Fibroblast Subpopulations in Systemic Sclerosis: Functional Implications of Individual Subpopulations and Correlations with Clinical Features

Honglin Zhu; Hui Luo; Brian Skaug; Tracy Tabib; Yi-Nan Li; Yongguang Tao; Alexandru-Emil Matei; Marka A Lyons; Georg Schett; Robert Lafyatis; Shervin Assassi; Jörg H W Distler

doi:10.1016/j.jid.2023.09.288

Fibroblast Subpopulations in Systemic Sclerosis: Functional Implications of Individual Subpopulations and Correlations with Clinical Features

J Invest Dermatol. 2023 Dec 24:S0022-202X(23)03206-2. doi: 10.1016/j.jid.2023.09.288. Online ahead of print.

Authors

Affiliations

¹ Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China; Department of Internal Medicine 3, Friedrich-Alexander University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China. Electronic address: honglinzhu@csu.edu.cn.
² Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
³ Division of Rheumatology, Department of Internal Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, USA.
⁴ Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
⁵ Department of Internal Medicine 3, Friedrich-Alexander University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany; Department of Rheumatology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Hiller Research Center, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
⁶ Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China; Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China.
⁷ Department of Internal Medicine 3, Friedrich-Alexander University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
⁸ Department of Internal Medicine 3, Friedrich-Alexander University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany; Department of Rheumatology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Hiller Research Center, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. Electronic address: joerg.distler@med.uni-duesseldorf.de.

PMID: 38147960
DOI: 10.1016/j.jid.2023.09.288

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.

Keywords: Dermal fibrosis; Disease outcomes; Fibroblasts; SSc; Subpopulations.

Abstract

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