Extracellular vesicles (EVs) have emerged as promising biomarkers in cancer diagnostics. However, rapid and nondestructive isolation of EVs from plasma remains challenging due to the presence of abundant interferents with smaller sizes (e.g., proteins, inorganic salts), particularly as these soluble components can mask the label-free SERS signals of EVs and compromise detection sensitivity. To address this, we developed a three-dimensional (3D) plasmonic AgNP@Au@PS nanosphere array chip (PANA-chip) capable of size-selective, nondestructive EV isolation from abundant plasma interferents, coupled with in situ label-free SERS analysis. During plasma droplet evaporation, the PANA-chip employs capillary action and size exclusion in its nanoporous nanosphere array to efficiently and nondestructively separate EVs from background biomolecules. In model validation experiments, our method requires only 5 μL EV samples and allows accurate typing of breast cancer subtypes combined with a quadratic support vector machine algorithm. Notably, the use of the PANA-chip resulted in excellent diagnostic accuracy in the processing of clinical samples. An accuracy of 99.6% was achieved in the classification of cancer and healthy individuals, while 85.3% accuracy was achieved in the identification of healthy individuals and stage 0, stage I, stage II, and stage III breast cancer samples. Our method enables direct, label-free identification of EVs without complex extraction steps or exogenous materials. It effectively discriminates tumor-derived EVs across disease progression stages, demonstrating a strong potential for clinical applications in early cancer screening and dynamic progression monitoring.
Wang et al. (Tue,) studied this question.
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