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Single-cell multiome profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression [snATAC-seq]

GSE273598 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2025/09/17 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 32 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
32 samples: 32 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 multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progressionScience advances · 2025
Summary (written by the submitters)
Genomic changes in the pancreas across stages of type 1 diabetes (T1D) progression are poorly understood. In this study, we performed single nucleus RNA-seq and ATAC-seq in whole pancreas samples obtained from non-diabetic, autoantibody-positive, and T1D organ donors. Genomic profiles from 480,154 cells were mapped to 18 pancreatic cell types and sub-types, and we identified altered abundance of beta, delta, immune, and exocrine sub-type cells in T1D progression.
Read the full summary ↓
Beta, delta, acinar, stellate, and other cell types had marked gene expression changes in both autoantibody-positive and recent-onset T1D together with key epigenomic drivers of altered cell type-specific gene activity. Combining cell type-specific changes in T1D progression with genetic association data revealed cellular pathways of T1D risk, including antigen presentation and interferon signaling in beta cells and metabolism in acinar cells. We identified changes in predicted cell signaling in the pancreas in T1D, including immune-beta signals associated with increased disease risk. Finally, we observed several ligands with altered signaling to beta cells in T1D that had protective effects on beta cell regulation in vitro. These results revealed genomic changes in the pancreas driving T1D risk and progression, observations that form the basis for future therapeutic targets for disease intervention.
Published in
Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression
Melton R, Jimenez S, Elison W et al. · Science advances 2025 · PMID 40929272 · doi:10.1126/sciadv.ady0080
This dataset
Open GSE273598 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE273598_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 PRJNA1141467 and SRA study SRP522832. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40929272) ↗
Study design
32 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 32 sample titles: 32 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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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.