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A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
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Living breast cancer subtype classification by membrane-interfacing 3D surface-enhanced Raman spectroscopy substrates

Hyeim Yu1, Xiang Ren2, Wonil Nam1

  • 1Department of Electronic Engineering, Pukyong National University, Busan 48513, Republic of Korea.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|October 18, 2025
PubMed
Summary
This summary is machine-generated.

We developed a novel 3D nanolaminate substrate for surface-enhanced Raman spectroscopy (SERS) to analyze living breast cancer cells. This advanced SERS platform enables accurate classification of cancer subtypes, offering new possibilities for drug response studies.

Keywords:
Cancer diagnosticsLiving cancer cellsMulti-class classificationNano-bio interfaceSurface-enhanced Raman spectroscopy

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Area of Science:

  • Biomedical Engineering
  • Spectroscopy
  • Nanotechnology

Background:

  • Conventional surface-enhanced Raman spectroscopy (SERS) for living cells faces challenges like cytotoxicity and limited signal capture.
  • Existing SERS substrates yield sparse data, restricting complex biological analyses and machine learning applications.

Purpose of the Study:

  • To develop an advanced 3D multilayer nanolaminate SERS substrate for label-free analysis of living breast cancer cells.
  • To overcome limitations of current SERS methods for high-throughput, information-rich cellular analysis.

Main Methods:

  • Fabrication of a 3D multilayer nanolaminate SERS substrate using gold and silica (Au/SiO₂).
  • High-speed, high-throughput, label-free SERS mapping of living breast cancer cells.
  • Application of conventional machine learning algorithms for subtype classification.

Main Results:

  • Acquisition of large, information-rich spectral datasets from living breast cancer cells.
  • Successful classification of four breast cancer subtypes with 92.5% accuracy.
  • Demonstration of a label-free SERS platform for complex cellular analyses.

Conclusions:

  • The developed 3D nanolaminate SERS substrate effectively overcomes limitations of conventional methods for living cell analysis.
  • This platform shows significant potential for advanced machine learning applications in cancer research and drug response studies.