Core C: Spatial Multiomics Core

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SPATIAL MULTIOMICS CORE: PROJECT SUMMARY/ABSTRACT To investigate how disease relevant changes, such as Alzheimer’s disease, are linked to specific risk factors (e.g., age-related changes, protein aggregates, vascular pathologies, etc.) to alter the spatial arrangement of cell types in the brain and their gene expression profiles, we need to build high-resolution maps of brain tissue at the cellular level in individuals with and without pathology. Building these maps requires proper characterization of the pathological features, as well as a reproducible workflow for data generation and imaging. To this end, the Spatial Multiomics Core will work closely with the Biospecimen Core, the Integrated Computational Analysis Core, and the four Projects to provide standardized, high quality generation and integration of large-scale spatial molecular data. Thus, the overall proposal will directly address an unmet need in the field to provide state of the art, rigorous, spatially resolved transcriptomic and proteomic data acquisition and integration methods, applied to human postmortem tissue at scale. This Core encompasses the generation of this data required for the research in the four main Projects in this proposal. Specifically, this Core will perform spatially resolved transcriptomics (ST) and multiplexed proteomics using iterative indirect immunofluorescence imaging (4i) experiments in postmortem tissue spanning a range of ages and pathologies and age-matched controls. The overarching aims of this core are: i) to build and optimize robust workflows based on existing protocols, to thereby enable the generation of Project data at high throughput and scale (Aims 1 and 2); and ii) to adopt new protocols for imaging-based validation studies on fixed and frozen tissue, to thereby enable the integration of new technology into the multiomics workflow (Aim 3). The completion of these objectives will provide a standardized body of data and analysis workflows for the Projects in this proposal, setting the stage for the careful cell type and gene expression signature analyses carried out in all of these projects. Finally, this overall framework will ultimately provide a rigorously curated, large-scale data set that will enable novel understanding of how differing etiologies produce specific pathological features, how these relate to physiology in each CNS cell type, and how the dysfunction in the various assemblies of cell types within brain regions lead to differing clinical presentation.