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In Biosensors & bioelectronics

The sensitive detection of the multiple immuno-subtypes of cancer-specific extracellular vesicles (EVs) has emerged as a promising method for multiclass cancer diagnosis; however, its limitations in sensitivity, accessibility, and multiple detection of EV subtypes have hindered its further implementation. Here, we present a platform for sensitive EV detection enabled by sessile droplet array (eSD) that exploits enhanced immuno-capture of EVs via evaporation-driven radial flows in a sessile droplet. Compared to a micro-well without internal flows, this platform demonstrates significantly enhanced EV capture and detection by detecting low levels of EVs with a detection limit of 384.7 EVs per microliter, which is undetectable in the micro-well. In addition, using a small sample consumption of ∼0.2 μL plasma per droplet, the platform detects EV immuno-subtypes against seven different antibodies in patient plasma samples of different cancer types (liver, colon, lung, breast and prostate cancers). Further, using the profiling data, the platform exhibits a sensitivity of 100% (95% confidence interval (CI): 83-100%) and a specificity of 100% (95% CI: 40-100%) for the diagnosis of cancer, and classified cancer types with an overall accuracy of 96% (95% CI: 86-100%) using a two-staged algorithm based on quadratic discriminant analysis technique for machine learning.

Lee Eunjeong, Shin Suyeon, Yim Sang-Gu, Lee Gyeong Won, Shim Yujin, Kim Yoon-Jin, Yang Seung Yun, Kim Anmo J, Choi Sungyoung


Cancer diagnosis, Extracellular vesicle, Multiclass cancer classification, Sessile droplet