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Cutaneous T cells promote distinct transcriptional programs in human skin structural cells associated with inflammatory disease

GSE272623 Homo sapiens (human) Single-cell RNA-seq 135 samples Submitted 2025/04/15 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 135 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
135 samples: 20 conditions, mostly with about 7 replicates eachGroups 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
T cells promote distinct transcriptional programs of cutaneous inflammatory disease in keratinocytes and dermal fibroblastsbioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
T cells coordinate with structural cells in the skin to promote appropriate inflammatory responses and subsequent restoration of barrier integrity following insult. Single cell studies of human skin have defined an assortment of transcriptionally distinct structural cell populations in healthy tissue and identified inflammatory disease-associated changes in epithelial keratinocytes and dermal fibroblasts.
Read the full summary ↓
Cutaneous T lymphocyte activity is implicated in the development of inflammatory skin disease, but the mechanisms by which T cells promote disease-associated changes in the skin remain unclear. We show that distinct subsets of circulating and resident CD4+ cutaneous T cells promote a diverse array of transcriptional outcomes in human keratinocytes and fibroblasts. Using these in vitro generated transcriptional signatures, we identify T cell-dependent gene modules associated with inflammatory skin diseases in vivo, such as a set of Thelper17 cell-induced genes in keratinocytes that are enriched in the skin of patients with psoriasis and normalized in response to anti-IL-17 therapy. Thus, we present an approach to define the immune-dependent gene networks of skin structural cells, which we use to study the T cell contribution to inflammatory skin disease and response to anti-cytokine therapy.
Published in
T cells promote distinct transcriptional programs of cutaneous inflammatory disease in keratinocytes and dermal fibroblasts
DeBerg HA, Fahning ML, Varkhande SR et al. · bioRxiv : the preprint server for biology 2024 · PMID 39131334 · doi:10.1101/2024.07.31.606077
This dataset
Open GSE272623 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 GSE272623_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 PRJNA1137851 and SRA study SRP520962. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39131334) ↗
Study design
20 conditions, mostly with about 7 replicates each
Fib_CD4+CLA+CD103+_PAM ×13 Ker_CD4+CLA+CD103+_PAM ×11 Ker_CD4+CLA+Th1_PAM ×7 Ker_CD4+CLA+Th17_PAM ×7 Ker_CD4+CLA+Th22_PAM ×7 Ker_Control ×7 Fib_CD4+CLA+Th1_PAM ×7 Fib_CD4+CLA+Th17_PAM ×7 +12 more

Supports a between-group comparison across 135 samples.

How we read this: 20 replicated groups read from 135 sample titles; they account for 135 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

+ 95 more — browse all 135 samples with per-sample file links →

Datasets on a similar topic

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