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Rapid Homogeneous Detection of Biological Assays Using Magnetic Modulation Biosensing System

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Published on: June 13, 2010

A programmable matrix-robust plasmonic MetaRing biosensor for rapid SERS-based chemotherapeutic response profiling.

Yue Fang¹, Guangyao Huang², Sensen Hao²

¹Hefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CAS), No. 350, Shushan Hu Road, Hefei, Anhui, China; University of Science and Technology of China, Hefei, China.

Biosensors & Bioelectronics

|March 21, 2026

Summary

A novel plasmonic biosensor, MetaRing, rapidly assesses chemotherapeutic response. This technology identifies drug sensitivity in various cancer samples, paving the way for precision oncology.

Keywords:

Chemotherapeutic drug sensitivity Coffee-ring effect Machine learning–assisted classification Programmable plasmonic nanoassembly SERS

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Last Updated: Mar 23, 2026

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Area of Science:

Nanotechnology
Biosensing
Precision Oncology

Background:

Assessing chemotherapeutic response is crucial for precision oncology but is challenged by tumor heterogeneity and complex biological samples.
Current methods often require significant biological material and time, limiting rapid clinical application.

Purpose of the Study:

To develop a programmable plasmonic biosensor (MetaRing) for rapid, label-free assessment of chemotherapeutic response.
To demonstrate MetaRing's robustness in diverse biological matrices and its ability to identify drug sensitivity fingerprints.

Main Methods:

Fabrication of MetaRing via dual regulation of nanoparticle concentration and evaporation temperature for deterministic nanoassembly.
Utilizing label-free surface-enhanced Raman spectroscopy (SERS) for profiling paclitaxel (PTX) response.
Integration with a 1D convolutional neural network for rapid classification of drug sensitivity.

Main Results:

MetaRing demonstrated exceptional matrix robustness in various buffers and cell lysates.
Distinct PTX-sensitivity spectral fingerprints were identified across cell lines, xenografts, and patient tissues.
Metabolomic analysis revealed metabolic reprogramming linked to chemoresistance.
The integrated AI model achieved >92% accuracy in classifying PTX sensitivity within 10 minutes.

Conclusions:

MetaRing is a robust, scalable plasmonic platform for rapid phenotypic drug response profiling.
The platform provides mechanistic insights into chemoresistance through metabolic pathway analysis.
MetaRing shows strong translational potential for precision oncology applications.