GEO series

Characterisation of a pathogenic non-migratory fibroblast population in systemic sclerosis skin [scRNA-Seq]

GSE292979 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2025/04/10 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 15 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: 15 samples: 12 × Pt vs 3 × HCGroups 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: Characterization of a pathogenic nonmigratory fibroblast population in systemic sclerosis skinJCI insight · 2025

Summary (written by the submitters)

Fibroblasts are central to pathogenesis of systemic sclerosis (SSc). However, studies of conventional explant fibroblast cultures incompletely reflect disease biology and treatment response. We isolated a second non-migratory “resident” population of fibroblasts from skin biopsies after outgrowth of explant “migratory” cells. These non-motile resident fibroblasts were compared with migratory cells from the same biopsy, using functional studies, bulk and scRNAseq, and localised in situ by multichannel immunofluorescence.

Read the full summary ↓

Migratory and resident fibroblast populations in SSc showed distinct pro-fibrotic characteristics and gene expression for pathogenic pathways differing by stage and autoantibody subgroup. TGFβ signalling was highly active in migratory fibroblasts in early stage dcSSc. Conversely, resident fibroblasts had less upregulated TGFβ signalling, especially in late dcSSc. Increased chemokine expression was a hallmark of resident fibroblasts at all stages. In vitro studies confirmed differential response to TGF1 and CCL2 between migratory and resident cells. We suggest that migratory fibroblasts are especially important in early skin disease whereas non-migratory fibroblasts may have a regulatory role and contribute more to fibrosis in later stage disease. Thus, we have identified a pathogenic fibroblast population in SSc, not isolated by conventional explant culture, that could play an important role in fibrosis and be targeted therapeutically.

Published in

Characterization of a pathogenic nonmigratory fibroblast population in systemic sclerosis skin

Clark KE, Xu S, Attar M et al. · JCI insight 2025 · PMID 40232859 · doi:10.1172/jci.insight.185618

This dataset

Open GSE292979 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 GSE292979_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 PRJNA1242285 and SRA study SRP573285. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40232859) ↗

Study design

12 × Pt vs 3 × HC

Supports a between-group comparison across 15 samples.

How we read this: 2 replicated groups read from 15 sample titles; they account for 15 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

GSM8872094 Pt1 Pt
GSM8872095 Pt2 Pt
GSM8872096 Pt3 Pt
GSM8872097 HC1 HC
GSM8872098 Pt4 Pt
GSM8872099 Pt5 Pt
GSM8872100 Pt6 Pt
GSM8872101 Pt7 Pt
GSM8872102 Pt8 Pt
GSM8872103 Pt9 Pt
GSM8872104 Pt10 Pt
GSM8872105 Pt11 Pt
GSM8872106 Pt12 Pt
GSM8872107 HC2 HC
GSM8872108 HC3 HC

Datasets on a similar topic

GSE292702 Characterisation of a pathogenic non-migratory fibroblast population in systemic sclerosis skin 12 samples shares non-migratory, pathogenic, fibroblast, population, sclerosis, systemic, skin
GSE320020 Macrophage–Fibroblast Signaling Network Identified by Single-Cell RNA Sequencing in Juvenile Systemic Sclerosis 11 samples shares fibroblast, sclerosis, systemic
GSE246215 Cross-tissue human fibroblast atlas reveals myofibroblast subtypes with distinct roles in immune modulation [scRNA-seq] 38 samples shares fibroblast, scrna-seq
GSE275145 Creeping fat-derived mechanosensitive fibroblasts drive intestinal fibrosis in Crohn’s disease strictures [Human scRNA-seq] 29 samples shares fibroblast, scrna-seq
GSE309392 Cancer-associated fibroblasts promote esophageal carcinogenesis and lung metastasis by CDH11-mediated extracellular matrix environment remodeling [scRNA-Seq] 23 samples shares fibroblast, scrna-seq
GSE270894 Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [pAML Patients scRNA-seq] 20 samples shares population, scrna-seq
GSE330762 Kabuki syndrome type I fibroblast multi-omics dataset: bulk RNA-seq profiles from primary skin fibroblasts 19 samples shares fibroblast, skin
GSE301908 Single-cell spatial transcriptomic profiling defines a pathogenic inflammatory niche in chronic active multiple sclerosis lesions - snRNA-seq 17 samples shares pathogenic, sclerosis

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