GEO series

Nasal CD4⁺ tissue-resident memory T cells provide cross-protective immunity to influenza

GSE270932 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/02/26 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: Mus musculus (mouse)
Samples: 12 samples: 12 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: Nasal CD4+ tissue-resident memory T cells provide cross-protective immunity to influenzaThe Journal of experimental medicine · 2026

Summary (written by the submitters)

CD4 tissue-resident memory T cells (TRM) are crucial adaptive immune components involved in preventing influenza A virus (IAV) infection. Despite their importance, their physiological role in the upper respiratory tract, the first site of contact with IAV, remains unclear. Here, we find that, after IAV infection, antigen-specific CD4 TRM persist in the nasal tissue (NT) compartment after infection and provide protection upon heterosubtypic challenge.

Read the full summary ↓

Single cell RNA sequencing analysis reveals that NT CD4 TRM are heterogeneous and transcriptionally distinct as compared to their lung counterparts. Mechanistically, we demonstrate that the CXCR6-CXCL16 axis promotes CD4 TRM residency in the NT. Furthermore, we show that the NT of mice and humans contains a high frequency of Th17 CD4 TRM that aid in local viral clearance and in reducing tissue damage. Collectively, our results support a robust physiological role for nasal tissue CD4 TRM in local protection during heterosubtypic IAV infection.

Published in

Nasal CD4+ tissue-resident memory T cells provide cross-protective immunity to influenza

Mathew NR, Gailleton R, Scharf L et al. · The Journal of experimental medicine 2026 · PMID 41941276 · doi:10.1084/jem.20251793

This dataset

Open GSE270932 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 GSE270932_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 PRJNA1129062 and SRA study SRP516576. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41941276) ↗

Study design

12 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 12 sample titles: 12 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

GSM8365316 Sample 1_GEX
GSM8365317 Sample 2_GEX
GSM8365318 Sample 3_GEX
GSM8365319 Sample 4_GEX
GSM8365320 Sample 5_GEX
GSM8365321 Sample 6_GEX
GSM8365322 Sample 1_VDJ
GSM8365323 Sample 2_VDJ
GSM8365324 Sample 3_VDJ
GSM8365325 Sample 4_VDJ
GSM8365326 Sample 5_VDJ
GSM8365327 Sample 6_VDJ

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Search all mouse 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.