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Single-cell transcriptomics reveals markers of regulatory T cell dysfunction in Gestational Diabetes Mellitus

GSE280975 Homo sapiens (human) Single-cell RNA-seq 33 samples Submitted 2026/06/10 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 33 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
33 samples: 33 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
Single-cell transcriptomics identifies regulatory T cell heterogeneity in gestational diabetes mellitusCommunications medicine · 2026
Summary (written by the submitters)
Gestational Diabetes Mellitus (GDM) is a common pregnancy complication and the role of regulatory T cells (Tregs) in this disease is ambiguous. This study aims to determine the aberrant transcriptional landscape in Tregs from GDM patients and controls. We identify gene expression programmes dysregulated in GDM Tregs and assess their utlilty as GDM biomarkers in bulk mRNA.
Published in
Single-cell transcriptomics identifies regulatory T cell heterogeneity in gestational diabetes mellitus
Mensah NE, Efthymiou A, Mureanu N et al. · Communications medicine 2026 · PMID 41933176 · doi:10.1038/s43856-026-01563-0
This dataset
Open GSE280975 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 GSE280975_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 PRJNA1181753 and SRA study SRP542951. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41933176) ↗
Study design
33 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 33 sample titles: 33 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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