ABSTRACT The current medical landscape is not equipped to handle Alzheimer’s Disease, with the projected afflicted population tripling between 2010 and 2050 and care costing the U.S. hundreds of billions of dollars each year. To better understand and diagnose the disease, attention has shifted towards biomarkers. The oligomeric forms of amyloid beta—which can be stabilized by metal ions—have been labeled one of the most destructive driving forces behind Alzheimer’s Disease. Mass spectrometry is a prominent force in the disease characterization, but due to the prominent noncovalent landscape of amyloid beta proteoforms, mass spectrometry must be applied in a way that preserves endogenous molecular context for maximal effect. Native top-down mass spectrometry improves on the information accessible from standard approaches by preserving inter- and intramolecular, noncovalent interactions. However, the approach is inaccessible in high throughput due to the required manual nature of data acquisition for large analytes. Another barrier to the characterization of noncovalent protein assemblies via mass spectrometry is the lack of software for the localization of labile modifications (e.g., metals). The proposed work will address the first obstacle through software that autonomously reconfigures the mass spectrometer to enhance each analyte’s transmission for characterization. The optimizations can happen in real time with the actual analyte during steady spray or in reference to a calibrant for high-throughput implementation. Neural networks built on previously collected data will allow for continual and lightweight model improvement. The second obstacle mentioned will be addressed through software that quantitatively identifies and places labile modifications in 1D and 3D space. The dual- software platform will enable rigorous screening and quantitation on amyloid beta and its oligomers, leading to data-driven conclusions on the spatiotemporal progression of Alzheimer’s Disease. Training will first take place via an internship at Thermo Fisher Scientific. While manipulating instrument hardware and software to enhance high-range signal transmission, the applicant will become proficient in instrument code and use. Then, the applicant will learn how to conduct top-down proteomics workflows at the Proteomics Center of Excellence while becoming proficient in bioinformatics software via Protinaceous. Each center is populated with high-impact scientists that engage in industry, which will prompt generalizable design. The proposed works align directly with the NIA’s mission in both their short-term and long-term implications. A platform will enable high-throughput, rigorous, and unprecedented characterization of amyloid beta and its oligomers within their native structural contexts. Furthermore, the created platform will be readily applicable to any other disease or biological system, tangential to the original research or not. The conclusions asserted using the proposed platform should inform understanding of Alzheimer’s Disease throughout progression and brain region, hopefully leading to better-informed treatments and a cure.

PROJECT NARRATIVE If successful, the proposed works will effectively map and quantify the spatiotemporal manifestation of Alzheimer’s Disease within a native context. Granular information on how the native proteome fluctuates across time and brain region will yield unprecedented accuracy in the clinical diagnosis of how far the disease has progressed. Furthermore, the uncovered landscape will direct future studies as to the origins and implications of significant proteoforms, proteoform complexes, and metal interactions in relevant biological pathways.