Biomineralized SERS Nanotags Encode Biomolecular Identity into Machine Learning-Resolvable Fingerprints

Surface-enhanced Raman scattering (SERS) nanotags provide highly sensitive platforms for molecular analysis but often require extensive customization to generate target-specific optical signatures. Biomineralization, in which biomolecules mediate inorganic material synthesis, offers a versatile yet underexplored strategy for generating functional SERS nanotags. Here, we demonstrate that biomolecule-directed biomineralization of gold nanoparticles (AuNPs) using amino acids and exosomes generates distinct nano-bio interfacial architectures that encode biomolecular identity into machine learning-resolvable SERS fingerprints through modulation of plasmonic coupling and Raman reporter organization. As a proof-of-concept system, amino acid-biomineralized AuNPs were synthesized using biomolecules with diverse physicochemical properties, including differences in size, polarity, and charge. The resulting nanotags were characterized using UV-vis spectroscopy, SERS spectroscopy, dynamic light scattering (DLS), and transmission electron microscopy (TEM). Random forest and support vector machine (SVM) models successfully differentiated amino acid-dependent SERS signatures with near-perfect classification performance. Extending this approach to a biologically complex preclinical cancer model, exosome-biomineralized AuNP nanotags were generated using exosomes derived from clinically relevant pediatric patient-derived osteosarcoma and neuroblastoma tumors. Distinct exosome-dependent spectral fingerprints enabled 85.9% classification accuracy through a regularized logistic regression model, while Shapley Additive exPlanations (SHAP) and t-distributed stochastic neighbor embedding (t-SNE) analyses identified diagnostically relevant spectral regions and visualized clustering between tumor classes. Collectively, this work establishes biomineralization as a strategy for encoding biomolecular identity into computationally resolvable optical fingerprints through engineered nano-bio interfaces, providing a versatile framework for machine learning-enabled classification of complex biological samples.