Extracellular vesicles (EV) provide a spatiotemporal fingerprint of the cell of origin, and EV secreted in urine or circulating in blood plasma carry biomarkers of therapy response. The use of labor intensive and time consuming bulk EV separation methods hampers our understanding of EV heterogeneity in biofluids, and impedes the clinical implementation of EV biomarkers. I will establish an innovative workflow consisting of EV fractionation by AF4-MALS and multi EV surface protein detection using ICP-ToF-MS (abbreviated as AF4-MALS-MS) to enable direct, rapid and sensitive sensing of EV surface proteins in biofluids. Using well-defined samples of increasing complexity, I will set-up the AF4-MALS-MS workflow, including optimization of antibody labeling, and analysis of the dynamic range. I will implement AF4-MALS-MS to map the heterogeneity and origin of urinary and blood plasma EV with unprecedented detail. By using longitudinal urine and blood plasma samples from respectively prostate cancer and breast cancer patients, I will explore the applicability of the AF4-MALS-MS workflow to discriminate pre- from post-treatment samples. Finally, I will benchmark the performance of AF4-MALS-MS against current state-of-the-art methods. In conclusion, I am confident that AF4-MALS-MS has the capacity to rEVolutionize the EV field towards mass EV-metry allowing a more fine-grained view of EV heterogeneity in biofluids with the potential to analyze biomarkers of therapy response.