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Updated: Jan 21, 2026
11:53
Paper-based Devices for Isolation and Characterization of Extracellular Vesicles
Published on: April 3, 2015
11.9K
Efficient isolation and sensitive quantification of extracellular vesicles based on an integrated ExoID-Chip using
Xing Dong1, Junjie Chi1, Liuzheng Zheng1
1State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China. czhao86@seu.edu.cn liuh@seu.edu.cn.
Lab on a Chip
|August 1, 2019
Summary
This study presents a microfluidic chip for isolating and detecting extracellular vesicles (EVs) for cancer diagnosis. The novel chip offers ultrasensitive detection, enabling early cancer pre-screening from patient serum samples.
Area of Science:
- Biomedical Engineering
- Nanotechnology
- Cancer Diagnostics
Background:
- Extracellular vesicles (EVs) are crucial in disease and potential cancer biomarkers.
- Efficient isolation and ultrasensitive detection of EVs remain significant challenges.
Purpose of the Study:
- To develop an integrated microfluidic chip for efficient isolation and ultrasensitive detection of EVs.
- To enable early cancer pre-screening using a novel EV detection method.
Main Methods:
- Integrated chip with double-filtration for EV isolation (20-200 nm) based on size-exclusion.
- CD63 aptamer-based capture and detection using a photonic crystal (PC) nanostructure.
- Competitive assay utilizing PC nanostructure fluorescence enhancement for EV quantification.
Main Results:
- Achieved a limit of detection as low as 8.9 × 10^3 EVs/mL with minimal sample volume (20 μL).
- Successfully distinguished between serum samples from breast cancer patients and healthy donors.
- Demonstrated high sensitivity and specificity for EV detection.
Conclusions:
- The developed microfluidic chip provides an effective and sensitive method for EV isolation and detection.
- This technology holds promise for early cancer pre-screening in clinical settings.
- The integrated chip addresses current limitations in EV biomarker analysis.
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