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Multi-omics profiling reveals microbiota, metabolite, lipid, and immunological heterogeneity underlying distinct pathophysiological mechanisms of age-related endotypes in type 1 diabetes

GSE316337 Homo sapiens (human) Single-cell RNA-seq 54 samples Submitted 2026/01/16 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 54 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
54 samples: 27 × peripheral blood mononuclear cells, HC, sample vs 26 × peripheral blood mononuclear cells, T1D, sampleGroups 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 reveals microbiota, metabolite, and immunological heterogeneity of age-related endotypes in type 1 diabetesSignal transduction and targeted therapy · 2026
Summary (written by the submitters)
Type 1 diabetes (T1D) is an autoimmune disease characterized by marked heterogeneity in age at diagnosis, clinical progression, and immune pathology. Increasing evidence suggests that age-related T1D endotypes may reflect distinct underlying molecular mechanisms; however, these mechanisms remain incompletely characterized at the cellular and transcriptional levels.
Read the full summary ↓
To investigate age-associated immune heterogeneity in T1D, peripheral blood mononuclear cells (PBMCs) were collected from a selected cohort of newly diagnosed pediatric individuals with T1D and healthy controls. Single-cell RNA sequencing was performed on PBMCs from 27 individuals with T1D and 27 age- and sex-matched healthy controls to profile peripheral immune cell populations and characterize transcriptional differences associated with age-related T1D endotypes.
Published in
Multi-omics reveals microbiota, metabolite, and immunological heterogeneity of age-related endotypes in type 1 diabetes
Pan L, Tan H, Yue T et al. · Signal transduction and targeted therapy 2026 · PMID 42297781 · doi:10.1038/s41392-026-02724-2
This dataset
Open GSE316337 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 GSE316337_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 PRJNA1402332 and SRA study SRP661874. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42297781) ↗
Study design
27 × peripheral blood mononuclear cells, HC, sample vs 26 × peripheral blood mononuclear cells, T1D, sample

Supports a between-group comparison across 53 samples.

How we read this: 2 replicated groups read from 54 sample titles; they account for 53 of them. 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.