Fingerprint Profiling\nof Glycans on Extracellular\nVesicles via Lectin-Induced Aggregation Strategy for Precise Cancer\nDiagnostics

Extracellular vesicles (EVs) harbor abundant glycans\nthat mediate\nvarious functions, such as intercellular communication and disease\nadvancement, which play significant roles in disease progression.\nHowever, the presence of EV heterogeneity in body fluids and the complex\nnature of the glycan structures have posed challenges for the detection\nof EV glycans. In this study, we provide a streamlined method integrated, <u>me</u>mbrane-<u>s</u>pecific <u>s</u>eparation with lectin-induced <u>ag</u>gr<u>e</u>gation strategy (MESSAGE), for multiplexed profiling\nof EV glycans. By leveraging a rationally designed lectin-induced\naggregation strategy, the expression of EV glycans is converted to\nsize-based signals. With the assistance learning machine algorithms,\nthe MESSAGE strategy with high sensitivity, specificity, and simplicity\ncan be used for early cancer diagnosis and classification, as well\nas monitoring cancer metastasis via 20 μL plasma sample within\n2 h. Furthermore, our platform holds promise for advancing the field\nof EV-based liquid biopsy for clinical applications, opening new possibilities\nfor the profiling of EV glycan signatures in various disease states.