Identification and validation of extracellular vesicle (EV)-associated biomarkers requires robust isolation and characterization protocols. We assessed the impact of some commonly implemented pre-analytical, analytical and post-analytical variables in EV research. Centrifugal filters with different membrane types and pore sizes are used to reduce large volume biofluids prior to EV isolation or to concentrate EVs. We compared five commonly reported filters for their efficiency when using plasma, urine and EV-spiked PBS. Regenerated cellulose membranes with pore size of 10 kDa recovered EVs the most efficient. Less than 40% recovery was achieved with other filters. Next, we analyzed the effect of the type of protein assays to measure EV protein in colorimetric and fluorometric kits. The fluorometric assay Qubit measured low concentration EV and BSA samples the most accurately with the lowest variation among technical and biological replicates. Lastly, we quantified Optiprep remnants in EV samples from density gradient ultracentrifugation and demonstrate that size-exclusion chromatography efficiently removes Optiprep from EVs. In conclusion, choice of centrifugal filters and protein assays confound EV analysis and should be carefully considered to increase efficiency towards biomarker discovery. SEC-based removal of Optiprep remnants from EVs can be considered for downstream applications.

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Recent years have seen an increase of extracellular vesicle (EV) research geared towards biological understanding, diagnostics and therapy. However, EV data interpretation remains challenging owing to complexity of biofluids and technical variation introduced during sample preparation and analysis. To understand and mitigate these limitations, we generated trackable recombinant EV (rEV) as a biological reference material. Employing complementary characterization methods, we demonstrate that rEV are stable and bear physical and biochemical traits characteristic of sample EV. Furthermore, rEV can be quantified using fluorescence-, RNA- and protein-based technologies available in routine laboratories. Spiking rEV in biofluids allows recovery efficiencies of commonly implemented EV separation methods to be identified, intra-method and inter-user variability induced by sample handling to be defined, and to normalize and improve sensitivity of EV enumerations. We anticipate that rEV will aid EV-based sample preparation and analysis, data normalization, method development and instrument calibration in various research and biomedical applications.

Extracellular vesicles (EV) are increasingly being recognized as important vehicles of intercellular communication and promising diagnostic and prognostic biomarkers in cancer. Despite this enormous clinical potential, the plethora of methods to separate EV from biofluids, providing material of highly variable purity, and lacking knowledge regarding methodological repeatability pose a barrier to clinical translation. Urine is considered an ideal proximal fluid for the study of EV in urological cancers due to its direct contact with the urogenital system. We demonstrate that density-based fractionation of urine by bottom-up Optiprep density gradient centrifugation separates EV and soluble proteins with high specificity and repeatability. Mass spectrometrybased proteomic analysis of urinary EV (uEV) in men with benign and malignant prostate disease allowed us to significantly expand the known human uEV proteome with high specificity and identifies a unique biological profile in prostate cancer not uncovered by the analysis of soluble proteins. In addition, profiling the proteome of EV separated from prostate tumour conditioned medium and matched uEV confirms the specificity of the identified uEV proteome for prostate cancer. Finally, a comparative proteomic analysis with uEV from patients with bladder and renal cancer provided additional evidence of the selective enrichment of protein signatures in uEV reflecting their respective cancer tissues of origin. In conclusion, this study identifies hundreds of previously undetected proteins in uEV of prostate cancer patients and provides a powerful toolbox to map uEV content and contaminants ultimately allowing biomarker discovery in urological cancers.