EVs‐on‐a‐Bubble: Self‐Aggregated Click Bubbles Streamline Isolation and Amplified Profiling of Circulating Extracellular Vesicles

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Abstract
Tumor‐derived circulating extracellular vesicles (EVs) provide a non‐invasive solution for cancer diagnostics, but hampered by challenges in EVs isolation and profiling. Herein, this work shows that bioorthogonal microbubbles (click bubbles) combine a panel of fluorescent aptamers for streamlined isolation and profiling of oncologic EVs in a single assay. This click bubble‐driven aptasensor (cBAS) features self‐aggregation, self‐separation and self‐enhancing in fluorescence. The facile protein phase transition renders bubble surface polyvalent with rich clickable motifs, enabling fast enrichment of EVs. Moreover, the buoyancy allows click bubbles to self‐float to the droplet apex for self‐aggregated fluorescence enhancement via a bubble lensing effect, thus achieving a sensitive profiling of EVs without requiring additional signal amplification or ultrasensitive detectors. By using cBAS to profile EVs surface proteins from a cohort (n = 45) across three cancer types, a machine learning algorithm enables cancer diagnosis and classification with an overall accuracy of 91%. This EVs‐on‐a‐bubble assay is fast, sensitive, self‐powered, and provides a promising tool to facilitate EVs‐based cancer diagnosis in clinical settings.
Funding Information
  • National Natural Science Foundation of China (21974037, 22264007, 22104020)
  • China Postdoctoral Science Foundation (2021MD703814)

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