Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease

了解导致阿尔茨海默病神经精神症状的分子机制

• 批准号: 10406707
• 负责人: STEVEN M FINKBEINER
• 金额: $ 25.38万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10406707
• 关键词: Address; Adverse effects; Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Bioconductor; Biological; Biology; Caregiver Burden; Cell Nucleus; Cells; Code; Cognitive; Communities; Complex; Computer software; Data; Data Set; Dementia; Development; Disease; Disease Progression; Documentation; Engineering; Environment; Etiology; Gene Expression; Gene Expression Profile; Genes; Genomics; Goals; Human; Institutionalization; Knowledge; Memory; Mind; Mining; Modeling; Molecular; Molecular Disease; Neurodegenerative Disorders; Pathway interactions; Quality of life; Research; Research Design; Resolution; Resources; Software Engineering; Source; Statistical Models; Testing; Time; Translating; Variant; Work; Writing; base; brain tissue; care giving burden; cell type; daily functioning; design; differential expression; epigenome; epigenomics; genomic data; high dimensionality; improved; large datasets; multiple data types; neuropsychiatric symptom; neuropsychiatry; new therapeutic target; novel therapeutics; open source; open source tool; parallel processing; parent grant; phenomics; phenotypic data; prevent; success; transcriptome; transcriptomics

PROJECT SUMMARY Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects 6.2M Americans, yet current therapies are not effective at preventing or slowing the cognitive decline1. Neuropsychiatric symptoms (NPS) are core features of AD and related dementias that are associated with major adverse effects on daily function and quality of life, and accelerate time to institutionalization. The overarching goal of the parent grant R01AG067025 is to integrate single nucleus transcriptome profiles with detailed NPS phenotype data from each donor and identify dysregulated genes associated with disease trajectory, identify clusters of donors with different gene expression disease signatures, and nominate genes and pathways for targeting with novel therapeutics. The compendium of single nucleus transcriptome profiles comprising ~7.2M nuclei from ~1,800 total donors generated by the parent grant R01AG067025 is a remarkable resource. Yet mining these transcriptome profiles to advance knowledge of AD etiology requires analytical workflows that scale to the unprecedented size of these and other emerging data. Existing workflows for multi-donor single cell and nucleus transcriptome data have either been 1) designed for a small number of donors and so cannot take advantage of the large-scale and complex study design used here, or 2) adapted from bulk transcriptome analyses and do not currently scale to hundreds of donors, dozens of cell types and millions of cells. The objective of addressing pressing biological hypotheses about AD biology necessitates the development of analytical workflows designed and engineered with the challenges of multi-donor single cell and nucleus transcriptome data in mind. In this Supplement, we propose developing a scalable, open source analytical workflow for multi-donor single cell/nucleus transcriptome data motivated by our previous work on linear mixed models2,3. We have previously applied linear mixed models to analyze bulk transcriptome profiles, and developed the open source variancePartition package to perform differential expression testing, account for technical batch effects and characterize the multiple biological and technical sources of expression variation. While the current software has facilitated analysis of bulk transcriptomic and epigenomic profiles by our group and many others, applying it to the multi-donor single nucleus data is currently limited by the ad hoc design of the variancePartition codebase. To address these limitations, here we propose (Aim 1) Scaling this analytical workflow to emerging datasets using best practices in software engineering, code refactoring, and empirical testing across multiple computing environments; and (Aim 2) Enabling broader use by (a) computational biologists by developing vignettes to illustrate applications of the software on public datasets, and by (b) open source developers by improving code design and documentation. Overall, reconceiving the analytical workflow of variancePartition will enable the powerful linear mixed model approach to scale to multi-donor single cell and nucleus transcriptome datasets in order to address questions about the etiology of AD and serve as an open source tool for the broader community.

项目概要 阿尔茨海默病 (AD) 是一种毁灭性的神经退行性疾病，影响着 620 万美国人，但目前 疗法不能有效预防或减缓认知能力下降1。神经精神症状（NPS）是 AD 和相关痴呆症的核心特征与日常功能的重大不利影响相关 生活质量，并加快送入机构的时间。家长补助金 R01AG067025 的总体目标 是将单核转录组图谱与来自每个供体的详细 NPS 表型数据整合起来 识别与疾病轨迹相关的失调基因，识别具有不同基因的供体簇 表达疾病特征，并指定新疗法靶向的基因和途径。 单核转录组谱概要，包含来自约 1,800 个供体的约 720 万个核 由母基金 R01AG067025 产生的资金是一项非凡的资源。然而挖掘这些转录组图谱 为了增进对 AD 病因学的了解，需要分析工作流程能够扩展到前所未有的规模 和其他新兴数据。多供体单细胞和核转录组数据的现有工作流程 要么 1) 是为少数捐助者设计的，因此无法利用大规模和 此处使用的复杂研究设计，或 2) 改编自批量转录组分析，目前尚未扩展到 数百名捐赠者、数十种细胞类型和数百万个细胞。解决紧迫的生物问题的目标 有关 AD 生物学的假设需要开发设计和设计的分析工作流程 考虑到多供体单细胞和细胞核转录组数据的挑战。 在本补充文件中，我们建议为多捐助者单一开发可扩展的开源分析工作流程 细胞/核转录组数据是由我们之前关于线性混合模型的工作激发的2,3。我们之前有过 应用线性混合模型来分析批量转录组图谱，并开发了开源 variancePartition 包用于执行差异表达测试，考虑技术批次效应和 描述表达变异的多种生物和技术来源。虽然目前的软件 促进了我们小组和其他许多人对大量转录组和表观基因组图谱的分析，并应用它 目前，对多捐助者单核数据的处理受到variancePartition代码库的临时设计的限制。 为了解决这些限制，我们在这里建议（目标 1）将此分析工作流程扩展到新兴数据集 使用软件工程、代码重构和跨多个计算的实证测试的最佳实践 环境； （目标 2）通过开发小插图来使（a）计算生物学家更广泛地使用 说明该软件在公共数据集上的应用，并由 (b) 开源开发人员通过改进代码来说明 设计和文档。总体而言，重新构思方差分区的分析工作流程将使 强大的线性混合模型方法可扩展到多供体单细胞和细胞核转录组数据集 为了解决有关 AD 病因学的问题，并作为更广泛社区的开源工具。