DNA Processor Modules Enabled Pattern Recognition of Extracellular Vesicles for Facile Cancer Diagnosis ^†

Extracellular vesicles (EVs) carry rich protein and nucleic acid information of host cells, thus, they are considered to be reliable biomarkers for cancer diagnosis. However, current EVs detection relies on technical expertise that requires special equipment to readout signals that prevent its point-of-care testing. In this study, we propose a Pattern Recognition of Molecular in Interest on Single EVs (PROMISE) strategy for clinical EVs detection. This strategy combines an aptamer-based DNA processor on single EVs, and a color-rendering enzyme to provide a visual output for naked-eyes enabled profiling. We demonstrate 100% accuracy in breast cancer discrimination. Furthermore, by utilizing thin-layer chromatography (TLC), we achieve a simultaneous screening of two types of cancers (breast and prostate cancer) in one sample. This PROMISE strategy could serve as a versatile platform for point-of-care EVs diagnosis. (Figure presented.).