Digital Decoding of Multicomponent Protein Systems via Nanocavity-Confined Single-Molecule Raman Fingerprinting

Area of Science:

• Biophysics
• Analytical Chemistry
• Spectroscopy

Background:

• Identifying individual proteins in complex biological systems is crucial for molecular diagnostics and proteomics.
• Current methods face challenges in specificity and sensitivity for single-molecule analysis.

Purpose of the Study:

• To develop a single-molecule Raman fingerprinting strategy for automatic digital decoding of protein compositions in complex systems.
• To establish a high-throughput platform for multiplexed protein analysis under ambient conditions.

Main Methods:

• Utilized a dual-amplified, interface-coupled plasmonic nanocavity architecture integrating gap-mode coupling and surface plasmon resonance.
• Achieved single-protein confinement and isolation within highly enhanced hotspots for intrinsic Raman spectra acquisition.
• Employed a customized machine learning algorithm trained on single-molecule Raman datasets for protein identification.

Main Results:

• Successfully performed high-throughput hyperspectral Raman fingerprinting of seven representative proteins.
• Demonstrated automatic identification and spatial mapping of individual protein species.
• Generated quantitative and addressable decoding maps of protein compositions.

Conclusions:

• The developed strategy enables intelligent, data-driven, multiplexed protein analysis.
• This approach has significant implications for next-generation molecular diagnostics, biosensing, and protein function studies.