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

Abstract

Abstract The ability to identify individual protein species within multicomponent systems remains a major challenge, yet is essential for next‐generation molecular diagnostics and proteomic analysis. Here, we present a single‐molecule Raman fingerprinting strategy for automatic digital decoding of protein compositions in complex systems. A dual‐amplified, interface‐coupled plasmonic nanocavity architecture synergistically integrates gap‐mode coupling with surface plasmon resonance, generating ultralow‐volume, highly enhanced hotspots that reproducibly confine and isolate single proteins, enabling acquisition of intrinsic Raman spectra free from spectral overlap. Using this platform, we achieve high‐throughput hyperspectral Raman fingerprinting of seven representative proteins. A customized machine learning algorithm trained on single‐molecule Raman datasets enables automatic identification and spatial mapping of individual protein species, yielding quantitative and addressable decoding maps. This broadly applicable strategy establishes an intelligent, data‐driven framework for multiplexed protein analysis under ambient conditions, with far‐reaching implications for molecular diagnostics, biosensing, and mechanistic studies of protein function.