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Unique B and plasma cell signature differentiates hidradenitis suppurativa from psoriasis and atopic dermatitis.

GSE306437 Homo sapiens (human) RNA-seq (gene expression) 118 samples Submitted 2026/02/01 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 118 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
Homo sapiens (human)
Samples
118 samples: 114 distinct titles, 4 of them repeatedGroups 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
Unique B- and plasma cell signature differentiates hidradenitis suppurativa from psoriasis and atopic dermatitisFrontiers in immunology · 2026
Summary (written by the submitters)
Background Hidradenitis suppurativa (HS), psoriasis (PsO) and atopic dermatitis (AD) are inflammatory skin diseases which, despite sharing some immunological pathways, have unique disease pathogenesis and distinct pathology. For example, Th17 cells are central to PsO pathogenesis and have also been implicated in HS inflammation. AD, on the other hand, is classically referred to as a Th2-mediated disease, however incidence of AD is twice as likely in HS patients compared with healthy individuals, demonstrating an association between HS and AD.
Read the full summary ↓
Objective To date the transcriptomics of HS, PsO and AD have not been directly compared. Therefore this study aimed to evaluate the transcriptomic differences between HS, PsO and AD lesions to identify a biological signature that defines HS inflammation. Methods Bulk RNA sequencing was performed on 19 healthy controls, 15 HS, 21 PsO and 15 AD lesional skin samples. Results Principal component analysis (PCA) identified a distinct transcriptome in HS lesions compared with healthy control skin, with increased expression of inflammatory mediators and more active inflammatory pathways including TNF, IL-17 and B cell signalling pathways. PsO lesions were defined by a strong IL-17 signature and while an IL-17 signature existed in HS lesions, HS was distinguished from PsO lesions by a unique B cell signature. When compared to AD lesions, HS lesions had a more complex inflammatory profile involving TNF, B cell and NLR signalling while AD lesions had high expression of Th2-associated genes. Weighted gene co-expression analysis suggested T cell signalling and epidermal development are important characteristics of PsO lesions while HS lesions can be defined by complement activation, B cell signalling and fibroblast-associated pathways indicating the potential for scarring in HS. Cell type deconvolution identified an enrichment of keratinocytes and T cells in PsO, Th2 cells in AD and B cells and plasma cells in HS. Finally, gene expression analysis identified increased expression of B cell, plasma cell and immunoglobulin-associated genes in HS lesions relative to healthy control skin, PsO and AD lesions. Conclusion These data suggest that HS pathogenesis is more complex than AD or PsO and highlights an important role of B and plasma cells in HS inflammation.
Published in
Unique B- and plasma cell signature differentiates hidradenitis suppurativa from psoriasis and atopic dermatitis
Smith CM, Hambly R, Gatault S et al. · Frontiers in immunology 2026 · PMID 42079581 · doi:10.3389/fimmu.2026.1768249
This dataset
Open GSE306437 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 GSE306437_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 PRJNA1310632 and SRA study SRP612352. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42079581) ↗
Study design
114 distinct titles, 4 of them repeated
101V1L ×2 101V1PL ×2 201V1L ×2 201V1PL ×2

How we read this: Read from 118 sample titles: 114 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

+ 78 more — browse all 118 samples with per-sample file links →

Datasets on a similar topic

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