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RIPK1 regulates beta-cell fate and autoimmunity in type 1 diabetes

GSE311066 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/11/24 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 24 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
24 samples: 6 × Control NIT-1 beta-cells Vehicle vs 6 × Control NIT-1 beta-cells TNF_IFN vs 6 × Ripk1KO NIT-1 beta-cells Vehicle vs 6 × Ripk1KO NIT-1 beta-cells TNF_IFNGroups 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
RIPK1 regulates β-cell fate via actions on gene expression and kinase signaling in a mouse model of β-cell self-reactivityCell death & disease · 2026
Summary (written by the submitters)
Type 1 diabetes (T1D) results from autoimmune destruction of pancreatic beta-cells. RIPK1, a key regulator of cell death and inflammation, has been implicated in beta-cell responses to cytokines and immune attack. To investigate RIPK1's role in beta-cell fate during autoimmune stress, we generated CRISPR/Cas9-mediated Ripk1-deleted NIT-1 beta-cells and exposed them to TNFα+IFNγ or self-reactive diabetic NOD splenocytes.
Read the full summary ↓
Using RNA-seq, kinome profiling, flow cytometry, and small molecule inhibitors, we demonstrate that RIPK1 deletion or pharmacological inhibition protects beta-cells from cytokine-induced and autoimmune cell death. This study provides insights into RIPK1-dependent pathways in beta-cell vulnerability and suggests potential therapeutic strategies for T1D.
Published in
RIPK1 regulates β-cell fate via actions on gene expression and kinase signaling in a mouse model of β-cell self-reactivity
Contreras CJ, Mukherjee N, Harris-Kawano A et al. · Cell death & disease 2026 · PMID 41680134 · doi:10.1038/s41419-026-08471-0
This dataset
Open GSE311066 in finder → Find more mouse 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 GSE311066_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1368636 and SRA study SRP648050. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41680134) ↗
Study design
6 × Control NIT-1 beta-cells Vehicle vs 6 × Control NIT-1 beta-cells TNF_IFN vs 6 × Ripk1KO NIT-1 beta-cells Vehicle vs 6 × Ripk1KO NIT-1 beta-cells TNF_IFN

Supports a between-group comparison across 24 samples.

How we read this: 4 replicated groups read from 24 sample titles; they account for 24 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
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