Assessment of technical and clinical utility of a bead-based flow cytometry platform for multiparametric phenotyping of CNS-derived extracellular vesicles

Extracellular vesicles (EVs) derived from the CNS are potential liquid-biopsy markers for early detection and monitoring of neurodegenerative diseases and brain tumors. This study assessed the performance of a bead-based flow cytometry assay (EV Neuro) for multiparametric detection of CNS-derived EVs and identification of disease-specific markers. Different sample materials and EV isolation methods were compared. Glioblastoma- and primary human astrocyte-derived EVs exhibited distinct EV profiles, with signal intensities increasing with higher EV input. Analysis of serum or plasma from glioblastoma, multiple sclerosis, Alzheimers Disease patients and healthy controls showed varying marker signal intensities. Notably, data normalization improved marker identification. Specific EV populations, such as CD36+EVs in glioblastoma and GALC+EVs in multiple sclerosis, were significantly elevated in disease compared to controls. Clustering analysis techniques effectively differentiated glioblastoma patients from controls. A potential correlation between CD107a+EVs and neurofilament levels in the blood was identified in multiple sclerosis patients. Together, the semi-quantitative EV Neuro assay demonstrated its utility for EV profiling in complex samples. However, reliable statistical results in biomarker studies require large sample cohorts and high effect sizes. Nonetheless, this exploratory trial confirmed the feasibility of discovering EV-associated biomarkers and monitoring circulating EV profiles in CNS diseases using the EV Neuro assay.