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Global transcriptome analysis of IL-33, IL-25, IL-13, TSLP, IL-4, and IL-9 overexpression in mouse spleen

GSE313261 Mus musculus (mouse) RNA-seq (gene expression) 49 samples Submitted 2025/12/10 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 49 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
49 samples: 49 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
IL-33 expands plasma cells, disrupts germinal centers, and increases autoantibody production in miceNature communications · 2025
Summary (written by the submitters)
IL-33 is an inflammatory cytokine contributing to asthma, Chronic Obstructive Pulmonary Disease (COPD), and autoimmune diseases. Although recent studies suggest that IL-33 can induce the generation of autoantibodies, the role of IL-33 on B cell maturation and tolerance is poorly understood. Here, by inducing systemic overexpression of IL-33 in mice, we show that this cytokine induces the IL-5 and CD4 T cell-dependent accumulation of plasmablasts and plasma cells of all isotypes in the spleen, and leads to an increased antibody production.
Read the full summary ↓
IL-33 also disrupts splenic architecture and elevates autoantibody production, indicating a break in peripheral tolerance. Consistently, elevated levels of IL-33 exacerbate autoantibody production, kidney damage, and decrease survival in a mouse model of lupus. Additionally, intranasal delivery of IL-33 in mice exposed to house dust mite extract (HDM) increases autoantibodies in the lung. Notably, blocking IL-33 reduced the autoantibodies generated during HDM exposure, indicating that HDM-induced autoantibody production is IL-33 dependent. Thus, our findings implicate IL-33 in the break of peripheral B cell tolerance, opening new therapeutic avenues for the treatment of infection, COPD and autoimmune conditions.
Published in
IL-33 expands plasma cells, disrupts germinal centers, and increases autoantibody production in mice
Conde E, Asrat S, Vecchione A et al. · Nature communications 2025 · PMID 41436488 · doi:10.1038/s41467-025-67867-2
This dataset
Open GSE313261 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 GSE313261_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 PRJNA1378840 and SRA study SRP654069. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41436488) ↗
Study design
49 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 49 sample titles: 49 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

+ 9 more — browse all 49 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.