GEO series

Endometrial Insights: Unmasking Endometriosis through Single-Cell Profiling and AI-Based Prediction

GSE266265 Homo sapiens Expression profiling by high throughput sequencing 60 samples Submitted 2026/03/20 Platform GPL24676

Summary

Endometriosis, affecting over 10% of women, presents treatment and diagnostic challenges. To address these issues, we generated the biggest single-cell atlas of endometrial tissue to date, comprising 466,371 cells from 35 endometriosis and 25 non-endometriosis patients without exogenous hormonal treatment. Detailed analysis reveals significant gene expression changes and altered receptor-ligand interactions present already 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 offer novel therapeutic targets. Using ScaiVision neural networks, we developed accurate models predicting endometriosis of varying disease severity, including a minimal 11-gene signature-based model. In conclusion, our findings illuminate numerous pathway and ligand-receptor changes in endometriosis endometrium, offering insights into pathophysiology, targets for novel treatments and accurate diagnostic models for enhanced outcomes in endometriosis management.

Published in

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

Duempelmann L, Sheppard S, McKinnon B et al. · Nature communications 2026 · PMID 42161907 · doi:10.1038/s41467-026-73020-4

This dataset

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Download

Direct links to NCBI, no account and no request form: the whole study as GSE266265_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 60 samples. Raw sequencing reads are also available from ENA.

Whole study · RAW.tar ↓ Series matrix ↓ All supplementary files ↗ Raw reads on ENA ↗

Also filed as BioProject PRJNA1106473 and SRA study SRP505041. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42161907) ↗

Study design

21 × ENDO_sample vs 19 × CTL_sample

Supports a between-group comparison across 40 samples.

2 replicated groups read from the first 40 of 60 sample titles; they account for 40 of them. Check it against the sample list below before relying on it.

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