Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning

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 comp...

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Vydané v:Frontiers in immunology Ročník 13; s. 835760
Hlavní autori: 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
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Media S.A 02.03.2022
Predmet:
Arthritis, Psoriatic - diagnosis
Arthritis, Psoriatic - genetics
Biomarkers
CITE-seq
diagnostic test
Epitopes
Humans
Immunology
Machine Learning
psoriasis
Psoriasis - diagnosis
Psoriasis - genetics
psoriatic arthritis
single cell
Transcriptome
single cell
psoriatic arthritis
machine learning
diagnostic test
CITE-seq
psoriasis
ISSN:1664-3224, 1664-3224
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Reviewed by: Eva Reali, University of Milano-Bicocca, Italy; Iannis E Adamopoulos, Beth Israel Deaconess Medical Center and Harvard Medical School, United States
This article was submitted to Autoimmune and Autoinflammatory Disorders, a section of the journal Frontiers in Immunology
Edited by: Xu-jie Zhou, Peking University First Hospital, China
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2022.835760