Spectral Fingerprinting of Engineered Nanomaterials for Precision Biosensing.

Abstract

Biological systems comprise a complex milieu of macromolecules, small molecules, and ions comprising tens of thousands of distinct species. Various clinical conditions alter the identities and concentrations of these species in a spatiotemporal-dependent manner. While bioanalytical methods such as omics or biochemical assays can precisely identify the targeted biomolecules over space and time, providing in-depth information on biological processes, they are generally considered low-throughput and costly. Spectral fingerprinting of engineered nanomaterials (SFEN) has emerged as an alternative method that addresses many of these limitations in the field of disease detection and chemical biology research. This approach leverages one or more closely related types of engineered nanomaterials to detect subtle biological differences via optical readout such as near-infrared fluorescence or surface-enhanced Raman spectroscopy. Variations of the technique have been developed to detect single or multiplexed target biomarkers as well as whole-cell- and organism-level biological states. In recent years, the incorporation of advanced analytical methods, such as feature extraction and machine learning, has significantly expanded the SFEN capabilities for broader applications with high accuracy. This perspective highlights recent developments of SFEN applications including but not limited to machine-learning-assisted live-cell phenotyping, serum-based cancer detection, and pathogen identification. We further comment on the future directions of this promising technology, which we envision will synergize with next-generation nano-omics and generative AI methods.