Living breast cancer subtype classification by membrane-interfacing 3D surface-enhanced Raman spectroscopy substrates...

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.