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

Abstract

Background

Extracellular vesicles (EVs) originating from the central nervous system (CNS) can enter the blood stream and carry molecules characteristic of disease states. Therefore, circulating CNS-derived EVs have the potential to serve as liquid-biopsy markers for early diagnosis and follow-up of neurodegenerative diseases and brain tumors. Monitoring and profiling of CNS-derived EVs using multiparametric analysis would be a major advance for biomarker as well as basic research. Here, we explored the performance of a multiplex bead-based flow-cytometry assay (EV Neuro) for semi-quantitative detection of CNS-derived EVs in body fluids. Methods

EVs were separated from culture of glioblastoma cell lines (LN18, LN229, NCH82) and primary human astrocytes and measured at different input amounts in the MACSPlex EV Kit Neuro, human. In addition, EVs were separated from blood samples of small cohorts of glioblastoma (GB), multiple sclerosis (MS) and Alzheimer’s disease patients as well as healthy controls (HC) and subjected to the EV Neuro assay. To determine statistically significant differences between relative marker signal intensities, an unpaired samples t-test or Wilcoxon rank sum test were computed. Data were subjected to tSNE, heatmap clustering, and correlation analysis to further explore the relationships between disease state and EV Neuro data. Results

Glioblastoma cell lines and primary human astrocytes showed distinct EV profiles. Signal intensities were increasing with higher EV input. Data normalization improved identification of markers that deviate from a common profile. Overall, patient blood-derived EV marker profiles were constant, but individual EV populations were significantly increased in disease compared to healthy controls, e.g. CD36+EVs in glioblastoma and GALC+EVs in multiple sclerosis. tSNE and heatmap clustering analysis separated GB patients from HC, but not MS patients from HC. Correlation analysis revealed a potential association of CD107a+EVs with neurofilament levels in blood of MS patients and HC. Conclusions

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.