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

使用单细胞表达和染色质可及性数据表征神经退行性疾病中先天免疫细胞的共同特征

• 批准号: 10527307
• 负责人: Manik Kuchroo
• 金额: $ 1.27万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-16 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10527307
• 关键词: Affect; Age related macular degeneration; Algorithms; Alzheimer' s Disease; Area; Astrocytes; Automobile Driving; Basic Science; Brain; Cell Nucleus; Cell physiology; Cells; Characteristics; Chromatin; Clinical Research; Computational Technique; Computational algorithm; Computing Methodologies; Data; Data Set; Degenerative Disorder; Development; Diffusion; Disease; Disease associated microglia; Drug Targeting; Epigenetic Process; Functional disorder; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genomics; Geometry; Grain; Human; Immune; Induced Mutation; Inflammation; Inflammatory; Intervention; Joints; Knowledge; Learning; Link; Machine Learning; Methods; Microglia; Modality; Multiple Sclerosis; Nerve Degeneration; Neuraxis; Neurodegenerative Disorders; Neurologist; Pathogenesis; Pathogenicity; Pathologic; Pathologic Neovascularization; Pathology; Pathway interactions; Patients; Phenotype; Physical condensation; Regulation; Retina; Retinal Degeneration; Role; Techniques; Therapeutic Intervention; Tissues; Vascular Endothelial Growth Factors; Work; age related neurodegeneration; base; cell type; computerized tools; design; drug development; effective intervention; epigenetic regulation; epigenomics; genetic signature; genome wide association study; multimodal data; multiple datasets; neovascularization; novel; risk variant; single cell analysis; therapeutic development; tool; transcription factor

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.

由于几乎没有有效的干预措施，超过1000万患者受到影响，神经退行性疾病 一个基础科学和临床研究密集的领域。受到基因组广泛关联研究(GWAS)的启发 已经确定了许多与免疫基因有关的风险变量，神经生物学家才刚刚开始了解 神经退行性变的炎症基础。各种退行性疾病，如阿尔茨海默病 (AD)和进行性多发性硬化症(MS)，应用于单细胞数据集的计算技术包括 确定免疫细胞在推动大脑病理变化中的作用。例如，最近的单元格 阿尔茨海默病的表达研究发现一种新类型的疾病相关小胶质细胞(DAM)与 这种疾病。视网膜组织单细胞表达数据的初步分析 患有老年性黄斑变性(AMD)的患者在AMD中概括了这种DAM表型。 衍生的小胶质细胞。此外，分析表明，AMD来源的星形胶质细胞驱动新生血管，一 AMD的病理标志，可能与血管内皮生长因子的表达增加有关。这些发现意味着 AMD的神经退行性变和病理变化是由先天性免疫细胞驱动的，而且，这些 神经退行性疾病的先天免疫细胞功能可能是相似的。我假设，与生俱来 推动病理的免疫细胞功能和调节在神经退行性变中是共享的 条件。为了识别这些共享功能，我将设计并将新的计算算法应用到Single 多种神经退行性疾病的细胞数据--AMD、AD和MS--以阐明先天的作用 各种条件下的免疫细胞。在目标1中，我将应用一种粗粒化算法，扩散凝聚，它 将所有粒度的细胞聚集在一起以识别单个细胞中病理的小胶质细胞和星形胶质细胞亚群 AMD患者视网膜组织的表达数据。我将进一步将该技术应用于子集 这些先天免疫细胞在公开可用的单细胞表达数据集中在MS和AD中进行识别 跨疾病的小胶质细胞和星形胶质细胞共享基因模块。在目标2中，我将应用多模式数据 整合了单细胞表达和染色质可及性数据的比对算法Harmonic Align 为每个细胞制作丰富的联合表达和可及性简档，以识别表观遗传调控因子 表情。当用于整合来自AMD患者和对照的数据集时，该算法将能够 确定染色质区域和候选转录因子，以调节关键基因的表达 小胶质细胞和星形胶质细胞功能障碍。通过重叠我们对来自AMD、AD和 MS，在神经退行性背景下，除了预测的先天免疫细胞功能障碍的表观遗传调节外， 我希望能够以一种特定的细胞类型方式来阐明风险变异在诱发疾病中的作用。这些 这些项目将有助于确定先天免疫细胞中共享的基因组和调控机制 这将有助于确定未来治疗发展的共同途径。