GEO series

Loss of tolerance in cow milk allergic endotypes is linked to allergen-specific CD4+ T cell subset outgrowth and microbial dysbioses

GSE303756 Homo sapiens (human) RNA-seq (gene expression) 114 samples Submitted 2026/07/27 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 114 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: Homo sapiens (human)
Samples: 114 samples: 114 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.

Summary (written by the submitters)

Cow milk allergy (CMA) is the most prevalent pediatric food allergy, presenting in IgE and non-IgE mediated forms. Currently, the CD4+ T cell inflammatory responses underlying non-IgE CMA remain poorly characterized. By applying an allergen-reactive CD4+ T cell sorting and differential gene expression analysis approach, we characterized the CD4+ T cell subsets associated with both IgE and non-IgE mediated CMA endotypes and assessed their associations with gut bacterial compositions.

Read the full summary ↓

Analysis of conventional (Tcon) and regulatory (Treg) CD4+ T cells revealed an expansion of inflammatory Tcon in CMA subjects, coincident with TH-like subset polarization and alterations in Treg signaling pathways. TH2-skewing was dominant in IgE mediated allergies, whereas subjects with non-IgE mediated allergies exhibited a CD4+ T cell compartment dominated by a novel TH response. Microbial dysbiosis was observed across all CMA endotypes, and network mapping revealed significant associations between a decrease in butyrate producing bacteria and the presence of inflammatory TH-like subsets. These insights link the composition of the allergen-reactive Tcon/Treg compartment to CMA pathology and identifies differences in bacterial dysbioses associated with specific disease endotypes.

This dataset

Open GSE303756 in finder → Find more human 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 GSE303756_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 PRJNA1297627 and SRA study SRP603946. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

114 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 114 sample titles: 114 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

GSM9135281 1, ALL, CD137
GSM9135282 2, AD, CD137
GSM9135283 3, CON, CD137
GSM9135284 4, ENP, CD137
GSM9135285 5, ENP, CD154
GSM9135286 6, CON, CD137
GSM9135287 7, CON, CD154
GSM9135288 8, ALL, CD137
GSM9135289 9, ALL, CD154
GSM9135290 10, ALL, CD137
GSM9135291 11, ALL, CD154
GSM9135292 12, CON, CD137
GSM9135293 13, CON, CD154
GSM9135294 14, AD, CD137
GSM9135295 15, AD, CD154
GSM9135296 16, ALL, CD137
GSM9135297 17, ALL, CD154
GSM9135298 18, AD, CD137
GSM9135299 19, AD, CD154
GSM9135300 20, ENP, CD137
GSM9135301 21, ENP, CD154
GSM9135302 22, CON, CD137
GSM9135303 23, CON, CD154
GSM9135304 24, AD, CD137
GSM9135305 25, AD, CD154
GSM9135306 26, ALL, CD137
GSM9135307 27, ALL, CD154
GSM9135308 28, ENP, CD137
GSM9135309 29, ENP, CD154
GSM9135310 30, ENP, CD137
GSM9135311 31, ENP, CD154
GSM9135312 32, AD, CD137
GSM9135313 33, AD, CD154
GSM9135314 34, AD, CD137
GSM9135315 35, AD, CD154
GSM9135316 36, ENP, CD137
GSM9135317 37, ENP, CD154
GSM9135318 38, ENP, CD137
GSM9135319 39, ENP, CD154
GSM9135320 40, ALL, CD137

+ 74 more — browse all 114 samples with per-sample file links →

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Search all human 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.