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血浆和脑脊液中炎性细胞外囊泡的分析:帕金森病的优化诊断模型

Profiling Inflammatory Extracellular Vesicles in Plasma and Cerebrospinal Fluid: An Optimized Diagnostic Model for Parkinson's Disease.

作者信息

Vacchi Elena, Burrello Jacopo, Burrello Alessio, Bolis Sara, Monticone Silvia, Barile Lucio, Kaelin-Lang Alain, Melli Giorgia

机构信息

Laboratory for Biomedical Neurosciences, Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland.

Faculty of Biomedical Sciences, Universita della Svizzera Italiana, 6900 Lugano, Switzerland.

出版信息

Biomedicines. 2021 Feb 25;9(3):230. doi: 10.3390/biomedicines9030230.

Abstract

Extracellular vesicles (EVs) play a central role in intercellular communication, which is relevant for inflammatory and immune processes implicated in neurodegenerative disorders, such as Parkinson's Disease (PD). We characterized and compared distinctive cerebrospinal fluid (CSF)-derived EVs in PD and atypical parkinsonisms (AP), aiming to integrate a diagnostic model based on immune profiling of plasma-derived EVs via artificial intelligence. Plasma- and CSF-derived EVs were isolated from patients with PD, multiple system atrophy (MSA), AP with tauopathies (AP-Tau), and healthy controls. Expression levels of 37 EV surface markers were measured by a flow cytometric bead-based platform and a diagnostic model based on expression of EV surface markers was built by supervised learning algorithms. The PD group showed higher amount of CSF-derived EVs than other groups. Among the 17 EV surface markers differentially expressed in plasma, eight were expressed also in CSF of a subgroup of PD, 10 in MSA, and 6 in AP-Tau. A two-level random forest model was built using EV markers co-expressed in plasma and CSF. The model discriminated PD from non-PD patients with high sensitivity (96.6%) and accuracy (92.6%). EV surface marker characterization bolsters the relevance of inflammation in PD and it underscores the role of EVs as pathways/biomarkers for protein aggregation-related neurodegenerative diseases.

摘要

细胞外囊泡(EVs)在细胞间通讯中起着核心作用,这与神经退行性疾病(如帕金森病(PD))中涉及的炎症和免疫过程相关。我们对PD和非典型帕金森综合征(AP)中独特的脑脊液(CSF)来源的EVs进行了表征和比较,旨在通过人工智能整合基于血浆来源的EVs免疫分析的诊断模型。从PD患者、多系统萎缩(MSA)患者、伴有tau蛋白病的AP(AP-Tau)患者和健康对照中分离出血浆和CSF来源的EVs。通过基于流式细胞术微球的平台测量37种EV表面标志物的表达水平,并通过监督学习算法建立基于EV表面标志物表达的诊断模型。PD组显示脑脊液来源的EVs数量高于其他组。在血浆中差异表达的17种EV表面标志物中,有8种也在一部分PD患者的脑脊液中表达,10种在MSA患者中表达,6种在AP-Tau患者中表达。使用在血浆和脑脊液中共同表达的EV标志物建立了两级随机森林模型。该模型以高灵敏度(96.6%)和准确度(92.6%)区分PD患者和非PD患者。EV表面标志物的表征支持了炎症在PD中的相关性,并强调了EVs作为蛋白质聚集相关神经退行性疾病的途径/生物标志物的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d19/7996605/4c97f3614226/biomedicines-09-00230-g001.jpg

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