Cell-type and organelle-specific multi-omics platform for the study of brain aging

用于研究大脑衰老的细胞类型和细胞器特异性多组学平台

• 批准号: 10626090
• 负责人: MICHAEL P CUMMINGS
• 金额: $ 69.39万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10626090
• 关键词: Address; Affect; Aging; Alzheimer' s disease related dementia; Bioinformatics; Biological Assay; Brain; CRISPR/Cas technology; Classification; Complex; Coupled; Data; Deposition; Development; Disease; Gene Expression Profile; Individual; Intervention; Investigation; Late Onset Alzheimer Disease; Lipids; Longevity; Lysosomes; Machine Learning; Mass Spectrum Analysis; Metabolic Pathway; Methodology; Methods; Microglia; Mitochondria; Mus; Nerve Degeneration; Neurodegenerative Disorders; Neurons; Organelles; Pathway interactions; Phenotype; Process; Proteins; Proteome; Proteomics; Publications; Pythons; Reporter Genes; Research Infrastructure; Research Personnel; Signal Transduction; Software Tools; Source Code; Testing; The Jackson Laboratory; Tissues; Transgenic Mice; Transgenic Organisms; age related; age related neurodegeneration; aging brain; brain dysfunction; cell type; computerized data processing; interest; lipid metabolism; lipidome; lipidomics; member; metabolomics; mouse model; multiple omics; novel; repository; response; single nucleus RNA-sequencing; single-cell RNA sequencing; tool; usability

PROJECT SUMMARY Interactions between different biomolecules, including proteins, lipids and metabolites, give rise to higher cellular, tissue and organismal phenotypes and functions. Perturbation of these interaction networks is observed during aging and in age-related diseases and identification of these changes can help identify contributing mechanisms. This may be particularly valuable in case of complex diseases, such as late-onset Alzheimer’s Disease and Related Dementias (AD/ADRD), with multiple contributing factors over a life span. Biomolecule networks are affected not only by the overall abundance of individual components but also by their distribution between specific cell types and localization to intracellular compartments. Although snRNA-seq advances allow identification of cell-type specific gene expression patterns in many tissues including the brain, cell-type and organelle-specific proteomic, lipidomic and metabolomic assessments remain challenging. Additionally, integration of the various “omics” data and defining the influence of these changes on cellular, tissue and organismal function presents an ongoing challenge. This poses a limitation, as understanding of disease states requires consideration of interactions between different classes of biomolecules and associated pathways. To address these challenges, we propose to develop an analytical multi-omics pipeline to identify cell-type and organelle-specific functional relationships between different omics parameters and their effects on organellar, cellular, tissue and organismal function during brain aging and in age-related neurodegenerative disease. Consistent with their known contribution to brain aging and neurodegeneration, our initial focus will be on interactions between lipid and protein networks specifically in mitochondria. We will use transgenic reporter mice and optimize MS-based analytical approaches to determine cell-type (microglial and neuronal) specific changes in mitochondrial lipidome and proteome and their influence on mitochondrial function during brain aging (AIM 1). We will develop machine learning-based tools and workflows to allow integrated analysis of proteomic and lipidomic data to generate testable hypotheses about mechanisms contributing to aging and to identify potential novel intervention targets (AIM 2). Eventually the focus will be expanded to include other compartments (lysosomes and lipid droplets) relevant to brain aging and AD/ADRD (AIM 3). Once established, our multi-omics pipeline and tools will be made available to members of UMB, UMCP and other researchers interested in understanding brain aging, neurodegeneration and other age-related processes.

项目摘要 不同生物分子之间的相互作用，包括蛋白质，脂质和代谢物，引起更高的细胞， 组织和有机体的表型和功能。这些相互作用网络的扰动被观察到， 衰老和与年龄有关的疾病的变化，并确定这些变化可以帮助确定促成机制。 这在复杂疾病的情况下可能特别有价值，例如迟发性阿尔茨海默病和阿尔茨海默病。 相关性痴呆（AD/ADRD），在一生中有多种因素。 生物分子网络不仅受到单个组分总体丰度的影响，而且还受到 它们在特定细胞类型之间的分布和定位于细胞内区室。虽然snRNA-seq 进展允许鉴定包括脑在内的许多组织中的细胞类型特异性基因表达模式， 细胞类型和细胞器特异性蛋白质组学、脂质组学和代谢组学评估仍然具有挑战性。 此外，整合各种“组学”数据并确定这些变化对细胞、组织和细胞内的影响， 和生物体功能提出了持续的挑战。这造成了一个局限性，因为对疾病的理解 状态需要考虑不同类别的生物分子和相关途径之间的相互作用。 为了应对这些挑战，我们建议开发一种分析性多组学管道来识别细胞类型， 以及不同组学参数之间的细胞器特异性功能关系及其对细胞器的影响， 在脑老化和与年龄相关的神经退行性疾病中的细胞、组织和有机体功能。 与已知的它们对大脑衰老和神经退行性变的贡献一致，我们最初的重点将是 脂质和蛋白质网络之间的相互作用，特别是在线粒体中。我们将使用转基因报告小鼠 并优化基于MS的分析方法，以确定细胞类型（小胶质细胞和神经元）的具体变化 线粒体脂质组和蛋白质组的变化及其对脑老化过程中线粒体功能的影响（AIM 1）。 我们将开发基于机器学习的工具和工作流程，以实现蛋白质组学和 脂质组学数据，以生成有关衰老机制的可验证假设，并确定潜在的 新的干预目标（AIM 2）。最终，重点将扩大到包括其他车厢 （溶酶体和脂滴）与脑老化和AD/ADRD相关（AIM 3）。一旦建立，我们的多组学 管道和工具将提供给UMB，UMCP和其他感兴趣的研究人员 了解大脑老化、神经退化和其他与年龄相关的过程。