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A 3D Nanofiltration Array for Direct Plasma EV Purification with Label-Free SERS Detection toward Accurate Clinical
Tingyu Wang1, Kai Zhu1, Jinxiu Wei1
1Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University Nanjing 210096, China.
ACS Sensors
|September 2, 2025
Summary
A novel PANA-chip enables size-selective isolation of extracellular vesicles (EVs) from plasma for label-free cancer diagnostics. This method achieves high accuracy in detecting cancer and monitoring disease progression using minimal samples.
Area of Science:
- Biomedical Engineering
- Nanotechnology
- Cancer Diagnostics
Background:
- Extracellular vesicles (EVs) are crucial biomarkers for cancer diagnostics.
- Isolating EVs from plasma is challenging due to interfering biomolecules that reduce detection sensitivity.
- Existing methods often require complex procedures or labeling.
Purpose of the Study:
- To develop a method for rapid, nondestructive isolation of EVs from plasma.
- To enable sensitive, label-free detection of EVs using Surface-Enhanced Raman Spectroscopy (SERS).
- To achieve accurate cancer diagnostics and monitoring of disease progression.
Main Methods:
- Development of a 3D plasmonic nanosphere array chip (PANA-chip) for size-selective EV isolation.
- Utilizing capillary action and size exclusion during plasma droplet evaporation for EV separation.
- In situ label-free SERS analysis coupled with a quadratic support vector machine algorithm for data interpretation.
Main Results:
- The PANA-chip successfully isolated EVs nondestructively from plasma interferents.
- Achieved 99.6% accuracy in distinguishing cancer patients from healthy individuals.
- Attained 85.3% accuracy in identifying different stages of breast cancer (0-III) in clinical samples.
- Demonstrated direct, label-free identification of EVs without complex extraction or exogenous materials.
Conclusions:
- The PANA-chip offers an efficient and sensitive platform for EV isolation and analysis.
- This technology shows significant potential for early cancer screening and monitoring of tumor progression.
- The label-free, direct detection approach simplifies EV-based diagnostics.
