward desired states. Although recent computational approaches can predict single-cell perturbation responses in silico, they cannot predict responses across dynamic cell trajectories, for example how early perturbations reconfigure later cell states. To address this gap, we introduce PerturbGen, a generative foundation model trained on over 100 million single-cell transcriptomes that predicts perturbation responses along cellular trajectories. PerturbGen predicts how genetic perturbation at source state shapes downstream states, alters gene programs and trajectories across time, for example in differentiation or disease progression. We apply PerturbGen to three newly generated multi-condition human single-cell datasets spanning immune responses, hematopoiesis and skin development. In an in vivo immune challenge, PerturbGen predicts that knocking out an IL1B signal in myeloid cells attenuates later cytokine-interferon programs, with downstream changes consistent with a reversal of IL-1{beta} stimulation signature. In hematopoiesis, anchoring perturbation-induced programs to human genetics enables simulation of monogenic blood disorders, recapitulates established disease-associated biology whilst systematically revealing lineage-specific programs, including in lineages where this was not previously possible. In skin organoids, PerturbGen predicted that Wnt activation enhances stromal differentiation recapitulating the trajectory observed in human prenatal skin, findings that were functionally validated by experimentally activating Wnt signaling. Together, PerturbGen extends modeling of gene perturbations from static to dynamic cellular systems. We envision PerturbGen enabling the creation of in silico, trajectory-aware perturbation atlases and virtual cells across diverse biological scenarios, supporting optimization of disease models and prioritization of candidate molecular interventions for therapeutic discovery.

bioinformatics↗

bioRxiv2025

Decoding human B cell ontogeny in prenatal and adult bone marrow and in vitro models via single-cell multiomics

In humans, the bone marrow becomes the primary site for B lymphopoiesis during the second trimester of pregnancy and continues throughout life. Prenatal and adult B cell progenitors play distinct roles in the aetiology and pathology of paediatric and adult hematopoietic malignancies, though the molecular drivers of these differences remain unclear. Here, we created a comprehensive multiomics atlas of over 500k cells covering immune and stromal compartment from prenatal and adult bone marrow, enabling high-resolution analysis of the cell-intrinsic and cell-extrinsic processes that modulate prenatal and adult B lymphopoiesis. Even though B cells follow broadly similar developmental trajectories, we identify a novel postnatal lateCLP subset equivalent to prenatal preProB cells, and uncover prenatal cells carry several signatures characteristic of leukemias, including enhanced proliferation, higher RAG1/RAG2 activity, extrinsic B cell signals such as IL7, and lower retention signals in the bone marrow. We also developed and characterised, at both cellular and molecular levels, a new experimental framework for generating B cell precursors from human induced pluripotent stem cells (hiPSCs), and show that it faithfully recapitulates key stages of B cell differentiation. Together, our single-cell multiomics atlas of B lymphopoiesis in vivo and in vitro offers detailed insights into the unique molecular features of prenatal and adult B cell lymphopoiesis, and serves as a powerful resource for investigating the early events that contribute to haematological disorders.

immunology↗

bioRxiv2025

Atlas-scale metabolic activities inferred from single-cell and spatial transcriptomics

Metabolism supplies energy, building blocks, and signaling molecules vital for cell function and communication, but methods to directly measure it at single-cell and/or spatial resolutions remain technically challenging and inaccessible for most researchers. Single-cell and spatial transcriptomics offer high-throughput data alternatives with a rich ecosystem of computational tools. Here, we present scCellFie, a computational framework to infer metabolic activities from human and mouse transcriptomic data at single-cell and spatial resolution. Applied to [~]30 million cell profiles, we generated a comprehensive metabolic atlas across human organs, identifying organ- and cell-type-specific activities. In the endometrium, scCellFie reveals metabolic programs contributing to healthy tissue remodeling during the menstrual cycle, with temporal patterns replicated in data from in vitro cultures. We also uncover disease-associated metabolic alterations in endometriosis and endometrial carcinoma, linked to proinflammatory macrophages, and metabolite-mediated epithelial cell communication, respectively. Ultimately, scCellFie provides a scalable toolbox for extracting interpretable metabolic functionalities from transcriptomic data.

bioinformatics↗

bioRxiv2023

An integrated single-cell reference atlas of the human endometrium

The human endometrium, the inner lining of the uterus, exhibits complex, dynamic changes throughout the menstrual cycle in response to ovarian hormones. Aberrant response of endometrial cells to hormones is associated with multiple disorders, including endometriosis. Previous single-cell studies of the endometrium profiled a limited number of donors and lacked consensus in defining cell types and states. Here, we introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas, combining published and newly generated single-cell transcriptomics datasets of endometrial biopsies of women with and without endometriosis. The HECA assigned consensus cell types and states, and uncovered novel ones, which we mapped in situ using spatial transcriptomics. We quantified how coordinated interactions between cell states in space and time contribute to endometrial regeneration and differentiation. In the continuously changing functionalis layer, we identified an intricate coordination of TGF{beta} signalling between stromal and epithelial cells, likely crucial for cell differentiation. In the basalis layer, we defined signalling between fibroblasts and a new epithelial cell population expressing epithelial stem/progenitor markers, suggesting their role in endometrial regeneration. Additionally, integrating the HECA single-cell data with genome-wide association study data and comparing endometrial samples from women with and without endometriosis, we pinpointed subsets of decidualised stromal cells and macrophages as the most dysregulated cell states in endometriosis. Overall, the HECA is an invaluable resource for studying endometrial physiology, investigating endometrial disorders, and guiding the creation of endometrial microphysiological in vitro systems.

genomics↗