Abstract
Fibroblasts constitute a heterogeneous population of cells. In this study, we integrated single-cell RNA-sequencing and bulk RNA-sequencing data as well as clinical information to study the role of individual fibroblast populations in systemic sclerosis (SSc). SSc skin demonstrated an increased abundance of COMP+, COL11A1+, MYOC+, CCL19+, SFRP4/SFRP2+, and PRSS23/SFRP2+ fibroblasts signatures and decreased proportions of CXCL12+ and PI16+ fibroblast signatures in the Prospective Registry of Early Systemic Sclerosis and Genetics versus Environment in Scleroderma Outcome Study cohorts. Numerical differences were confirmed by multicolor immunofluorescence for selected fibroblast populations. COMP+, COL11A1+, SFRP4/SFRP2+, PRSS23/SFRP2+, and PI16+ fibroblasts were similarly altered between normal wound healing and patients with SSc. The proportions of profibrotic COMP+, COL11A1+, SFRP4/SFRP2+, and PRSS23/SFRP2+ and proinflammatory CCL19+ fibroblast signatures were positively correlated with clinical and histopathological parameters of skin fibrosis, whereas signatures of CXCL12+ and PI16+ fibroblasts were inversely correlated. Incorporating the proportions of COMP+, COL11A1+, SFRP4/SFRP2+, and PRSS23/SFRP2+ fibroblast signatures into machine learning models improved the classification of patients with SSc into those with progressive versus stable skin fibrosis. In summary, the profound imbalance of fibroblast subpopulations in SSc may drive the progression of skin fibrosis. Specific targeting of disease-relevant fibroblast populations may offer opportunities for the treatment of SSc and other fibrotic diseases.