{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T13:17:34Z","timestamp":1778505454788,"version":"3.51.4"},"reference-count":39,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["P50-AR-060780"],"award-info":[{"award-number":["P50-AR-060780"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis &amp; Rheumatology"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Systemic sclerosis (SSc) is a multifactorial autoimmune fibrotic disorder involving complex rewiring of cell\u2010intrinsic and cell\u2010extrinsic signaling coexpression networks involving a range of cell types. However, the rewired circuits as well as corresponding cell\u2013cell interactions remain poorly understood. To address this, we used a predictive machine learning framework to analyze single\u2010cell RNA\u2010sequencing data from 24 SSc patients across the severity spectrum as quantified by the modified Rodnan skin score (MRSS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used a least absolute shrinkage and selection operator (LASSO)\u2013based predictive machine learning approach on the single\u2010cell RNA\u2010sequencing data set to identify predictive biomarkers of SSc severity, both across and within cell types. The use of L1 regularization helps prevent overfitting on high\u2010dimensional data. Correlation network analyses were coupled to the LASSO model to identify cell\u2010intrinsic and cell\u2010extrinsic co\u2010correlates of the identified biomarkers of SSc severity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We found that the uncovered cell type\u2013specific predictive biomarkers of MRSS included previously implicated genes in fibroblast and myeloid cell subsets (e.g., SFPR2+ fibroblasts and monocytes), as well as novel gene biomarkers of MRSS, especially in keratinocytes. Correlation network analyses revealed novel cross\u2010talk between immune pathways and implicated keratinocytes in addition to fibroblast and myeloid cells as key cell types involved in SSc pathogenesis. We then validated the uncovered association of key gene expression and protein markers in keratinocytes, KRT6A and S100A8, with SSc skin disease severity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our global systems analyses reveal previously uncharacterized cell\u2010intrinsic and cell\u2010extrinsic signaling coexpression networks underlying SSc severity that involve keratinocytes, myeloid cells, and fibroblasts.<\/jats:p><jats:p><jats:boxed-text content-type=\"graphic\" position=\"anchor\"><jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/art42536-toc-0001-m.png\"><jats:alt-text>image<\/jats:alt-text><\/jats:graphic><\/jats:boxed-text><\/jats:p><\/jats:sec>","DOI":"10.1002\/art.42536","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T03:57:16Z","timestamp":1682395036000},"page":"1819-1830","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Cell Type\u2013Specific Biomarkers of Systemic Sclerosis Disease Severity Capture <scp>Cell\u2010Intrinsic<\/scp> and <scp>Cell\u2010Extrinsic<\/scp> Circuits"],"prefix":"10.1002","volume":"75","author":[{"given":"Jacob S.","family":"Berkowitz","sequence":"first","affiliation":[{"name":"Center for Systems Immunology, Departments of Immunology and Computational &amp; Systems Biology University of Pittsburgh  Pittsburgh Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tracy","family":"Tabib","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine University of Pittsburgh  Pittsburgh Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanxi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Center for Systems Immunology, Departments of Immunology and Computational &amp; Systems Biology University of Pittsburgh  Pittsburgh Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabrielle M.","family":"Sadej","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine University of Pittsburgh  Pittsburgh Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1412-4453","authenticated-orcid":false,"given":"Dinesh","family":"Khanna","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Department of Medicine University of Michigan  Ann Arbor"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrizia","family":"Fuschiotti","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine University of Pittsburgh  Pittsburgh Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert A.","family":"Lafyatis","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine University of Pittsburgh  Pittsburgh Pennsylvania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5747-064X","authenticated-orcid":false,"given":"Jishnu","family":"Das","sequence":"additional","affiliation":[{"name":"Center for Systems Immunology, Departments of Immunology and Computational &amp; 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