Extracellular vesicles (EVs) are lipid-bilayer-enclosed membrane vesicles (30−1000 nm in diameter) that are actively secreted by most mammalian cells and abundantly present in various body fluids. Tumor-derived EVs (tEVs) carry abundant tumor-related cargos, such as proteins and nucleic acids, taking part in tumor development. Molecular profiling of tEVs holds great promise for non-invasive cancer diagnosis. However, sensitive and accurate identification of tEVs is challenged by the heterogeneity of EV phenotypes which reflect different cell origins. Here we present a DNA computation device mediated by thermophoresis for detection of tEVs. The strategy leverages the aptamer-based logic gate using multiple protein biomarkers on single EVs as the input and thermophoretic accumulation to amplify the output signals for highly sensitive and specific profiling of tEVs. Two breast cancer (BC) associated proteins, epithelial cell adhesion molecule (EpCAM) and human epidermal growth actor receptor 2 (HER2), are selected as inputs for logic gate activation on EV membranes to improve the identification of tEVs closely associated with BC. Employing this platform, we demonstrate a high accuracy of 97% for discrimination of BC patients and healthy donors in a clinical cohort (n = 30). Furthermore, molecular phenotyping assessed by tEVs is in concordance with the results from tissue biopsy in BC patients. The thermophoresis-mediated molecular computation on EVs thus provides new opportunities for accurate detection and classification of cancers.

Molecular identification of tumor-derived extracellular vesicles using thermophoresis-mediated DNA computation

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ACS Fall 2022

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