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

GSE266265 Homo sapiens (human) Single-cell RNA-seq 60 samples Submitted 2026/03/20 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 60 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
60 samples: 35 × ENDO_sample vs 25 × CTL_sampleGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Endometriosis-related alterations in the endometrium revealed by integrated single-cell and AI-powered approachesNature communications · 2026
Summary (written by the submitters)
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.
Read the full summary ↓
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
Open GSE266265 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE266265_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

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
35 × ENDO_sample vs 25 × CTL_sample

Supports a between-group comparison across 60 samples.

How we read this: 2 replicated groups read from 60 sample titles; they account for 60 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.