Label-Free SERS Fingerprinting of Neuroprotein Conformational Dynamics in Human Saliva.

Abstract

Conformational transitions of neuroproteins are closely associated with neurological disorders and represent key biomarkers for diagnosis and disease monitoring. A reliable and label-free method for detecting and tracking these structural changes is critical for effective clinical application. In this work, a galvanic molecular entrapment (GME) strategy is presented to combine with surface-enhanced Raman scattering (SERS) for localization and label-free detection of neuroproteins. This approach integrates galvanic replacement with in situ Au surface growth to enable precise molecular entrapment and plasmonic hotspot formation directly around target analytes. Such spatial confinement optimizes analyte positioning within the electromagnetic field, thereby enhancing SERS signal intensity and overcoming geometric mismatches that typically limit sensitivity. The GME method successfully profiled neuroprotein conformational states and demonstrated strong potential for proteomic analysis. A logistic regression model applied to the spectral dataset enabled accurate classification of saliva samples from individuals with epilepsy, schizophrenia, Parkinson's disease, and healthy controls, achieving high sensitivity, specificity, and diagnostic accuracy. This integrated platform offers a non-invasive tool for neurological diagnostics and holds significant promise for advancing personalized healthcare in the management of neurological disorders.