4.8 Article

Membrane Feature-Inspired Profiling of Extracellular Vesicles for Pancreatic Cancer Diagnosis

Journal

ANALYTICAL CHEMISTRY
Volume 93, Issue 28, Pages 9860-9868

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.1c01712

Keywords

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Funding

  1. National Natural Science Foundation of China (NSFC) [91859204, 81729002, 81727804]
  2. Open Project of State Key Laboratory of Natural Medicines [SKLNMZZCX201819]
  3. Class Developing Project of China Pharmaceutical University [CPU2018CY24]

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Extracellular vesicles (EVs) have emerged as a promising tumor biomarker, offering benefits for cancer diagnosis, but the practicality of EV assays remains challenging due to various factors. A membrane-based biosensor has been developed for precise and sensitive EV identification, integrating EV capture and detection based on EV membrane features, showing great potential for clinical prostate cancer diagnosis.
Extracellular vesicles (EVs) have recently emerged as a promising tumor biomarker, and EV phenotyping offers many benefits for cancer diagnosis. However, the practicality of EV assays remains a challenge due to macromolecule disturbances, biomarker heterogeneities, and EV abundance limitations. Here, we demonstrate a membrane-based biosensor for precise and sensitive EV identification. The sensor synergistically integrates EV capture and detection by virtue of EV membrane features (membrane protein and lipid bilayer), comprising antibody-conjugated magnetic beads (AbMBs) and duplex-specific nuclease (DSN)-mediated amplification cycles. Bivalent cholesterol (biChol)modified RNA-DNA duplexes are designed to insert into the EV membrane, transforming EV signals into RNA signals and initiating the signal amplification. The membrane-based signal production pattern eliminates protein interference. By employing four antibodies specific to PCa-related membrane proteins, the AbMB-biChol platform enables the successful differentiation and monitoring of PCa-related EVs and distinguishes PCa patients from healthy donors with improved efficacy, exhibiting superior efficiency over the analyses based on clinically used biomarker CA19-9 and PCa-related proteins. As such, the developed system has great potential for clinical PCa diagnosis.

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