Characterizing the Heterogeneity of Small Extracellular Vesicle Populations in Multiple Cancer Types via an Ultrasensitive Chip

Identifying small extracellular vesicle (sEV) subpopulations based on their different molecular signatures could potentially reveal the functional roles in physiology and pathology. However, it is a challenge to achieve this aim due to the nanosized dimensions of sEVs, low quantities of biological cargo each sEV carries, and our incomplete knowledge of identifying features capable of separating heterogeneous sEV subpopulations. Here, a sensitive, multiplexed, and nano-mixing-enhanced sEV subpopulation characterization platform (ESCP) is proposed to precisely determine the sEV phenotypic heterogeneity and understand the role of sEV heterogeneity in cancer progression and metastasis. The ESCP utilizes spatially patterned anti-tetraspanin-functionalized micro-arrays for sEV subpopulation sorting and nanobarcode-based surface-enhanced Raman spectroscopy for multiplexed read-outs. An ESCP has been used for investigating sEV phenotypic heterogeneity in terms of canonical sEV tetraspanin molecules and cancer-associated protein biomarkers in both cancer cell line models and cancer patient samples. Our data explicitly demonstrate the selective enrichment of tetraspanins and cancer-associated protein biomarkers, in particular sEV subpopulations. Therefore, it is believed that the ESCP could enable the evaluation and broader application of sEV subpopulations as potential diagnostic disease biomarkers.

Automated summary

This study introduces a novel platform for characterizing sEV subpopulations, the ESCP, which accurately identifies the phenotypic heterogeneity of sEVs and investigates the role of sEV heterogeneity in cancer progression and metastasis. The ESCP utilizes anti-tetraspanin-functionalized micro-arrays and nanobarcode-based surface-enhanced Raman spectroscopy for multiplexed read-outs, and has been applied to studying sEV phenotypic heterogeneity in cancer cell line models and cancer patient samples.