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Mapping Decidualization Resistance in Former Severe Preeclampsia Patients at Multi-Omic Levels

GSE265862 Homo sapiens (human) Single-cell RNA-seq 23 samples Submitted 2024/10/16 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 23 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
23 samples: 23 samples, each with a distinct title — no replicated groupsGroups 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
Multi-omics-based mapping of decidualization resistance in patients with a history of severe preeclampsiaNature medicine · 2025
Summary (written by the submitters)
Endometrial decidualization resistance (DR) is implicated in various gynaecological and obstetric conditions. Employing a multi-omic strategy, we unraveled the cellular and molecular characteristics of DR in patients that have suffered severe preeclampsia (sPE). Morphological analysis unveiled significant glandular anatomical abnormalities, confirmed histologically.
Read the full summary ↓
Single-cell RNA sequencing (scRNA-seq) of endometrial samples from sPE cases (n=11) and controls (n=12) revealed sPE-associated shifts in cell composition, manifesting as a stromal mosaic state characterized by proliferative stromal cells (MMP11, SFRP1+) alongside IGFBP1+ decidualized cells, with concurrent epithelial mosaicism and a dearth of epithelial-stromal transition associated with decidualization. Cell-cell communication network mapping underscored aberrant crosstalk among specific cell types, implicating crucial pathways such as endoglin and WNT. Spatial transcriptomics in a replication cohort validated DR-associated features. Laser capture microdissection/mass spectrometry in a second replication cohort corroborated several scRNA-seq findings, notably the absence of stromal to epithelial transition at a pathway level, indicating disrupted response to steroid hormones, particularly estrogens. These insights shed light on potential molecular mechanisms underpinning DR pathogenesis in the context of sPE.
Published in
Multi-omics-based mapping of decidualization resistance in patients with a history of severe preeclampsia
Muñoz-Blat I, Pérez-Moraga R, Castillo-Marco N et al. · Nature medicine 2025 · PMID 39775038 · doi:10.1038/s41591-024-03407-7
This dataset
Open GSE265862 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 GSE265862_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 PRJNA1104569. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39775038) ↗
Study design
23 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 23 sample titles: 23 distinct titles with little repetition. 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

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