Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning.
Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning.
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DOI：
10.3389/fimmu.2022.835760
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发表时间：
2022
影响因子：
7.3
通讯作者：
Liao W
中科院分区：
文献类型：
--
作者：
Liu J;Kumar S;Hong J;Huang ZM;Paez D;Castillo M;Calvo M;Chang HW;Cummins DD;Chung M;Yeroushalmi S;Bartholomew E;Hakimi M;Ye CJ;Bhutani T;Matloubian M;Gensler LS;Liao W
银屑病关节炎 (PSA) 的早期诊断对于成功的治疗干预非常重要，但目前仍然具有挑战性，部分原因是非侵入性生物标志物的稀缺。在这项研究中，我们对转录组和细胞表面蛋白表达进行了单细胞分析，以比较 PSA 个体、单纯皮肤银屑病 (PSO) 个体和健康个体的外周血免疫细胞群。我们鉴定了 PSA、PSO 和健康受试者在 30 种免疫细胞类型中差异表达的基因和蛋白质，并观察到某些细胞类型以及特定的细胞表型子集在这些群体之间存在丰度差异。 PSA、PSO 和健康组之间的细胞类型特异性基因和蛋白质表达差异，以及之前发表的 200 个 PSA 遗传风险因素，被进一步用于执行机器学习分类，当在三组之间对受试者进行分类或专门区分 PSA 和 PSO 时，最佳模型达到 AUROC ≥ 0.87。因此，我们的研究结果扩展了与 PSA 相关的基因、蛋白质和细胞生物标志物的范围，并证明了基于机器学习的诊断对于这种疾病的实用性。
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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影响因子：
27.4
作者：
Haroon, Muhammad;Gallagher, Phil;FitzGerald, Oliver
通讯作者：
FitzGerald, Oliver
DOI：
10.1038/nri3062
发表时间：
2011-09-09
期刊：
Nature reviews. Immunology
影响因子：
--
作者：
通讯作者：
--
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
5.5
作者：
Chandran, Vinod;Cook, Richard J.;Gladman, Dafna D.
通讯作者：
Gladman, Dafna D.
影响因子：
5
作者：
O'Brien-Gore, Charlotte;Gray, Elizabeth H.;Kirkham, Bruce W.
通讯作者：
Kirkham, Bruce W.