Acidification of blood plasma facilitates the separation and analysis of extracellular vesicles

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

BACKGROUND

Blood plasma is the main source of extracellular vesicles (EVs) in clinical studies aiming to identify biomarkers and to investigate 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 modulate cell signaling, metabolism, and thromboinflammation.

OBJECTIVES

Here, we aimed to isolate plasma EVs after depleting lipoproteins, thereby improving sample purity and EV thromboinflammatory analysis.

METHODS

Density-based gradient ultracentrifugation (G-UC) was used for lipoprotein depletion before EV 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 in 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 recovery of highly pure EVs with potential implications for functional assays and proteomic and lipidomic analyses.

Chemically-Induced Lipoprotein Breakdown for Improved Extracellular Vesicle Purification

Extracellular vesicles (EVs) are nanosized biomolecular packages involved in intercellular communication. EVs are released by all cells, making them broadly applicable as therapeutic, diagnostic, and mechanistic components in (patho)physiology. Sample purity is critical for correctly attributing observed effects to EVs and for maximizing therapeutic and diagnostic performance. Lipoprotein contaminants represent a major challenge for sample purity. Lipoproteins are approximately six orders of magnitude more abundant in the blood circulation and overlap in size, shape, and density with EVs. This study represents the first example of an EV purification method based on the chemically-induced breakdown of lipoproteins. Specifically, a styrene-maleic acid (SMA) copolymer is used to selectively breakdown lipoproteins, enabling subsequent size-based separation of the breakdown products from plasma EVs. The use of the polymer followed by tangential flow filtration or size-exclusion chromatography results in improved EV yield, preservation of EV morphology, increased EV markers, and reduced contaminant markers. SMA-based EV purification enables improved fluorescent labeling, reduces interactions with macrophages, and enhances accuracy, sensitivity, and specificity to detect EV biomarkers, indicating benefits for various downstream applications. In conclusion, SMA is a simple and effective method to improve the purity and yield of plasma-derived EVs, which favorably impacts downstream applications.

Peptide-based capture-and-release purification of extracellular vesicles and statistical algorithm enabled quality assessment

Circulating extracellular vesicles (EVs) have gained significant attention for discovering tumor biomarkers. However, isolating EVs with well-defined homogeneous populations from complex biological samples is challenging. Different isolation methods have been found to derive different EV populations carrying different molecular contents, which confounds current investigations and hinders subsequent clinical translation. Therefore, standardizing and building a rigorous assessment of isolated EV quality associated with downstream molecular analysis is essential. To address this need, we introduce a statistical algorithm (ExoQuality Index, EQI) by integrating multiple EV characterizations (size, particle concentration, zeta potential, total protein, and RNA), enabling direct EV quality assessment and comparisons between different isolation methods. We also introduced a novel capture-release isolation approach using a pH-responsive peptide conjugated with NanoPom magnetic beads (ExCy) for simple, fast, and homogeneous EV isolation from various biological fluids. Bioinformatic analysis of next-generation sequencing (NGS) data of EV total RNAs from pancreatic cancer patient plasma samples using our novel EV isolation approach and quality index strategy illuminates how this approach improves the identification of tumor associated molecular markers. Results showed higher human mRNA coverage compared to existing isolation approaches in terms of both pancreatic cancer pathways and EV cellular component pathways using gProfiler pathway analysis. This study provides a valuable resource for researchers, establishing a workflow to prepare and analyze EV samples carefully and contributing to the advancement of reliable and rigorous EV quality assessment and clinical translation.