Efficient EVs separation and detection by an alumina-nanochannel-array-membrane integrated microfluidic chip and an antibody barcode biochip

Background: Small endosome-derived lipid nanovesicles (30-200 nm) are actively secreted by living cells and serve as pivotal biomarkers for early cancer diagnosis. However, the study of extracellular vesicles (EVs) requires isolation and purification from various body fluids. Although traditional EVs isolation and detection technologies are mature, they usually require large amount of sample, consumes long-time, and have relatively low throughput. How to efficiently isolate, purify and detect these structurally specific EVs from body fluids with high-throughput remains a great challenge in in vitro diagnostics and clinical research. Results: Herein, we suggest a nanosized microfluidic device for efficient and economical EVs filtration based on an alumina nanochannel array membrane. We evaluated the filtration device performance of alumina membranes with different diameters and found that an optimized chamber array with a hydrophilic-treated channel diameter of 90 nm could realize a filtration efficiency of up to 82% without any assistance from chemical or physical separation methods. Importantly, by integrating meticulously designed multichannel microfluidic biochips, EVs can be captured in-situ and monitored by antibody barcode biochip. The proposed filtration chip together with the high-throughput detection chip were capable of filtration of a few tens of mu L samples and recognition of different phonotypes. The practical filtration and detection of EVs from clinical samples demonstrated the high performance of the device. Significant: Overall, this work provides a cost-effective, highly efficient and automated EVs filtration chip and detection dual-function integrated chip platform, which can directly separate EVs from serum or cerebrospinal fluid with an efficiency of 82% and conduct in-situ detection. This small fluidic device can provide a powerful tool for highly efficient identifying and analyzing EVs, presenting great application potential in clinical detection.