Density-based lipoprotein depletion improves extracellular vesicle isolation and functional analysis

BACKGROUND:Blood plasma is the main source of extracellular vesicles (EV) in clinical studies aiming the identification of biomarkers and the investigation of pathophysiological processes, especially regarding EV roles in inflammation and thrombosis. However, EV isolation from plasma has faced the fundamental issue of lipoprotein contamination, representing an important bias since lipoproteins are highly abundant and also modulate cell signaling, metabolism and thromboinflammation. OBJECTIVE:Here we aimed to isolate plasma EVs after depleting lipoproteins, improving sample purity and EV thromboinflammatory analysis. METHODS:Density-based gradient ultracentrifugation (G-UC) was used for lipoprotein depletion before EVs isolation from plasma through size-exclusion chromatography (SEC) or serial centrifugation (SC). Recovered EVs were analyzed by size, concentration, cellular source, ultrastructure and bottom-up proteomics. RESULTS:G-UC efficiently separated lipoproteins from the plasma, allowing subsequent EV isolation through SEC or SC. Combined analysis from EV proteomics, cholesterol quantification and apoB-100 detection confirmed the significant reduction of lipoproteins from isolated EVs. Proteomic analysis identified similar gene ontology and cellular components in EVs regardless of lipoprotein depletion, which was consistent with similar EV cellular sources, size and ultrastructure by flow cytometry and transmission electron microscopy. Importantly, lipoprotein depletion increased the detection of less abundant proteins in EV proteome and enhanced thromboinflammatory responses of platelets and monocytes stimulated in vitro with EV isolates. CONCLUSION:Combination of G-UC+SEC significantly reduced EV lipoprotein contamination without interfering in EV cellular source, gene ontology and ultrastructure, allowing the obtention of highly pure EVs with potential implications for functional assays, proteomic and lipidomic analysis.