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Effect of anti-CD3 antibody treatment on islet transcriptomes in the nonobese diabetic (NOD) mouse model

GSE275350 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/06/13 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 6 × IgG control NOD mouse islets vs 6 × anti-CD3 NOD mouse isletsGroups 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
Autoreactive T Cells and Cytokine Stress Drive β-Cell Senescence Entry and Accumulation in Type 1 DiabetesDiabetes · 2025
Summary (written by the submitters)
Type 1 Diabetes (T1D) is a chronic autoimmune disease resulting from T cell-mediated destruction of pancreatic beta cells. Although T cells are known to destroy beta cells, how they may impact non-lethal forms of beta cell stress that also contribute to T1D development is not known. Here, we established an intermittent treatment regimen using a validated monoclonal anti-CD3 antibody on the female nonobese diabetic (NOD) mouse model for T1D at late prediabetic stages that leads to depletion of antigen-specific T cells and improvement of glucose tolerance.
Read the full summary ↓
We used mRNA-seq to explore transcriptomic changes in isolated pancreatic islets from mice treated with anti-CD3 antibody or IgG as a control.
Published in
Autoreactive T Cells and Cytokine Stress Drive β-Cell Senescence Entry and Accumulation in Type 1 Diabetes
Pipella J, Motlagh RA, Rampazzo Morelli N et al. · Diabetes 2025 · PMID 40498653 · doi:10.2337/db24-1122
This dataset
Open GSE275350 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 GSE275350_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 PRJNA1150520 and SRA study SRP527772. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40498653) ↗
Study design
6 × IgG control NOD mouse islets vs 6 × anti-CD3 NOD mouse islets

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 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
Datasets on a similar topic

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