Surface-Enhanced Raman Scattering Nanotags: Design Strategies, Biomedical Applications, and Integration of Machine Learning

Abstract

Surface-enhanced Raman scattering (SERS) is a transformative technique for molecular identification, offering exceptional sensitivity, signal specificity, and resistance to photobleaching, making it invaluable for disease diagnosis, monitoring, and spectroscopy-guided surgeries. Unlike traditional Raman spectroscopy, which relies on weak scattering signals, SERS amplifies Raman signals using plasmonic nanoparticles, enabling highly sensitive molecular detection. This technological advancement has led to the development of SERS nanotags with remarkable multiplexing capabilities for biosensing applications. Recent progress has expanded the use of SERS nanotags in bioimaging, theranostics, and more recently, liquid biopsy. The distinction between SERS and conventional Raman spectroscopy is highlighted, followed by an exploration of the molecular assembly of SERS nanotags. Significant progress in bioimaging is summarized, including in vitro studies on 2D/3D cell cultures, ex vivo tissue imaging, in vivo diagnostics, spectroscopic-guided surgery for tumor margin delineation, and liquid biopsy tools for detecting cancer and SARS-CoV-2. A particular focus is the integration of machine learning (ML) and deep learning algorithms to boost SERS nanotag efficacy in liquid biopsies. Finally, it addresses the challenges in the clinical translation of SERS nanotags and offers strategies to overcome these obstacles.

Keywords: SERS nanotags; biosensing; machine learning; surface‐enhanced Raman scattering; surgical interventions.