Identifying the Phenotypes of Tumor-Derived Extracellular Vesicles Using Size-Coded Affinity Microbeads.

Tumor-derived extracellular vesicle (tEV) biomarkers can reflect cancer cell phenotypes and have great potential for cancer diagnosis and treatment. However, tEVs display high heterogeneity, and rapid and sensitive identification of EV biomarkers remains challenging due to their low expression. Spectral overlap also significantly limits the multiplex analysis of EV biomarkers by fluorescent probes. Herein, we developed a method for highly sensitive tEV phenotyping that uses size-coded microbeads that carry hairpin probes that can bind to aptamers targeting distinct tEV biomarkers. We also designed a microfluidic chip containing spacer arrays that segregate these microbeads in distinct chip regions according to their size to generate location-specific signals indicating the level of different EV biomarkers. The EV biomarker signal on these microbeads was amplified by in situ rolling cyclic amplification (RCA). This strategy permits the simultaneous detection of multiple tEV phenotypes by fluorescence spectroscopy without the limitations of spectral overlap. This study demonstrates that this tEV phenotyping method can rapidly and simultaneously detect six different tEV phenotypes with high sensitivity. Due to the programmability of the sensing platform, this method can be rapidly adapted to detect different tEV phenotype substitutions of the detected biomarkers. Notably, clinical cohort studies show that this strategy may provide new ideas for the precise diagnosis and personalized treatment of cancer patients.