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Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis

GSE328048 Homo sapiens (human) Single-cell RNA-seq 116 samples Submitted 2026/06/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 116 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
116 samples: 34 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)
Atopic dermatitis (AD) is a common chronic inflammatory skin disease with complex, poorly understood pathogenesis due to its mechanistic heterogeneity. To investigate underlying mechanisms, we performed single-cell RNA sequencing (scRNA-seq) on lesional (LAD) and non-lesional (NAD) skin from 42 AD patients and skin from 23 healthy controls (HC).
Read the full summary ↓
Keratinocytes (KCs) were the most abundant cell type identified. In healthy skin, KC differentiation followed a linear trajectory from basal KCs (BKs) through seven differentiated stages (DK1–DK7) to terminal keratinized cells (KK1 and KK2). In LAD, this process was disrupted, showing a reversed transition from KK2 to KK1, mainly driven by DK7. APOD and LYZ were identified as LAD-specific regulators of this aberrant keratinization, linked to IL-13- and IL-22-driven responses involving endoplasmic reticulum (ER) stress and oxidative damage. Mitochondrial and ER dysfunction were specifically enriched in LAD DK6 cells, suggesting this subcluster as a key pathogenic compartment associated with cytokine activation. Further, cell-cell interaction analysis (validated through Xenium spatial transcriptomics and immunohistochemistry) highlighted TWEAK, derived from IL13+ Th2 and cycling T cells acting on FN14+ basal and differentiated KCs, as a key contributor to AD-associated epidermal responses. These findings reveal how disrupted KC differentiation and specific immune pathways contribute to AD pathogenesis, identifying distinct cellular compartments and inflammatory circuits as potential therapeutic targets.
This dataset
Open GSE328048 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 GSE328048_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 PRJNA1453562 and SRA study SRP691918. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
34 conditions, mostly in duplicate
J ×13 6138-AB ×4 7461-JF ×4 6726-JF ×3 6788-JF ×3 7063-JF ×3 7115-JF ×3 7450-JF ×3 +52 more

Supports a between-group comparison across 90 samples.

How we read this: 34 replicated groups read from 116 sample titles; they account for 90 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

+ 76 more — browse all 116 samples with per-sample file links →

Datasets on a similar topic

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