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Islet-Intrinsic Sex Differences in Inflammatory Signaling Contribute to Autoimmune Diabetes Susceptibility

GSE310965 Homo sapiens (human) Single-cell RNA-seq 60 samples Submitted 2026/05/17 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 60 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
60 samples: 60 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.
Summary (written by the submitters)
Whereas most autoimmune diseases exhibit female predominance, type 1 diabetes (T1D) occurs more frequently in males after puberty, suggesting a role for sex hormones in disease modification. Because islet β cells actively shape local immune responses, we hypothesized that sex-specific islet responses to inflammation contribute to this disparity.
Read the full summary ↓
Using transcriptomic and proteomic analyses of human islets from male and female donors, we found that male islets exhibited a more aggressive response to proinflammatory cytokines, characterized by greater induction of interferon signaling and suppression of developmental signaling compared to female islets. Treatment of human islets and mouse β cells with the sex hormone 17β-estradiol (E2) suppressed inflammatory signaling and markers of β-cell maturity while enhancing developmental gene programs. Complementary studies in non-obese diabetic (NOD) mice showed that E2 treatment reduced diabetes incidence and limited progression to severe insulitis. Islet single-cell RNA sequencing revealed that E2 treatment of NOD mice suppressed interferon signaling, chemokine production, and antigen presentation in β cells, while reducing activation and cytotoxicity pathways in immune cells. In an aggressive adoptive transfer model, E2 pretreatment of the recipient mice attenuated hyperglycemia. These findings support a model in which estradiol-mediated β-cell reprogramming reduces β-cell immunogenicity and promotes local immune tolerance, offering mechanistic insight into sex-biased T1D susceptibility.
This dataset
Open GSE310965 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 GSE310965_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 PRJNA1367637 and SRA study SRP648334. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
60 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 60 sample titles: 60 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

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.