Abstract
Proteins localized on the surface or within the lumen of tumor-derived extracellular vesicles (EVs) play distinct roles in cancer progression. However, quantifying both populations of proteins within EVs has been hampered due to the limited sensitivity of the existing protein detection methods and inefficient EV isolation techniques. In this study, the eSimoa framework, an innovative approach enabling spatial decoding of EV protein biomarkers with unmatched sensitivity and specificity is presented. Using the luminal eSimoa pipeline, the absolute concentration of luminal RAS or KRASG12D proteins is released and measured, uncovering their prevalence in pancreatic tumor-derived EVs. The pulldown eSimoa pipeline measured absolute protein concentrations from low-abundance EV subpopulations. The eSimoa assays detected EVs in both PBS and plasma samples, confirming their applicability across diverse clinical sample types. Overall, the eSimoa framework offers a valuable tool to (1) detect EVs at concentrations as low as 105 EV mL-1 in plasma, (2) quantify absolute EV protein concentrations as low as fM, and (3) decode the spatial distribution of EV proteins. This study highlights the potential of eSimoa in identifying disease-specific EV protein biomarkers in clinical samples with minimal pre-purification, thereby driving advancements in clinical translation.