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CD69 regulates the tissue dynamics of epigenetically imprinted memory CD4+ T cells [RNA-seq_ATAC-seq]

GSE325227 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 22 samples Submitted 2026/03/23 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 22 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
22 samples: 8 conditions, mostly in duplicateGroups 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)
Tissue-resident memory T (TRM) cells reside in non-lymphoid tissues and provide the first line of defense against pathogens. A subset of TRM cells can egress from non-lymphoid tissues into the circulation. However, the functional consequences and the extent of epigenetic imprinting in recirculating TRM cells remain unknown. We herein demonstrate that in CD4+ TRM cells, the CD69-S1PR1 axis controls tissue residency, and that interrupting this axis results in ablation of lung CD4+ TRM cells.
Read the full summary ↓
A subpopulation of CD69+CD4+ TRM cells re-entered circulation via lymphatic vessels, where they epigenetically maintained the characteristics of TRM cells in both mice and humans. Circulating Ex-lung-TRM cells in mice caused enhanced skin inflammation compared to circulating memory cells. Furthermore, we identified GPR183 and CD161 as potential markers of Ex-TRM in human PBMC. In chronic inflammatory diseases, the transposition of allergic inflammation to multiple tissues may therefore occur via recirculation of tissue-imprinted memory CD4+ T cells.
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 GSE325227_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1439229 and SRA study SRP684773. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in duplicate
ATAC_OTII_CirculatingExTcirc ×2 ATAC_OTII_TissueExTcirc ×2 ATAC_OTII_CirculatingExTRM ×2 TRM ×2 TissueExTcirc ×2 CirculatingExTcirc ×2 TissueExTRM ×2 CirculatingExTRM ×2 +6 more

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 22 sample titles; they account for 16 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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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.