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: 10431729
• 负责人: MICHAEL P CUMMINGS
• 金额: $ 70.76万
• 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/10431729
• 关键词: Address; Affect; Aging; Alzheimer' s disease related dementia; Bioinformatics; Biological Assay; Brain; CRISPR/Cas technology; Classification; Complex; Coupled; Data; Data Sources; 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; Research Infrastructure; Research Personnel; Signal Transduction; Small Nuclear RNA; Software Tools; Source Code; Testing; The Jackson Laboratory; Tissues; Transgenic Mice; Transgenic Organisms; age related; age related neurodegeneration; aging brain; base; brain cell; brain dysfunction; cell type; computerized data processing; interest; lipid metabolism; lipidome; lipidomics; member; metabolomics; mouse model; multiple omics; novel; repository; response; single-cell RNA sequencing; tool

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.

项目总结 不同生物分子之间的相互作用，包括蛋白质、脂类和代谢物，导致更高的细胞， 组织和组织的表型和功能。这些相互作用网络的扰动在 衰老和与年龄相关的疾病的发生以及对这些变化的识别有助于确定致病机制。 这在复杂疾病的情况下可能特别有价值，例如晚发性阿尔茨海默病和 相关痴呆症(AD/ADRD)，一生中有多种致病因素。 生物分子网络不仅受单个组分总体丰度的影响，而且还受 它们在特定细胞类型之间的分布和对细胞内隔间的定位。尽管SnRNA-seq 先进的技术可以识别包括大脑在内的许多组织中的细胞类型特定基因表达模式， 细胞类型和细胞器特定的蛋白质组、脂组和代谢组评估仍然具有挑战性。 此外，整合各种“组学”数据并定义这些变化对细胞、组织的影响 而组织功能是一个持续不断的挑战。这造成了对疾病的理解的局限性 各国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多组学流水线来鉴定细胞类型 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、组织和器官功能。 与它们对大脑老化和神经退行性变的已知贡献一致，我们最初的重点将放在 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)的特定变化 线粒体中的脂组和蛋白质组及其在脑老化过程中对线粒体功能的影响(AIM 1)。 我们将开发基于机器学习的工具和工作流程，以允许对蛋白质组和 脂肪组学数据，以生成关于衰老机制的可测试假说并确定潜在的 新的干预靶点(目标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有关(目标3)。一旦建立起来，我们的多元组学 管道和工具将提供给UMB、UMCP和其他有兴趣的研究人员 了解大脑老化、神经退行性变和其他与年龄相关的过程。