From the Laboratory for Biomedical Neurosciences (E.V., A.K.-L., G.M.), Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale; Faculty of Biomedical Sciences (E.V., G.V., L.B., A.K.-L., G.M.), Università della Svizzera Italiana; Cellular and Molecular Cardiology Laboratory (J.B., G.V.), Cardiocentro Ticino Foundation, Lugano, Switzerland; Proteomic and Metabolomic Laboratory (D.D.S., P.M.), Institute for Biomedical Technologies-National Research Council (ITB-CNR), Segrate (Milan), Italy; Department of Electrical (A.B.), Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Italy; Laboratory for Cardiovascular Theranostics (S.B., L.B.), Cardiocentro Ticino Foundation, Lugano, Switzerland; Neurology Department (C.W.C., A.K.-L., G.M.), Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, Lugano; and Immunobiology of Neurological Disorders Lab (C.F.), Institute of Experimental Neurology (INSpe) and Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Neurol Neuroimmunol Neuroinflamm. 2020 Aug 12;7(6). doi: 10.1212/NXI.0000000000000866. Print 2020 Nov.
To develop a diagnostic model based on plasma-derived extracellular vesicle (EV) subpopulations in Parkinson disease (PD) and atypical parkinsonism (AP), we applied an innovative flow cytometric multiplex bead-based platform.
Plasma-derived EVs were isolated from PD, matched healthy controls, multiple system atrophy (MSA), and AP with tauopathies (AP-Tau). The expression levels of 37 EV surface markers were measured by flow cytometry and correlated with clinical scales. A diagnostic model based on EV surface markers expression was built via supervised machine learning algorithms and validated in an external cohort.
Distinctive pools of EV surface markers related to inflammatory and immune cells stratified patients according to the clinical diagnosis. PD and MSA displayed a greater pool of overexpressed immune markers, suggesting a different immune dysregulation in PD and MSA vs AP-Tau. The receiver operating characteristic curve analysis of a compound EV marker showed optimal diagnostic performance for PD (area under the curve [AUC] 0.908; sensitivity 96.3%, specificity 78.9%) and MSA (AUC 0.974; sensitivity 100%, specificity 94.7%) and good accuracy for AP-Tau (AUC 0.718; sensitivity 77.8%, specificity 89.5%). A diagnostic model based on EV marker expression correctly classified 88.9% of patients with reliable diagnostic performance after internal and external validations.
Immune profiling of plasmatic EVs represents a crucial step toward the identification of biomarkers of disease for PD and AP.
为了开发一种基于帕金森病（PD）和非典型帕金森综合征（AP）患者血浆衍生细胞外囊泡（EV）亚群的诊断模型，我们应用了一种创新的流式细胞术多指标微珠检测平台。
从 PD、相匹配的健康对照者、多系统萎缩（MSA）以及伴有tau 病理的 AP（AP-Tau）患者的血浆中分离 EV。通过流式细胞术测量 EV 表面标志物的表达水平，并与临床量表相关联。通过有监督的机器学习算法构建基于 EV 表面标志物表达的诊断模型，并在外部队列中进行验证。
与炎症和免疫细胞相关的独特 EV 表面标志物池根据临床诊断对患者进行分层。PD 和 MSA 显示出更大的过度表达免疫标志物池，提示 PD 和 MSA 与 AP-Tau 之间存在不同的免疫失调。EV 复合标志物的受试者工作特征曲线分析显示对 PD（曲线下面积 [AUC] 0.908；敏感度 96.3%，特异性 78.9%）和 MSA（AUC 0.974；敏感度 100%，特异性 94.7%）具有最佳的诊断性能，对 AP-Tau 也具有较好的准确性（AUC 0.718；敏感度 77.8%，特异性 89.5%）。基于 EV 标志物表达的诊断模型在内部和外部验证后，具有可靠的诊断性能，可正确分类 88.9%的患者。
对血浆 EV 的免疫特征分析是识别 PD 和 AP 疾病生物标志物的重要步骤。
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