Endometriosis-related alterations in the endometrium revealed by integrated single-cell and AI-powered approaches

Lea Duempelmann, Sheppard S, Shaoline Sheppard, Brett McKinnon, Angelo Duo, Duo A, Jitka Skrabalova, Skrabalova J, Thomas Andrieu, Lusby R, Ryan Lusby, Wiebke Solaß, Solass W, Wiebke Solass, Goehlsdorf D, Dennis Goehlsdorf, Muzumdar S, Sukalp Muzumdar, Cinzia Donato, Bösmüller H, Hans Bösmüller, Sarah Carl, Nestorov P, Peter Nestorov, Mueller MD, Michael D. Mueller
Nature communications · 2026 · vol. 17(1) · doi:10.1038/s41467-026-73020-4 · PMID:42161907 · PMC13385355 · W7161850616
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This study generated a single-cell atlas of endometrial tissue and used AI to reveal gene expression changes, altered cell interactions, and predictive models for endometriosis.

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The study profiled endometrial tissue using integrated single-cell RNA sequencing, analyzing 466,371 cells from 35 endometriosis and 25 non-endometriosis donors not treated with exogenous hormones, and then applied AI methods to build predictive models. Compared with controls, endometriosis patients showed significant gene expression changes and altered receptor–ligand interactions across multiple cell types, with signals consistent with increased inflammation, adhesion, proliferation, cell survival, and angiogenesis. The authors trained neural network models with ScaiVision to predict endometriosis severity, reporting a median AUC of 0.83, including a model based on 11 dysregulated genes, but they explicitly note the models were not yet externally validated. This paper is centrally about endometriosis — it maps endometriosis-associated cellular and ligand-receptor alterations in the endometrium and develops AI predictors of disease severity.

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Abstract

Endometriosis, affecting 1 in 9 women, presents treatment and diagnostic challenges. To address these issues, we generated a comprehensive single-cell atlas of endometrial tissue, comprising 466,371 cells from 35 endometriosis and 25 non-endometriosis donors without exogenous hormonal treatment. Detailed analysis reveals significant gene expression changes and altered receptor-ligand interactions present in the endometrium of endometriosis patients, including increased inflammation, adhesion, proliferation, cell survival, and angiogenesis in various cell types. These alterations may enhance endometriosis lesion formation and identify potential therapeutic targets. Using ScaiVision, we trained neural network models to predict endometriosis of varying disease severity (median AUC = 0.83), including one model based solely on a set of 11 genes confirmed as dysregulated in endometriosis patients through differential expression analysis. In conclusion, our findings reveal numerous pathway and ligand-receptor changes in the endometrium of endometriosis patients, offering insights into pathophysiology, potential targets for improved treatments, and predictive models for enhanced outcomes in endometriosis management. Our models, while not yet externally validated, can serve as a tool for hypothesis generation and starting point for further clinical development.
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Abstract

Endometriosis, affecting 1 in 9 women, presents treatment and diagnostic challenges. To address these issues, we generated a comprehensive single-cell atlas of endometrial tissue, comprising 466,371 cells from 35 endometriosis and 25 non-endometriosis donors without exogenous hormonal treatment. Detailed analysis reveals significant gene expression changes and altered receptor-ligand interactions present in the endometrium of endometriosis patients, including increased inflammation, adhesion, proliferation, cell survival, and angiogenesis in various cell types. These alterations may enhance endometriosis lesion formation and identify potential therapeutic targets. Using ScaiVision, we trained neural network models to predict endometriosis of varying disease severity (median AUC = 0.83), including one model based solely on a set of 11 genes confirmed as dysregulated in endometriosis patients through differential expression analysis. In conclusion, our findings reveal numerous pathway and ligand-receptor changes in the endometrium of endometriosis patients, offering insights into pathophysiology, potential targets for improved treatments, and predictive models for enhanced outcomes in endometriosis management. Our models, while not yet externally validated, can serve as a tool for hypothesis generation and starting point for further clinical development. Similar content being viewed by others

Acknowledgements

The authors thank all participants for their valuable participation in the study and their sample donation, the physicians, nurses, and study nurses involved in donor recruitment and sample collection, and Anne Vaucher for processing biological samples. Thanks also to Philip Knuckles for his support during the initiation of the project. The authors further thank the Next Generation Sequencing (NGS) Platform (Pamela Nicholson and Daniela Steiner) for scRNA library preparation and sequencing, the Steroid Laboratory (Michael Grössl and Clarissa Vögel, Nephrology depart., Inselspital, Bern) for the progesterone measurements, the Translational Research Unit (TRU) at Institute Of Tissue Medicine and Pathology (Irene Centeno Ramos, Paulina Brönnimann, Therese Waldburger and Cristina Graham Martinez) for donor sample provision, H&E stainings and CXCL3 IHC and the Interfaculty Bioinformatics Unit (Heidi Tschanz-Lischer) for time trajectory analysis. Funding M.M. and P.N. disclose support for the research of this work from the Swiss National Science Foundation, Kommission für Technologie und Innovation (Innosuisse, Project n° 40967.1 IP-LS) and Scailyte AG. L.D., J.S., T.A. and M.M. disclose support from the Inselspital. S.S., A.D., R.L., D.G., S.M., C.D., S.C., P.N. disclose support from Scailyte AG. LD discloses additional support from Innosuisse (Project n° 40967.1 IP-LS) and a 120% Care Grant from University of Bern. Author information Authors and Affiliations Corresponding authors Ethics declarations Competing interests This study was partially funded by Scailyte AG. The authors L.D., S.S., M.D.M., P.N., C.D., S.M., A.D., D.G. and S.C. are co-inventors on patent applications related to clinical applications of the gene signatures disclosed in this paper. The authors have no other competing interests to declare. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. About this article Cite this article Duempelmann, L., Sheppard, S., McKinnon, B. et al. Endometriosis-related alterations in the endometrium revealed by integrated single-cell and AI-powered approaches. Nat Commun (2026). https://doi.org/10.1038/s41467-026-73020-4 Received: Accepted: Published: DOI: https://doi.org/10.1038/s41467-026-73020-4

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endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

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