2020
DOI: 10.1212/nxi.0000000000000866
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Immune profiling of plasma-derived extracellular vesicles identifies Parkinson disease

Abstract: ObjectiveTo 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.MethodsPlasma-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 … Show more

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Cited by 47 publications
(47 citation statements)
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“…The exome marker curves for PD and MSA were rather congruent and different from those of corticobasal degeneration and progressive supranuclear palsy. In the panels shared by PD and MSA was a transcription factor playing, SP1, which is an important regulator of neuroinflammation in multiple sclerosis ( Vacchi et al, 2020 ).…”
Section: Discussionmentioning
confidence: 99%
“…The exome marker curves for PD and MSA were rather congruent and different from those of corticobasal degeneration and progressive supranuclear palsy. In the panels shared by PD and MSA was a transcription factor playing, SP1, which is an important regulator of neuroinflammation in multiple sclerosis ( Vacchi et al, 2020 ).…”
Section: Discussionmentioning
confidence: 99%
“…MuSIC probes have a wide variety of applications for flow cytometry, one of which is immune profiling. Immune profiling is typically performed using flow cytometry [45][46][47] , which has previously limited multiplexing to roughly a dozen analytes (depending on the capabilities of the instrument) as a result of spectral overlap 48,49 . Mass cytometry has been transformative for immune profiling [50][51][52] , but is slower than flow cytometry and is destructive so prevents further use of the cells after analysis 48 .…”
Section: Discussionmentioning
confidence: 99%
“…We characterized distinctive EV subpopulations in plasma by simultaneously immunophenotyping 37 different membrane proteins using a flow cytometry multiplex bead-based platform [12,13] and then a diagnostic model based on the distinctive EV surface proteins profile was generated via supervised machine learning algorithms. The resulting diagnostic model was able to correctly classify 88.9% of patients, with reliable diagnostic performance after internal and external validations [14]. Indeed, recent studies trying to dissect the cell/tissue origin of circulating EVs, showed that the majority of plasma-derived EVs come from hematopoietic cells, in particular platelets, B cells and T cells [15].…”
Section: Introductionmentioning
confidence: 93%