← BioTransfer GEO Dataset Finder
GEO series

Integrative Single-Cell Analysis Reveals Iron Overload-Induced Senescence and Metabolic Reprogramming in Ovarian Endometriosis-Associated Infertility

GSE276193 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2025/08/27 Platform GPL34284
At a glance

Single-cell RNA-seq · human · 18 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
18 samples: 10 × Non-endometriosis, follicle fluid, vs 8 × endometriosis, follicle fluid,Groups 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
Integrative Single-Cell Analysis Reveals Iron Overload-Induced Senescence and Metabolic Reprogramming in Ovarian Endometriosis-Associated InfertilityAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
Summary (written by the submitters)
Endometriosis, particularly ovarian endometriosis (OE), significantly impacts fertility by often leading to decreased oocyte quality and impaired ovarian function. Iron overload has been identified as a critical factor driving disease progression in endometriosis lesions. This study aims to investigate the effects of iron overload on follicular function in OE-associated infertility (OEI).
Read the full summary ↓
Using single-cell sequencing technologies, we first identified oocyte dysfunction in OEI patients and established a single-cell atlas of iron overload in follicular fluid prior to ovulation in OEI patients. This atlas revealed dynamic changes in iron metabolism and identified iron-induced senescence phenotypes. We also examined the differentiation trajectories of granulosa cells and the relationship between macrophage M1/M2 phenotypes and iron metabolism. Additionally, we utilized Stereo-seq (spatiotemporal enhanced resolution omics-sequencing) to construct a spatial transcriptomic map of iron-overloaded mouse ovaries, uncovering various enriched senescence phenotypes. Finally, we extended our findings by exploring the dynamics of iron metabolism imbalance in aging human ovaries. Overall, these findings enhance our understanding of iron overload-related cellular interactions and molecular characteristics in ovarian pathology, providing new therapeutic targets for improving oocyte quality in OEI patients.
Published in
Integrative Single-Cell Analysis Reveals Iron Overload-Induced Senescence and Metabolic Reprogramming in Ovarian Endometriosis-Associated Infertility
Li Y, Zhou W, Ding J et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2025 · PMID 40693455 · doi:10.1002/advs.202417528
This dataset
Open GSE276193 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 GSE276193_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 PRJNA1155666 and SRA study SRP530045. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40693455) ↗
Study design
10 × Non-endometriosis, follicle fluid, vs 8 × endometriosis, follicle fluid,

Supports a between-group comparison across 18 samples.

How we read this: 2 replicated groups read from 18 sample titles; they account for 18 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
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.