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Updated: Jun 17, 2026

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
Published on: March 17, 2023
An Aptamer-Engineered Phenotyping System for Extracellular Vesicles Based on Honeycomb-like Melamine-Formaldehyde
Jing Li1,2, Liang Xu1,2, Hao Fan1,2
1Department of Oncology, Xiangyang Central Hospital, affiliated hospital of Hubei University of Arts and Science, Xiangyang 441021, China.
Abstract:
Molecular phenotyping of extracellular vesicles (EVs) holds promise for noninvasive cancer diagnosis; however, current methodologies encounter limitations in cost, throughput, and sensitivity. Herein, we developed an aptamer-engineered phenotyping system designed for multiplexed, cost-effective, and user-friendly detection of EV membrane proteins, utilizing a sensing array constructed from two tailored functional materials. Initially, amino-rich, honeycomb-like melamine-formaldehyde microspheres were functionalized with CD63 aptamers to facilitate efficient EV capture. Subsequently, iron-doped carbon dot nanozymes with high peroxidase-like activity were conjugated to aptamers targeting CD63, HER2, MUC1, and PD-L1, thereby providing specific recognition and colorimetric signal amplification. The working principle of the system was thoroughly validated by multiple techniques, including nanoflow cytometric analysis. The system exhibited a wide linear detection range (103-107 particles/mL) and high capture efficiency (∼4.5 × 107 particles/mg), and successfully distinguished EVs from eight cell lines, comprising two normal and six cancerous types. In clinical validation, the system effectively differentiated early stage breast or pancreatic cancer patients from healthy controls using only 50 μL of 1000-fold diluted plasma (equivalent to 50 nL neat plasma) without ultracentrifugation, with diagnostic performance surpassing traditional serum biomarkers. This work presents a practical EV phenotyping system that integrates capture, recognition, signal amplification, and machine-learning classification, offering a promising liquid-biopsy approach for early cancer screening.

