Biomimetic 3D Recognition with 2D Flexible Nanoarchitectures for Ultrasensitive and Visual Extracellular Vesicle Detection

Despite increasing recognition of extracellular vesicles being important circulating biomarkers in disease diagnosis and prognosis, current strategies for extracellular vesicle detection remain limited due to the compromised sample purification and extensive labeling procedures in complex body fluids. Here, we developed a 2D magnetic platform that greatly improves capture efficiency and readily realizes visible signal conversion for extracellular vesicle detection. The technology, termed high -affinity recognition and visual extracellular vesicle testing (HARVEST), leverages 2D flexible Fe3O4-MoS2 nanostructures to recognize extracellular vesicles through multidentate affinity binding and feasible magnetic separation, thus enhancing the extracellular vesicle capture performance with both yield and separation time, affording high sensitivity with the detection limit of 20 extracellular vesicle particles/mu L. Through integration with lipid labeling chemistry and the fluorescence visualization system, the platform enables rapid and visible detection. The number of extracellular vesicles can be feasibly determined by smart mobile phones, readily adapted for point-of-care diagnosis. When clinically evaluated, the strategy accurately differentiates melanoma samples from the normal cohort with an AUC of 0.98, demonstrating the efficient extracellular vesicle detection strategy with 2D flexible platforms for cancer diagnosis.

Automated summary

The study developed a novel 2D magnetic platform, named HARVEST, for improving the capture efficiency of extracellular vesicles and realizing visible signal conversion, enhancing the detection performance of extracellular vesicles. The number of extracellular vesicles can be rapidly determined through smartphones, providing strong support for cancer diagnosis.