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Updated: Jul 17, 2025

Isolation and Analysis of Traceable and Functionalized Extracellular Vesicles from the Plasma and Solid Tissues
Published on: October 17, 2022
Profiling extracellular vesicle surface proteins with 10 µL peripheral plasma within 4 h
Jie He1,2, Hengyu Li3, John Mai4
1Department of Anesthesiology and Surgical Intensive Care Unit School of Medicine and School of Biomedical Engineering, Xinhua Hospital, Shanghai Jiao Tong University, Shanghai, China.
This study introduces MESS2CAN, a novel method for detecting cancer biomarkers on extracellular vesicles (EVs). This technique enables sensitive and rapid diagnosis using trace amounts of EVs from clinical samples.
Area of Science:
- Biochemistry
- Molecular Biology
- Nanotechnology
Background:
- Extracellular vesicle (EV) surface proteins are crucial biomarkers for early cancer detection.
- Detecting these EV proteins is challenging due to low abundance and interference in clinical samples.
Purpose of the Study:
- To develop a sensitive and rapid method for direct detection of EV surface proteins.
- To establish a diagnostic tool for analyzing cancer biomarkers on EVs.
Main Methods:
- A MEmbrane-Specific Separation and two-step Cascade AmpLificatioN (MESS2CAN) strategy was developed.
- Utilized novel lipid probes and streptavidin-coated magnetic beads for EV recovery (49.6% in 1 hour).
- Employed a dual amplification strategy (primer exchange reaction cascaded by Cas12a) for sensitive protein detection (10 EV particles/µL).
Main Results:
- MESS2CAN achieved high EV recovery and sensitive detection of target proteins.
- Demonstrated successful analysis of HER2, EpCAM, and EGFR on EVs from cell lines and patient plasma.
- Achieved high specificity and sensitivity for EGFR (AUC=0.98) and HER2 (AUC=1) in discriminating breast cancer subtypes and healthy donors.
Conclusions:
- MESS2CAN is a pioneering method for highly sensitive in vitro EV diagnostics.
- The method is applicable to clinical samples with trace amounts of EVs.
- MESS2CAN offers a promising approach for early cancer diagnosis through EV surface protein analysis.
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