Statistical methods for population-level cell-type-specific analyses of tissue omics data for Alzheimer's disease

阿尔茨海默病组织组学数据的群体水平细胞类型特异性分析的统计方法

• 批准号: 10589254
• 负责人: Christopher McKennan
• 金额: $ 37.83万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589254
• 关键词: Acceleration; Address; Affect; Alzheimer' s Disease; Behavior; Biology; Brain; Brain region; Cell Lineage; Cells; Cessation of life; Child; Clinical; Communities; Complement; Complex; Computational algorithm; Computer software; Computing Methodologies; DNA; DNA Methylation; Data; Data Analyses; Data Set; Dementia; Dependence; Development; Functional disorder; Future; Gene Expression; Genes; Genotype; Goals; Human; Intervention; Knowledge; Late Onset Alzheimer Disease; Learning; Masks; Mediation; Medicine; Methods; Methylation; Multiomic Data; National Institute on Aging; Parents; Pathogenesis; Pathogenicity; Pathway interactions; Persons; Population; Prevention; Prevention strategy; Problem Solving; Proteins; Proteome; Proteomics; RNA; Research; Resolution; Role; Sample Size; Sampling; Signal Transduction; Site; Solid; Specificity; Statistical Methods; Statistical Models; Techniques; Technology; Testing; Tissue Sample; Tissues; Variant; aging population; brain cell; brain tissue; cell type; cohort; cost; cost efficient; epigenome; genome-wide; high dimensionality; improved; innovation; insight; multiple omics; novel; progressive neurodegeneration; single cell technology; single nucleus RNA-sequencing; single-cell RNA sequencing; statistics; tool; transcriptome; treatment strategy

PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) accounts for 60-80% of dementia cases and causes progressive neurodegeneration that ultimately leads to death. While the number of US people with late-onset AD is expected to reach 13.8 million by 2050, its prevention and treatment remain only modestly effective. Many efforts have been made to study AD pathophysiology by collecting and curating rich omics data from AD-affected or unaffected human brains, e.g., the National Institute on Aging's Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) project. Most of those omics data, such as gene expression, DNA methylation, and proteomics, are collected at the tissue level, and thus the cell-type-specific (CTS) signals are masked. Recently, with the emerging single-cell techniques, single-cell RNA-seq and DNA methylation data have been generated. However, given the difficulty of quantifying a small number of molecules and associated high costs, single-cell data suffer from high technical variation and are constrained to a small number of samples that lack representativity. To address these issues in AD research and accelerate our understanding of cellular multi-omics mechanisms underlying AD, we aim to: 1) Improve estimation of cellular fractions in brain tissue samples by the ensemble over existing methods and considering cell-type hierarchy. 2) Identify CTS differentially methylated regions (DMR) associated with AD. We will consider the spatial correlation of CpG sites and cell-type specificity. 3) We will further build statistical models to systematically integrate those CTS omics estimates via omics-wide association studies and causal mediation analyses. Through extensive analyses of several large cohorts in AMP-AD datasets, we will produce statistically significant and biologically meaningful omics results at an unprecedented population-scale and cell-type resolution, which will improve our understanding of complex AD biology. We will validate our findings using additional data available within and outside the AMP-AD project, including single-cell multi-omics data. The resulting methods will be implemented as efficient computational algorithms via public software readily available to the research community. Successful completion of this project will provide state-of-the-art methods for cell- type deconvolution and integrative multi-omics analyses and advance our knowledge of genes/proteins contributing to AD in selectively vulnerable brain regions and cell types.

项目摘要/摘要 阿尔茨海默病(AD)占痴呆病例的60%-80%，并导致进行性神经变性 这最终会导致死亡。而美国晚发性阿尔茨海默病的人数预计将达到1380万人 到2050年，它的预防和治疗仍然只是适度有效。对AD的研究已经做了很多努力 通过收集和整理来自受AD影响或未受影响的人脑的丰富组学数据，例如， 国家老龄化研究所的阿尔茨海默病加速药物伙伴关系(AMP-AD)项目。 大多数组学数据，如基因表达、DNA甲基化和蛋白质组学，都是在组织中收集的 电平，因此细胞类型特定(CTS)信号被屏蔽。最近，随着单细胞的出现， 技术、单细胞rna-seq和DNA甲基化数据已经产生。然而，考虑到困难， 在量化少量分子和相关的高成本方面，单细胞数据受到高科技的影响 变化，并被限制在少数缺乏代表性的样本。要解决这些问题 在AD研究中，并加速我们对AD背后的细胞多组学机制的理解，我们的目标是： 1)通过集成改进了现有方法对脑组织样本中细胞比例的估计，并 考虑单元格类型层次结构。2)确定与AD相关的CTS差异甲基化区域(DMR)。我们 将考虑CpG位点的空间相关性和细胞类型特异性。3)进一步建立统计模型 通过全基因组学关联研究和因果调解，系统地整合这些CTS组学估计 分析。通过对AMP-AD数据集中几个大型队列的广泛分析，我们将产生统计结果 具有重大意义和生物学意义的组学结果达到了前所未有的人口规模和细胞类型 这将提高我们对复杂的AD生物学的理解。我们将使用以下工具验证我们的发现 AMP-AD项目内外的其他数据，包括单细胞多组学数据。这个 由此产生的方法将通过现成的公共软件作为有效的计算算法来实现 向研究界致敬。该项目的成功完成将为CELL提供最先进的方法 类型去卷积和整合多组学分析和提高我们对基因/蛋白质的认识 在选择性脆弱的大脑区域和细胞类型中导致阿尔茨海默病。