← BioTransfer GEO Dataset Finder
GEO series

Multi-modal skin atlas associates a multicellular immune-stromal community with altered cornification and T cell expansion in atopic dermatitis [scRNA-seq]

GSE204762 Homo sapiens (human) Single-cell RNA-seq 43 samples Submitted 2025/05/13 Platform GPL18573Platform GPL19057
At a glance

Single-cell RNA-seq · human · 43 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
43 samples: 43 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.
Summary (written by the submitters)
In healthy skin, a cutaneous immune system maintains balance between tolerance towards innocuous environmental antigens and immune responses against pathological agents, but in atopic dermatitis (AD), barrier and immune dysfunction result in chronic tissue inflammation. Our understanding of the skin tissue ecosystem in AD remains incomplete, including pathological barrier formation, and cellular state and clonal composition of disease-promoting cells.
Read the full summary ↓
Here, we generated a multi-modal cell census of 310,691 cells from 86 cell subsets from whole skin tissue of 19 adult individuals, including non-lesional and lesional skin from 11 AD patients, and integrated it with 396,321 cells from 4 studies into a comprehensive atlas. Lesional skin comprised a unique immune and stromal multicellular community that included populations of MMP12+ DCs, mature migratory DCs, cycling ILCs, NK cells, inflammatory CCL19+ IL4I1+ fibroblasts, and clonally expanded IL13+IL22+IL26+ T cells with overlapping type 2 and type 17 characteristics, connected by multiple inter-cellular positive feedback loops. Reconstruction of human keratinocyte differentiation from basal to cornified layers revealed a disrupted cornification trajectory in AD associated with signals from the disease-associated immune compartment. AD GWAS gene expression was enriched in cornified keratinocytes, IL13+IL22+IL26+ T cells, and ILCs, suggesting that epithelial or immune dysfunction can initiate and then converge towards AD. Our work highlights specific, disease-associated cell subsets and interactions as potential targets in progression and resolution of chronic inflammation.
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 GSE204762_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: 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 PRJNA842220. Searching any of these in the dataset finder brings you back here.

Study design
43 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 35 samples read as cases, 7 as controls.

How we read this: Read from 43 sample titles: 43 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

+ 3 more — browse all 43 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

Share this dataset

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.