Dataset Information

Extracellular Vesicle Separation Techniques Impact Results from Human Blood Samples: Considerations for Diagnostic Applications.

ABSTRACT: Extracellular vesicles (EVs) are reminiscent of their cell of origin and thus represent a valuable source of biomarkers. However, for EVs to be used as biomarkers in clinical practice, simple, comparable, and reproducible analytical methods must be applied. Although progress is being made in EV separation methods for human biofluids, the implementation of EV assays for clinical diagnosis and common guidelines are still lacking. We conducted a comprehensive analysis of established EV separation techniques from human serum and plasma, including ultracentrifugation and size exclusion chromatography (SEC), followed by concentration using (a) ultracentrifugation, (b) ultrafiltration, or (c) precipitation, and immunoaffinity isolation. We analyzed the size, number, protein, and miRNA content of the obtained EVs and assessed the functional delivery of EV cargo. Our results demonstrate that all methods led to an adequate yield of small EVs. While no significant difference in miRNA content was observed for the different separation methods, ultracentrifugation was best for subsequent flow cytometry analysis. Immunoaffinity isolation is not suitable for subsequent protein analyses. SEC + ultracentrifugation showed the best functional delivery of EV cargo. In summary, combining SEC with ultracentrifugation gives the highest yield of pure and functional EVs and allows reliable analysis of both protein and miRNA contents. We propose this combination as the preferred EV isolation method for biomarker studies from human serum or plasma.

SUBMITTER: Tzaridis T

PROVIDER: S-EPMC8431127 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:While extracellular vesicles (EVs) continue to gain interest for therapeutic applications, their clinical translation is limited by a lack of optimal isolation methods. We sought to determine how universally applied isolation methods impact EV purity and yield. EVs were isolated by ultracentrifugation (UC), polyethylene glycol precipitation, Total Exosome Isolation Reagent, an aqueous two-phase system with and without repeat washes or size exclusion chromatography (SEC). EV-like particles could be detected for all isolation methods but varied in their purity and relative expression of surface markers (Alix, Annexin A2, CD9, CD63 and CD81). Assessments of sample purity were dependent on the specificity of characterisation method applied, with total particle counts and particle to protein (PtP) ratios often not aligning with quantitative measures of tetraspanin surface markers obtained using high-resolution nano-flow cytometry. While SEC resulted in the isolation of fewer particles with a relatively low PtP ratio (1.12 × 107 ± 1.43 × 106 vs highest recorded; ATPS/R 2.01 × 108 ± 1.15 × 109, p ⩽ 0.05), EVs isolated using this method displayed a comparatively high level of tetraspanin positivity (e.g. ExoELISA CD63⁺ particles; 1.36 × 1011 ± 1.18 × 1010 vs ATPS/R 2.58 × 1010 ± 1.92 × 109, p ⩽ 0.001). Results originating from an accompanying survey designed to evaluate pragmatic considerations surrounding method implementation (e.g. scalability and cost) identified that SEC and UC were favoured for overall efficiency. However, reservations were highlighted in the scalability of these methods, which could potentially hinder downstream therapeutic applications. In conclusion, variations in sample purity and yield were evident between isolation methods, while standard non-specific assessments of sample purity did not align with advanced quantitative high-resolution analysis of EV surface markers. Reproducible and specific assessments of EV purity will be critical for informing therapeutic studies.

Dataset Information

Extracellular Vesicle Separation Techniques Impact Results from Human Blood Samples: Considerations for Diagnostic Applications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets