Unravelling the proteomic landscape of extracellular vesicles in prostate cancer by density-based fractionation of urine

Abstract 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 spectrometry-based 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

Benchmarking blood collection tubes and processing intervals for extracellular vesicle performance metrics

Abstract The analysis of extracellular vesicles (EV) in blood samples is under intense investigation and holds the potential to deliver clinically meaningful biomarkers for health and disease. Technical variation must be minimized to confidently assess EV-associated biomarkers, but the impact of pre-analytics on EV characteristics in blood samples remains minimally explored. We present the results from the first large-scale EV Blood Benchmarking (EVBB) study in which we systematically compared 11 blood collection tubes (BCT; six preservation and five non-preservation) and three blood processing intervals (BPI; 1, 8 and 72 h) on defined performance metrics (n = 9). The EVBB study identifies a significant impact of multiple BCT and BPI on a diverse set of metrics reflecting blood sample quality, ex-vivo generation of blood-cell derived EV, EV recovery and EV-associated molecular signatures. The results assist the informed selection of the optimal BCT and BPI for EV analysis. The proposed metrics serve as a framework to guide future research on pre-analytics and further support methodological standardization of EV studies

The generation and use of recombinant extracellular vesicles as biological reference material

Abstract 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