Autonomous AI-Driven Nanoscale Spatial Mapping Reveals Novel Targets and Ternary Architectures in 5xFAD Alzheimer's Disease Model
Year: 2026
Type: preprint
Abstract: Alzheimer's disease (AD) is characterized by the deposition of amyloid-beta (Aβ) 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 Aβ, RIM1, and GluA2 nanodomains. ASCENDTM identified significant and previously unreported protein associations, including nanoscale colocalization of Aβ with postsynaptic AMPA-receptor subunit GluA2 and amyloid-bridged Aβ-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.
Institutions: University of California San Diego, University of California, San Francisco, Arizona State University, University of California, Berkeley, University of California, Los Angeles, Kent State University, Wayne State University, University of South Carolina System, Massachusetts Institute of Technology, University of California, Irvine
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7213289157