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Updated: Dec 11, 2025
Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
Published on: January 31, 2025
Immune profiling of plasma-derived extracellular vesicles identifies Parkinson disease
Elena Vacchi1, Jacopo Burrello1, Dario Di Silvestre1
1From 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.
Objective:
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.
Methods:
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.
Results:
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.
Conclusions:
Immune profiling of plasmatic EVs represents a crucial step toward the identification of biomarkers of disease for PD and AP.
