Extracellular vesicles (EVs) carry a rich repertoire of glycan structures that exhibit crucial functions in diverse biological processes. Currently, the deciphering of EV glycans is predominantly conducted using bulk methods, which only yield ensemble-averaged information about a population of EV particles. This kind of deciphering manner suffers from limited sensitivity (particularly in detecting specific EV populations within complex biological matrices) and is inadequate for effectively elucidating fundamental characteristics (e.g., heterogeneity) of EV glycans, thereby impeding the exploitation of EV glycans. To overcome these challenges, we proposed a droplet-based strategy to achieve the deep analysis of EV glycans down to the single-particle level. Named Droplet-EVG, this assay leveraged polydisperse droplets formed through facile shaking as reaction units to compartmentalize distinct EV particles, and transferred the glycan signatures into fluorescence signals by integrating nanoagent-assisted EV manipulation, lectin-glycan affinity, and enzyme-mediated signal amplification. Compared to conventional bulk assays (ELISA and MAEG), this assay demonstrated a significantly enhanced detection sensitivity (∼107 particles/mL) and, more importantly, systematically revealed the heterogeneity widely present in diverse EV glycan signatures. Moreover, its excellent performance in mock samples, particularly at low target EV concentrations, conclusively substantiated the assay's capability for directly distinguishing specific EV glycan signatures from complex biological backgrounds. With its advantages of user-friendly operation, minimal requirement for complex instrumentation, high accessibility, and good practicality in complex samples, this assay is envisioned to offer a robust deciphering tool for deepening the understanding of EV glycoscience and advancing EV glycan-based biomedical applications.
Yan et al. (2026) studied this question.