GEO series

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

GSE273594 Homo sapiens (human) Single-cell RNA-seq 8 samples Submitted 2025/09/17 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 8 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: 8 samples: 8 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.
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 GSE273594 in finder → Find more human datasets →

Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE273594_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

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. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40929272) ↗

Study design

8 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 8 sample titles: 8 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

GSM8433394 nPOD_6229_multiome_MM_510_RNA_MM_505_ATAC
GSM8433395 nPOD_6375_multiome_MM_660_RNA_MM_652_ATAC
GSM8433396 nPOD_6362_multiome_MM_661_RNA_MM_653_ATAC
GSM8433397 nPOD_6220_multiome_MM_662_RNA_MM_654_ATAC
GSM8433398 nPOD_6234_multiome_MM_664_RNA_MM_656_ATAC
GSM8433399 nPOD_6228_multiome_MM_665_RNA_MM_657_ATAC
GSM8433400 nPOD_6236_multiome_MM_666_RNA_MM_657_ATAC
GSM8433401 nPOD_6267_multiome_MM_667_RNA_MM_659_ATAC

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