Label-Free Small Extracellular Vesicles Capturing Strategy for Lung Cancer Diagnosis and Typing Based on a Natural Polyphenol–Metal Three-Dimensional Network

Small extracellular vesicles (sEVs) have been increasingly recognized as circulating biomarkers and prognosticators for disease diagnosis. However, the clinical applications of sEVs are seriously limited by the lack of a robust and easy scale-up isolation technique. Herein, the feasibility of a polyphenol–metal three-dimensional (3D) network for label-free sEV isolation was explored. As a proof-of-concept, with tannic acid (TA) as the polyphenolic ligand and Fe(III) as the coordinated metal, the TA-Fe(III) 3D network coating mesoporous silica beads (SiO2@BSA@Fe-TA6) was designed and fabricated via a coordination-driven layer-by-layer self-assembly approach. The successful fabrication of SiO2@BSA@Fe-TA6 was validated by Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, and thermogravimetric analysis. With the low-cost TA (as low as US$ 0.18/g) as the probe, SiO2@BSA@Fe-TA6 achieved universal capture toward sEVs in different cells and plasma samples. The capture efficiency reached 85.4 ± 1.5%, which is comparable to the antibody-based capture techniques and significantly higher than the ultracentrifugation (UC) method. The purity of sEVs isolated by SiO2@BSA@Fe-TA6 from the H1299 cell culture supernatant was measured as (1.07 ± 0.14) × 1011 particles/μg, which is 3.1 times higher than that via the UC method. Another important superiority of SiO2@BSA@Fe-TA6 is the facile self-assembly approach, which can harvest a yield of up to grams, allowing simultaneous processing of more than 500 plasma samples. The SiO2@BSA@Fe-TA6-based strategy was further successfully employed to distinguish nonsmall cell lung cancer (NSCLC) and small cell lung cancer (SCLC) with an accuracy of 87.1%. The developed SiO2@BSA@Fe-TA6 is a label-free, universal, low cost, and easy scale-up technique for sEV-based liquid biopsy in lung cancer diagnosis and typing.