Ultrasensitive Detection of Tumor-Derived Extracellular Vesicle microRNAs via Aptamer-Driven Targeted Membrane Fusion and Nonenzymatic Amplification

Extracellular vesicles (EVs) have emerged as a prominent and rapidly evolving class of biomarkers, garnering an increasing amount of attention in recent years. The analysis of diverse EV-derived biomolecules, including proteins, mRNA, and miRNA, has driven the development of numerous innovative analytical technologies for EV detection. Nevertheless, despite significant advancements, most current EV analysis methods still require substantial improvements to address critical limitations, such as operational complexity, insufficient sensitivity, and the persistent issue of false-positive signals. In this study, we present a targeted fusion and nonenzymatic amplification strategy for ultrasensitive detection of tumor-derived EV miRNAs. The proposed approach integrates an aptamer-anchored liposome system that facilitates specific recognition and fusion with tumor EVs, coupled with a spatial confinement nonenzymatic signal amplification system to significantly enhance detection sensitivity. In conjunction with flow cytometry, the platform demonstrates a detection limit of 4 ng/mL for EV- associated proteins (0.2 fM for EV miR-21) and effectively discriminates cancer patients from healthy controls. This innovative strategy shows substantial promise for diverse biomedical applications, potentially revolutionizing early cancer diagnosis, metastasis monitoring, and prognostic evaluation while also offering opportunities for the detection and management of various other diseases.

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Publication Details

Journal
Analytical Chemistry
Published
2026-07-26
DOI
https://doi.org/10.1021/acs.analchem.6c02962
Primary Topic
Extracellular vesicles in disease
Type
article
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article

Ultrasensitive Detection of Tumor-Derived Extracellular Vesicle microRNAs via Aptamer-Driven Targeted Membrane Fusion and Nonenzymatic Amplification

Yanling Song, Sinong Liu, Ruixiao Peng, Wenhai Tian et al.
Analytical Chemistry
Extracellular vesicles in disease
article

Ultrasensitive Detection of Tumor-Derived Extracellular Vesicle microRNAs via Aptamer-Driven Targeted Membrane Fusion and Nonenzymatic Amplification

Yanling Song, Sinong Liu, Ruixiao Peng, Wenhai Tian, Chaoyong James Yang, Yuqian Zhang, Liang Cui, X Xu, Weirong Xiao
article en

Abstract

Extracellular vesicles (EVs) have emerged as a prominent and rapidly evolving class of biomarkers, garnering an increasing amount of attention in recent years. The analysis of diverse EV-derived biomolecules, including proteins, mRNA, and miRNA, has driven the development of numerous innovative analytical technologies for EV detection. Nevertheless, despite significant advancements, most current EV analysis methods still require substantial improvements to address critical limitations, such as operational complexity, insufficient sensitivity, and the persistent issue of false-positive signals. In this study, we present a targeted fusion and nonenzymatic amplification strategy for ultrasensitive detection of tumor-derived EV miRNAs. The proposed approach integrates an aptamer-anchored liposome system that facilitates specific recognition and fusion with tumor EVs, coupled with a spatial confinement nonenzymatic signal amplification system to significantly enhance detection sensitivity. In conjunction with flow cytometry, the platform demonstrates a detection limit of 4 ng/mL for EV- associated proteins (0.2 fM for EV miR-21) and effectively discriminates cancer patients from healthy controls. This innovative strategy shows substantial promise for diverse biomedical applications, potentially revolutionizing early cancer diagnosis, metastasis monitoring, and prognostic evaluation while also offering opportunities for the detection and management of various other diseases.

Analytical Chemistry
Zhejiang Sci-Tech University (CN), Shanghai Jiao Tong University (CN), Xiamen University (CN), Renji Hospital (CN), Ingenierie des Materiaux polymeres (FR), Institute of Molecular Medicine (IN), Collaborative Innovation Center of Chemistry for Energy Materials (CN), Interface (United Kingdom) (GB)
Reduced inequalities
Openalex Percentile: Top 14%
Extracellular vesicles in disease
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