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Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning.
Liu, Jared; Kumar, Sugandh; Hong, Julie; Huang, Zhi-Ming; Paez, Diana; Castillo, Maria; Calvo, Maria; Chang, Hsin-Wen; Cummins, Daniel D; Chung, Mimi; Yeroushalmi, Samuel; Bartholomew, Erin; Hakimi, Marwa; Ye, Chun Jimmie; Bhutani, Tina; Matloubian, Mehrdad; Gensler, Lianne S; Liao, Wilson.
Afiliación
  • Liu J; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Kumar S; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Hong J; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Huang ZM; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Paez D; Division of Rheumatology, Department of Medicine, University of California at San Francisco, San Francisco, CA, United States.
  • Castillo M; Division of Rheumatology, Department of Medicine, University of California at San Francisco, San Francisco, CA, United States.
  • Calvo M; Division of Rheumatology, Department of Medicine, University of California at San Francisco, San Francisco, CA, United States.
  • Chang HW; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Cummins DD; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Chung M; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Yeroushalmi S; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Bartholomew E; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Hakimi M; Department of Dermatology, University of California at San Francisco, San Francisco, CA, United States.
  • Ye CJ; Division of Rheumatology, Department of Medicine, University of California at San Francisco, San Francisco, CA, United States.
  • Bhutani T; Institute for Human Genetics, University of California at San Francisco, San Francisco, CA, United States.
  • Matloubian M; Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA, United States.
  • Gensler LS; Institute of Computational Health Sciences, University of California at San Francisco, San Francisco, CA, United States.
  • Liao W; Parker Institute for Cancer Immunotherapy, San Francisco, CA, United States.
Front Immunol ; 13: 835760, 2022.
Article en En | MEDLINE | ID: mdl-35309349
Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Psoriasis / Artritis Psoriásica Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: Front Immunol Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Psoriasis / Artritis Psoriásica Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Revista: Front Immunol Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza