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Antigen-reactive CD4 T cells after SARS-CoV-2 vaccination show divergent phenotypic states with or without restimulation [separate samples]

GSE310441 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/11/19 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 16 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
16 samples: 16 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
Integrating complementary approaches reveals antigen-reactive CD4(+) T cell states after SARS-CoV-2 vaccinationiScience · 2026
Summary (written by the submitters)
Understanding T cell fate requires precise knowledge of antigen reactivity. For CD4+ T cells, conventional identification of antigen reactivity by peptide restimulation relies on cellular activation, but thereby also introduces a phenotypic bias. However, by performing single-cell RNA and TCR sequencing on both antigen-stimulated and unstimulated samples, clonotypes can be tracked across conditions to identify antigen-reactive CD4+ T cells and simultaneously assess their phenotypes in the unperturbed state.
Read the full summary ↓
Using this “reverse phenotyping” strategy, complemented by DNA-barcoded peptide–HLA class II multimers, we here investigated the longitudinal phenotypic evolution of SARS-CoV-2 spike–reactive CD4+ T cells after repeated mRNA vaccination. Without stimulation, reactive clones showed more Th-neutral features and less of an activated Th1-like state than would be assessed after antigen restimulation, yet retained a distinct transcriptional state into the memory phase. These results uncover durable imprints in antigen-reactive CD4+ T cells and highlight that cell state classification can differ fundamentally when judged by phenotype versus function.
Published in
Integrating complementary approaches reveals antigen-reactive CD4(+) T cell states after SARS-CoV-2 vaccination
Kocher K, Drost F, Schülein C et al. · iScience 2026 · PMID 42305594 · doi:10.1016/j.isci.2026.116175
This dataset
Open GSE310441 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 GSE310441_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 PRJNA1366340 and SRA study SRP646799. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42305594) ↗
Study design
16 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 16 sample titles: 16 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
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.