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Diseaase context dictates the cellular targets of IL-17 in inflammatory skin disease [2]

GSE341985 Mus musculus (mouse) Single-cell RNA-seq 27 samples Submitted 2026/08/07 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 27 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
Mus musculus (mouse)
Samples
27 samples: 15 × 13893-JF vs 12 × 13894-JFGroups 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)
Therapeutic blockade of IL-17 and TNF can effectively treat inflammatory skin diseases such as hidradenitis suppurativa and psoriasis, yet the relative importance of the different cell types that respond to IL-17 and TNF remains unresolved. Keratinocytes are viewed as the dominant effector cells, whereas fibroblasts have recently emerged as important contributors.
Read the full summary ↓
In mice, topical imiquimod induces IL-17- and TNF-dependent skin inflammation and is frequently used to model psoriasis. Here, we demonstrate that intradermal injection of recombinant IL-17 and TNF elicits skin inflammation with features of hidradenitis suppurativa, including a gene expression program that is distinct from psoriasis and imiquimod-induced inflammation. Single-cell transcriptomic network analysis identified dermal fibroblasts as the dominant cell communication hub in hidradenitis suppurativa and in mice injected with IL-17 and TNF. In contrast, fibroblasts and keratinocytes both show strong network involvement in psoriasis and in mice challenged with imiquimod. Cell-type-specific deletion of IL-17 receptor A in mice revealed that imiquimod-induced inflammation depends equally on IL-17 signaling in fibroblasts and keratinocytes, whereas inflammation induced by intradermal IL-17 and TNF only requires fibroblasts to recognize IL-17 and is independent of keratinocyte IL-17 sensing. Single-cell transcriptomic analysis of these conditional knockout mice further demonstrated that keratinocytes and fibroblasts activate divergent and disease-dependent transcriptional programs following activation by IL-17. Together, these findings introduce a new conceptual framework wherein IL-17 signaling is routed through distinct cellular and molecular pathways depending on disease context and establish complementary experimental systems for interrogating type 17 skin inflammation.
This dataset
Open GSE341985 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 GSE341985_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 PRJNA1504959 and SRA study SRP722860. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
15 × 13893-JF vs 12 × 13894-JF

Supports a between-group comparison across 27 samples.

How we read this: 2 replicated groups read from 27 sample titles; they account for 27 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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