Multi-modal imaging-based biosensing platform for digital small extracellular vesicles profiling and cancer detection

Abstract

Detecting the level of small extracellular vesicle (sEV) and their molecular contents digitally can potentially improve cancer detection. This potential has been hampered by insufficient sensitivity, dynamic range, and the need for purification and/or concentrating. We demonstrate a multi-modal imaging-based biosensing platform by streamlining a label-free (plasmonic) modality to provide unbiased digital sEV detection/localization and a fluorescence modality for molecular profiling of cargo micro-RNA (miR). The digital counts by the plasmonic modality indicate a remarkable difference between healthy volunteers (N=106) and cancer patients (N=205), and can detect cancer patients with a sensitivity of 99.5% and a specificity of 97.3%. The cargo miR-21 provides molecular specificity within the same sEV population, which pinpoints the sEVs subset of cancer origin. The label-free count threshold has then been further validated using 38 cancer patients and 29 healthy volunteers in a blinded fashion and yielded 100% accuracy.