GEO series

Peanut Allergy Oral Immunotherapy Drives Single-Cell Multi-omic Changes in Peanut-reactive T cells Associated with Sustained Unresponsiveness

GSE288452 Homo sapiens (human) Single-cell RNA-seq 22 samples Submitted 2025/09/25 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 22 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 22 samples: 22 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: Peanut allergy oral immunotherapy drives single-cell multi-omic changes in peanut-reactive T cells associated with sustained unresponsivenessNature immunology · 2025

Summary (written by the submitters)

Oral immunotherapy (OIT) is the only FDA-approved treatment for peanut allergy (PA). Peanut-reactive (pr) CD4+ T cells are pivotal in PA pathogenesis as well as OIT-induced desensitization (DS). However, the underlying pr CD4+ T cell immune mechanisms leading to sustained unresponsiveness (SU) after OIT discontinuation remain largely unknown.

Read the full summary ↓

We analyzed single-cell RNA and protein immunophenotypes and TCR repertoires of pr CD4+ T cells from a phase 2 peanut OIT trial cohort. We identify increased cytotoxicity-related phenotypes and Th1 CTL (cytotoxic T lymphocytes)-like cell clonal expansion during OIT in pr T effector memory cells, while Th2-related phenotypes and Th2a-like cell clonal expansion decreased. OIT participants who achieved SU had lower baseline Th2-related phenotypes, elevated cytotoxicity-related gene signatures in pr Teff cells post-OIT and higher CD39 expression in pr Tregs (T regulatory cells) after OIT withdrawal. These findings clarify OIT-induced CD4+ T cell tolerance mechanisms and can guide effective allergen-specific OIT strategies.

Published in

Peanut allergy oral immunotherapy drives single-cell multi-omic changes in peanut-reactive T cells associated with sustained unresponsiveness

Han X, Skatova V, Mikelov A et al. · Nature immunology 2025 · PMID 41188408 · doi:10.1038/s41590-025-02323-3

This dataset

Open GSE288452 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 GSE288452_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1217789 and SRA study SRP560880. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41188408) ↗

Study design

22 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 22 sample titles: 22 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

GSM8768151 sample pool C for RNAseq Abseq
GSM8768152 sample pool D for RNAseq Abseq
GSM8768153 sample pool E for RNAseq Abseq
GSM8768154 sample pool F for RNAseq Abseq
GSM8768155 sample pool G for RNAseq Abseq
GSM8768156 sample pool H for RNAseq Abseq
GSM8768157 sample pool I for RNAseq Abseq
GSM8768158 sample pool J for RNAseq Abseq
GSM8768159 sample pool K for RNAseq Abseq
GSM8768160 sample pool L for RNAseq Abseq
GSM8768161 sample pool M for RNAseq Abseq
GSM8768162 sample pool C for TCRseq
GSM8768163 sample pool D for TCRseq
GSM8768164 sample pool E for TCRseq
GSM8768165 sample pool F for TCRseq
GSM8768166 sample pool G for TCRseq
GSM8768167 sample pool H for TCRseq
GSM8768168 sample pool I for TCRseq
GSM8768169 sample pool J for TCRseq
GSM8768170 sample pool K for TCRseq
GSM8768171 sample pool L for TCRseq
GSM8768172 sample pool M for TCRseq

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