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

GSE298699 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2026/03/13 Platform GPL28330
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 samples: 6 conditions, mostly in triplicateGroups 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 65%.
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
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.

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 GSE298699_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1270976 and SRA study SRP589453. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in triplicate
PCMCs, WT, No prime, NC stimulation, ×3 PCMCs, WT, IL-33 prime, NC stimulation, ×3 PCMCs, WT, No prime, ATP stimulation, ×3 PCMCs, WT, IL-33 prime, ATP stimulation, ×3 PCMCs, KO, IL-33 prime, NC stimulation, ×3 PCMCs, KO, IL-33 prime, ATP stimulation, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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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.