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Mechanosensing by T cells promotes a tissue-resident memory transcriptional program

GSE278556 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/06/02 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 3 × WT_CD8_d0_naive vs 3 × WT_CD8_d4_media vs 3 × WT_CD8_d4_Nutragen2mgmL vs 3 × WT_CD8_d4_RatCol2mgmLGroups 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)
Cell migration and strategic positioning within tissues is critical for the rapid mobilization of a T cell response. T cells must remain motile in both lymphoid and non-lymphoid tissues, which vary widely in mechanical properties such as stiffness. Here we showed that activated T cells sensed mechanical cues and responded with changes in cell morphology, nuclear envelope composition and initiation of DNA repair to protect their genomic material from force-mediated damage.
Read the full summary ↓
Increased mechanical input also drove the transcriptional reprogramming of activated T cells, including changes in many of the core genes shared by tissue resident memory T (TRM) cells across diverse tissues, by modulating the expression of the TRM cell-associated transcription factors Klf2, Runx3 and Hic1. Thus, mechanosensing by activated T cells impacted T cell fate, promoting a transcriptional program associated with tissue-residency.
This dataset
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.

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 GSE278556_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1167774 and SRA study SRP536082. Searching any of these in the dataset finder brings you back here.

Study design
3 × WT_CD8_d0_naive vs 3 × WT_CD8_d4_media vs 3 × WT_CD8_d4_Nutragen2mgmL vs 3 × WT_CD8_d4_RatCol2mgmL

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
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