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Synergistic effect of IL-33 priming and ATP stimulation in bone marrow derived mast cells

GSE263663 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/03/13 Platform GPL28330
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: 3 × BMMCs, No prime, NC stimulation, vs 3 × BMMCs, IL-33 prime, NC stimulation, vs 3 × BMMCs, No prime, ATP stimulation, vs 3 × BMMCs, IL-33 prime, ATP stimulation,Groups 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
Stromal Cell-Mast Cell Communication Orchestrates Anti-Viral Immunity in the MeningesAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
Summary (written by the submitters)
Synergy of signals often causes cells to exhibit stronger physiological responses, especially immune cells. Here we reported that priming of interleukin-33 (IL-33) played a crucial role in the subsequent response of mast cells to ATP. Judging from the levels of cytokines and chemokines, the synergy of IL-33 priming and ATP stimulation was as high as 64%.
Read the full summary ↓
At the entire transcriptional level, high synergy was also observed. This provided evidence supporting the sequential release of IL-33 and ATP derived from stromal cells on mast cells in vivo. Likewise, the upregulation of numerous cytokines and chemokines provided enormous support for mast cells to regulate antiviral immunity.
Published in
Stromal Cell-Mast Cell Communication Orchestrates Anti-Viral Immunity in the Meninges
Li Q, Chen W, Sun M et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2026 · PMID 41195564 · doi:10.1002/advs.202514842
This dataset
Open GSE263663 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 GSE263663_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 PRJNA1098892 and SRA study SRP501625. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41195564) ↗
Study design
3 × BMMCs, No prime, NC stimulation, vs 3 × BMMCs, IL-33 prime, NC stimulation, vs 3 × BMMCs, No prime, ATP stimulation, vs 3 × BMMCs, IL-33 prime, ATP stimulation,

Supports a between-group comparison across 12 samples.

How we read this: 4 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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