Autonomous AI-Driven Nanoscale Spatial Mapping Reveals Novel Targets and Ternary Architectures in 5xFAD Alzheimer's Disease Model

Abstract

Alzheimer's disease (AD) is characterized by the deposition of amyloid-beta (A{beta}) and microtubule-associated protein tau (MAPT) neurofibrillary tangles in the brain; however, the molecular mechanisms underlying associated synaptic dysfunction remain unclear. Eratos' AI for Spatial Computing and Embedded Neurotherapeutic Discovery (ASCENDTM) engine was employed to analyze multiplexed expansion revealing (multiExR) data from the 5xFAD and wild-type mouse somatosensory cortex, enabling quantitative mapping of A{beta}, RIM1, and GluA2 nanodomains. ASCENDTM identified significant and previously unreported protein associations, including nanoscale colocalization of A{beta} with postsynaptic AMPA-receptor subunit GluA2 and amyloid-bridged A{beta}-GluA2-RIM1 ternary assemblies. These spatial signatures indicate complex synaptic disruption, defined by distinct morphological and density profiles associated with neurobiological and neuroinflammatory pathology. Integrating high-dimensional spatial computing with cross-modal literature synthesis advances traditional microscopy analysis toward autonomous, AI-driven scientific discovery. Identifying novel, druggable interfaces within native tissue expedites the discovery of precision neurotherapeutics for AD and other complex central nervous system disorders.