An All-in-One Nanohole Array for Size-Exclusive Trapping and High-Throughput Digital Counting of Single Extracellular Vesicles for Non-Invasive Cancer Screening.

The analysis of single small extracellular vesicles (sEVs) could distinguish the heterogeneity of sEVs thus better extract tumor-related signatures. Current protocols for the analysis of single sEV rely mainly on the advanced techniques and require lengthy isolation procedures, limiting applications in clinical diagnosis. Herein, we developed a one-step procedure for rapid isolation of single sEVs from urine, along with an analytical pipeline for the diagnosis of early bladder cancer (BCa). Single sEVs are isolated by an EV-imprinted gold nanohole (EI-AuNH) array that selectively traps individual sEVs and spatially enhances their Raman spectra. After the invalid spectral data from incomplete or absent sEVs was eliminated using Smart-Filter, a convolutional neural network model identifies the origin of the spectra and generates a digital count matrix for each patient. By integrating the digital count data of both tumor-associated and normal sEVs, our model achieves an accuracy of 97.37% in early diagnosis of BCa. Feature extraction using explainable AI identified nine BCa-related signatures, with noticeable reduction on cholesterol and lipids in BCa-associated sEVs. These signatures could further distinguish BCa from other cancers. Overall, the present non-invasive and highly accurate diagnosis platform may revolutionize clinical disease diagnostics through simplified single sEV isolation and advanced modeling.