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Macrophage–Fibroblast Signaling Network Identified by Single-Cell RNA Sequencing in Juvenile Systemic Sclerosis

GSE320020 Homo sapiens (human) Single-cell RNA-seq 11 samples Submitted 2026/04/09 Platform GPL34284Platform GPL18573
At a glance

Single-cell RNA-seq · human · 11 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
11 samples: 9 × SCGroups 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)
Juvenile systemic scleroderma (SSc) is a rare autoimmune disease characterized by excessive fibrosis and vascular involvement. Adult-onset SSc and inflammatory-driven multi-organ fibrosis are similar; interstitial lung disease was recently found in 40% of SSc patients in an international cohort. While recent single-cell studies have explored different types of fibroblasts that are involved in juvenile systemic scleroderma, less is known about the specific immune interactions with fibroblasts that maintain their activation.
Read the full summary ↓
This study aims to address that gap by mapping macrophage-fibroblast signaling using interaction analyses. The current abstract describes the single-cell RNA sequencing (scRNA-seq) profile of 9 SSc patients’ skin compared to 9 healthy subjects. ScRNA-seq was performed on the skin of 9 SSc patients and 9 healthy age-matched subjects. Samples were dissociated into single-cell suspensions and processed using the 10x Genomics® Chromium platform for single-cell RNA sequencing. Data preprocessing and clustering were performed in R using Seurat and Harmony for normalization and batch correction, followed by visualization with UMAP. Cell-to-cell communication was investigated using NicheNet and CellChat to predict ligand–receptor interactions between macrophage and fibroblast populations, including associated signaling pathways and target gene networks. Analysis of scRNA-sequenced data revealed specific fibroblast and macrophage populations driving fibrosis in systemic scleroderma (SSc). Clustering led to 11 unique groups of fibroblasts. Clusters COL11A1+/POSTN+ and STC1+/FZD9+ were markedly expanded in SSc skin and showed activation of TGFβ, WNT, and FGF pathways associated with extracellular matrix (ECM) production. The STC1+/FZD9 cluster was a novel finding, with pathway involvement in pulmonary and hepatic fibrosis signaling and TGFβ pathway activation. Macrophage subsets MMP19+/CTSL+ and MERTK+ emerged as important profibrotic sender cells, expressing ligands such as TGFB1, AREG, and COL6A2 that signal to fibroblast receptors TGFBR1, TGFBR2, FGFR1, FGFR2, and FZD9. NicheNet and CellChat analyses identified a coordinated macrophage–fibroblast signaling network integrating TGFβ, FGF, WNT, IGF, NOTCH, and TNF pathways. Notably, TGFβ signaling persisted through accessory activators (ITGAV, THBS1, and SDC2), suggesting a self-sustaining loop of fibroblast activation independent of accessory inflammatory input. Analysis of scRNA-sequenced data revealed specific fibroblast and macrophage populations driving fibrosis in systemic scleroderma (SSc). Clustering led to 11 unique groups of fibroblasts. Clusters COL11A1+/POSTN+ and STC1+/FZD9+ were markedly expanded in SSc skin and showed activation of TGFβ, WNT, and FGF pathways associated with extracellular matrix (ECM) production. The STC1+/FZD9 cluster was a novel finding, with pathway involvement in pulmonary and hepatic fibrosis signaling and TGFβ pathway activation. Macrophage subsets MMP19+/CTSL+ and MERTK+ emerged as important profibrotic sender cells, expressing ligands such as TGFB1, AREG, and COL6A2 that signal to fibroblast receptors TGFBR1, TGFBR2, FGFR1, FGFR2, and FZD9. NicheNet and CellChat analyses identified a coordinated macrophage–fibroblast signaling network integrating TGFβ, FGF, WNT, IGF, NOTCH, and TNF pathways. Notably, TGFβ signaling persisted through accessory activators (ITGAV, THBS1, and SDC2), suggesting a self-sustaining loop of fibroblast activation independent of accessory inflammatory input. These findings define a macrophage-driven signaling circuit that reinforces chronic fibrosis and identify novel molecular targets that may disrupt this feedback loop. Consistent with adult SSc, fibroblast–macrophage interactions likely sustain fibrosis in SSc, yet the specific fibroblast populations involved are different. TGFβ, FGF, and WNT pathways form an interconnected network that drives fibroblast persistence and ECM accumulation, while NOTCH and TNF signaling amplify immune–fibrotic crosstalk. Targeting key ligand–receptor interactions or accessory activators such as ITGAV, THBS1, and SDC2 may provide novel therapeutic strategies to disrupt chronic fibrotic signaling in SSc. By characterizing these ligand–receptor interactions, our study provides a detailed picture of macrophage–fibroblast communication in juvenile systemic scleroderma.
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 GSE320020_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 PRJNA1426001 and SRA study SRP678273. Searching any of these in the dataset finder brings you back here.

Study design
9 × SC

Supports a between-group comparison across 9 samples.

How we read this: 1 replicated groups read from 11 sample titles; they account for 9 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
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