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Identification of antigen-presenting cell-T cell interactions driving immune responses to food

GSE281286 Mus musculus (mouse) RNA-seq (gene expression) 787 samples Submitted 2024/12/19 Platform GPL21626Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 787 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
787 samples: 9 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
Identification of antigen-presenting cell-T cell interactions driving immune responses to foodScience (New York, N.Y.) · 2025
Summary (written by the submitters)
The intestinal immune system must concomitantly tolerate food and commensals and protect against pathogens. Antigen-presenting cells (APCs) orchestrate these immune responses by presenting luminal antigens to CD4+ T cells and inducing their differentiation into regulatory (pTreg) or inflammatory (Th) subsets. Here, we used LIPSTIC to identify APCs that presented dietary antigens under tolerizing and inflammatory conditions.
Read the full summary ↓
We show that helminth infections disrupted tolerance proportionally to the reduction in ratio between tolerogenic, including migratory cDC1s and Rorγt+ APCs, and inflammatory APCs, represented primarily by cDC2 subsets. However, the inflammatory subset of cDC2s expanded by helminth infection did not present dietary antigens, thus avoiding diet-specific TH2 cell differentiation. Our data uncover cellular mechanisms by which tolerance to food is induced and can be disrupted by infection.
Published in
Identification of antigen-presenting cell-T cell interactions driving immune responses to food
Campos Canesso MC, de Castro TBR, Nakandakari-Higa S et al. · Science (New York, N.Y.) 2025 · PMID 39700315 · doi:10.1126/science.ado5088
This dataset
Open GSE281286 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 GSE281286_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 PRJNA1183317 and SRA study SRP545005. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39700315) ↗
Study design
9 conditions, mostly in triplicate
Smartseq2 ×376 WT_nSV_CD103P ×3 DC2KO_nSV_CD103P ×3 WT_SV_CD103P ×3 DC2KO_SV_CD103P ×3 WT_nSV_CD103N ×3 DC2KO_nSV_CD103N ×3 WT_SV_CD103N ×3 +1 more

Supports a between-group comparison across 400 samples.

How we read this: 9 replicated groups read from the first 400 of 787 sample titles; they account for 400 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

+ 747 more — browse all 787 samples with per-sample file links →

Datasets on a similar topic

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