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Adaptive immunity to retroelements promotes barrier integrity [single_cell_CD8]

GSE266528 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/05/01 Platform GPL30172
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: 4 × CD8 T cells, scRNA_GEX vs 4 × CD8 T cells, scRNA_HTO vs 4 × CD8 T cells, scRNA_VDJGroups 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)
The skin is the body’s largest and outermost barrier tissue. Whether environmental exposures reinforce barrier resilience remains poorly understood. Here, we show that xenobiotic-induced retroelement-specific immunity enhances tissue repair; more specifically, mild detergent exposure promotes the reactivation of defined retroelements, and retroelement-specific CD8+ responses, in a Langerhans cell-dependent manner.
Read the full summary ↓
This discrete population of retroelement-specific CD8 T cells are recruited to the skin, where they develop as tissue-resident cells and are enriched for genes associated with wound repair. Upon injury, retroelement-specific CD8+ T cells significantly accelerate wound repair via IL-17A. Collectively, this work demonstrates that tonic environmental exposures promote the accumulation of tissue-resident retroelement-specific T cells that preemptively set the tissue for maximal resilience to barrier breaches.
This dataset
Open GSE266528 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 GSE266528_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: 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 PRJNA1107514 and SRA study SRP505529. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × CD8 T cells, scRNA_GEX vs 4 × CD8 T cells, scRNA_HTO vs 4 × CD8 T cells, scRNA_VDJ

Supports a between-group comparison across 12 samples.

How we read this: 3 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
Datasets on a similar topic

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