Characterizing Shared Features of Innate Immune Cells across Neurodegenerative Diseases using Single Cell Expression and Chromatin Accessibility Data

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With few effective interventions available and over 10 million patients affected, neurodegenerative diseases are an area of intense basic science and clinical research. Inspired by Genome Wide Association Studies (GWAS) that have identified many risk variants linked to immune genes, neurobiologists are just beginning to understand the inflammatory basis for neurodegeneration. Across degenerative conditions, such as Alzheimer’s Disease (AD) and Progressive Multiple Sclerosis (MS), computational techniques applied to single cell datasets are identifying the role of immune cells in driving pathological changes in the brain. For instance, recent single cell expression studies in AD have identified a novel type of Disease Associated Microglia (DAM) associated with the disease. Preliminary analysis I performed on single cell expression data produced from retinal tissue of patients suffering from Age-related Macular Degeneration (AMD) recapitulated this DAM phenotype in AMD- derived microglia. Furthermore, analysis revealed that AMD-derived astrocytes drive neovascularization, a pathologic hallmark of AMD, through the increased expression of VEGF. These findings imply that neurodegeneration and pathologic changes in AMD are driven by innate immune cells, and, further, that these innate immune cell functions may be similar across neurodegenerative diseases. I hypothesize that innate immune cell function and regulation that drives pathology is shared across neurodegenerative conditions. To identify these shared features, I will design and apply novel computational algorithms to single cell datasets from multiple neurodegenerative diseases - AMD, AD and MS - to elucidate the role of innate immune cells across conditions. In aim 1, I will apply a coarse graining algorithm, Diffusion Condensation, that clusters cells at all levels of granularity to identify pathologic microglial and astrocyte subsets in single cell expression data produced from retinal tissue of patients with AMD. I will further apply this technique to subset these innate immune cells in publicly available single cell expression datasets in MS and AD in order to identify gene modules shared among microglia and astrocytes across diseases. In aim 2, I will apply a multi-modal data alignment algorithm, Harmonic Alignment, that integrates single cell expression and chromatin accessibility data to produce a rich, joint expression and accessibility profile for every cell to identify epigenetic regulators of expression. When used to integrate datasets derived from AMD patients and controls, this algorithm will be able to identify chromatin regions and candidate transcription factors that regulate the expression of genes key to microglial and astrocytic dysfunction. By overlapping our knowledge of GWAS risk alleles from AMD, AD and MS, on top of predicted epigenetic regulators of innate immune cell dysfunction in a neurodegenerative context, I hope to be able to elucidate the effect of risk variants in inducing disease in a cell-type specific manner. These projects will help identify shared genomic and regulatory mechanisms in innate immune cells across neurodegenerative diseases and will help identify common pathways for future therapeutic development.