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Updated: Aug 19, 2026

Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics
Published on: March 13, 2026
Logic-Gated Dual-Aptamer Proximity AlphaLISA for Rapid and Sensitive Detection of Tumor-Derived Small Extracellular
Shangbin Kao1, Nannan Tian1, Yaping Zhang1,2
1College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, Zhejiang310016, China.
Abstract:
Small extracellular vesicles (sEVs) have emerged as promising biomarkers for liquid biopsy; however, the limited specificity of single-marker detection and the intrinsic heterogeneity of circulating sEVs hinder their reliable clinical application. Herein, we develop a programmable dual-aptamer amplified luminescent proximity homogeneous assay (AlphaLISA) for rapid and wash-free detection of tumor-derived sEVs. In this strategy, EpCAM and PD-L1 aptamers simultaneously recognize two membrane proteins on the same vesicle, enabling proximity-induced interaction between donor and acceptor beads to generate a chemiluminescent signal via singlet oxygen-mediated energy transfer. This dual-recognition design effectively improves analytical specificity by implementing a molecular "AND" logic requirement for signal generation. The proposed assay exhibits a broad linear range of 5.3 × 105 to 5.3 × 109 particles mL-1 with a limit of detection of 1.63 × 104 particles mL-1. Good analytical performance was demonstrated by recovery rates of 93.9-107.5% with relative standard deviations below 5%, indicating high accuracy and reproducibility. Notably, the assay enables signal readout within 1 min under homogeneous conditions, highlighting its operational simplicity and rapid response. Clinical applicability was evaluated using serum samples from 20 lung cancer patients and 20 healthy controls, achieving an area under the ROC curve of 0.87 (95% CI: 0.75-0.97), with a sensitivity of 95% and a specificity of 75%. Overall, this programmable dual-aptamer AlphaLISA platform provides a rapid, homogeneous, and highly adaptable strategy for tumor-derived sEVs analysis, offering promising potential for noninvasive cancer diagnostics.
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