Topographic, cell type and molecular pathway characterization ofAlzheimer's disease using single cell transcriptomics and epigenomics

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ABSTRACT (PROJECT 2) Alzheimer's disease (AD) is a highly penetrant neurodegenerative disease projected to affect 13.8 million cases in the US by 2050 at a cost of $1.1 trillion if no treatment is developed. AD is characterized by stereotyped progressive neurodegeneration and accumulation of two misfolded proteins in brain regions important for cognition and memory. Neurofibrillary tangles (NFTs) of hyperphosphorylated tau follow a progression like neurodegeneration, while extracellular amyloid beta (Aβ) plaques are initially detected in cortical and deep brain structures. It is unclear whether these pathologies are causal or effects of other underlying processes and currently no anti-tau or anti- Aβ therapies stop or reverse AD. Gene expression studies of AD have largely been performed on tissue or cell populations, and impact of neuronal loss and gliosis on these results is unknown. Epigenetic modifications are also associated with AD, though methylation studies have produced conflicting results and no clear pattern of epigenetic dysregulation associated directly with AD progression has emerged. The present study adapts recently developed high-throughput, single-cell methods for transcriptomic and epigenetic analysis to the identify molecular and gene regulatory hallmarks of “clinically typical” AD without significant co-morbidities. Building off a detailed understanding of neurotypical adult cell types, the project aims to identify transcriptional changes in specific cell types or classes correlated with increasing severity of AD pathology in different brain regions affected by the disease, and then identify gene and chromatin accessibility changes with pathology in vulnerable cell populations. This project will initially optimize single nucleus RNA-seq and epigenetics methods for use with postmortem samples of varying pathology and tissue quality, and generate reference datasets for brain regions to be analyzed in AD. Low-cost, droplet-based single nucleus RNA-seq will then be used to classify and characterize cell types in regions differentially affected by tau and amyloid pathology from many donors spanning AD progression with quantified tau and Aβ pathologies. A broader set of brain regions will then be surveyed on a subset of cases with consistent AD-related phenotypes to understand whether there is a common AD signature across brain regions, and whether signatures of AD can be detected prior to the emergence of neuropathology. Finally, higher-resolution methods will target transcriptomic and epigenetic changes in AD associated with pathology and disease diagnosis in specific cell types, aimed at achieving a mechanistic understanding of AD phenotypes. Using this design, this project can directly probe dysregulated gene networks within affected cell types for the first time, providing a potential causal link between genetic or epigenetic states and resulting gene expression. The resulting datasets and platform will produce valuable insights into the cellular and molecular basis of AD and will be made publicly accessible through the Data Core.