Deep Learning‐Powered Plasmonic Platform for Decoding Dynamic Protein Aggregation Landscapes in Parkinson's Disease Progression

ABSTRACT In‐depth characterization of pathological protein aggregate with its subcomponents is pivotal for early diagnosis and staging of neurodegenerative disorders. Here, we introduce the Protein Aggregate NanoDynamics Analyzer (PANDA), a plasmonic nanotechnology‐powered system that translates the supramolecular size distribution of protein aggregates into distinct scattering signatures via sterically constrained immunogold clustering. In this work, PANDA enabled the profiling of α‐synuclein (α‐syn) aggregates with diverse supramolecular architectures from human serum samples and experimentally revealed biological stage‐dependent distribution patterns in Parkinson's disease (PD). Aggregate size profiles from PANDA readouts showed strong correlation with clinical scores ( r max = 0.6) and dopaminergic PET imaging ( r max = 0.7). Notably, it also confirmed the pathophysiological transition of “oligomer‐to‐fibril” in the course of PD progression. To leverage this transition, we incorporated a deep neural network (DNN) to classify PD stages. The network achieved high accuracy and enabled an objective reference to evaluate PD progression. PANDA system thus offers a noninvasive, artificial intelligence (AI)‐augmented framework for molecular diagnosis and stratification of neurodegenerative diseases such as PD.