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Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signals [single cell]

GSE335464 Homo sapiens (human) Single-cell RNA-seq 28 samples Submitted 2026/06/16 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 28 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
28 samples: 28 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
Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signalsCell reports · 2025
Summary (written by the submitters)
Genome-wide association studies (GWAS) have identified over 100 signals associated with type 1 diabetes (T1D). However, it has been challenging to translate any given T1D GWAS signal into mechanistic insights, such as causal variants, their target genes, and the specific cell types involved. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in human pancreatic islets under baseline and T1D-stimulating conditions.
Read the full summary ↓
We nominate effector cell types for all T1D GWAS signals and the regulatory elements and genes for three independent T1D signals acting through beta cells at the DLK1/MEG3, RASGRP1, and TOX loci. We validated the functional impact of these genes and regulatory regions using isogenic human embryonic stem cells (hESCs). Loss of RASGRP1 or DLK1, as well as disruption of their corresponding regulatory regions, led to increased beta cell apoptosis, and beta cells derived from isogenic hESCs carrying the T1D risk allele of rs3783355 showed elevated beta cell death. Through additional RNA-seq and ATAC-seq analyses, we identified five genes upregulated in both RASGRP1-/- and DLK1-/- beta-like cells, four of which are near T1D GWAS signals.
Published in
Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signals
D'Oliveira Albanus R, Zhang X, Zhao Z et al. · Cell reports 2025 · PMID 40737125 · doi:10.1016/j.celrep.2025.116065
This dataset
Open GSE335464 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 GSE335464_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 PRJNA1478579 and SRA study SRP709717. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40737125) ↗
Study design
28 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 28 sample titles: 28 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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