Core D: Integrated Computational Analysis Core

核心D：综合计算分析核心

• 批准号: 10555896
• 负责人: Vilas Menon
• 金额: $ 92.59万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10555896
• 关键词: 3-Dimensional; Address; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease pathology; Anus; Atlases; Blood Vessels; Brain; Brain Mapping; Brain Pathology; Cells; Communities; Computer Analysis; Data; Data Analyses; Derivation procedure; Dimensions; Disease; Education; Feedback; Gene Expression; Generations; Goals; Human; Image; Individual; Link; Maps; Measurable; Modality; Modeling; Pathologic; Pathology; Process; RNA; Reproducibility; Research Personnel; Resolution; Risk Factors; Sampling; Spatial Distribution; Tissues; Work; age related; aging brain; brain tissue; cell type; genome-wide; genomic data; image registration; large scale data; multiple omics; protein aggregation; protein expression; scale up; secondary analysis; stem; transcriptomics; usability

INTEGRATED COMPUTATIONAL ANALYSIS CORE: PROJECT SUMMARY/ABSTRACT To investigate how disease relevant changes, such as Alzheimer’s disease, are linked to specific risk factors (e.g., age-related changes, protein aggregates, vascular pathologies, etc.) to alter the spatial arrangement of cell types in the brain and their gene expression profiles, we need to build high-resolution maps of brain tissue at the cellular level in individuals with and without pathology. Building these maps requires proper characterization of the pathological features, as well as a reproducible workflow for data generation and imaging. To this end, the Integrated Computational Analysis Core will work closely with the Spatial Multiomics Core, the Biospecimen Core, and the four Projects on all aspects of imaging and genomics data analysis. This includes the following: 1) Primary analysis, comprising image registration, segmentation, and gene expression quantification, 2) Secondary analysis, comprising cross-sample registration and cell type assignment using multiplexed protein expression and genome-wide transcriptomics data, and 3) Tertiary analysis, involving the derivation of discrete measurable attributes summarizing key aspects of cell type, gene expression, and pathological composition and distribution in space. By scaling up and adapting existing workflows to address these data analysis needs, this Core is centrally involved in all analysis aims of each Project, as well as being engaged in continuous feedback with the Spatial Multiomics Core to optimize data generation workflows. An additional goal of this core is to integrate the raw and processed data, as well as the spatial attribute models, into a queryable, interactive portal called the Multidimensional Atlas of Aging and Pathology in the Brain (MAAP-Brain) visualizer. This publicly accessible portal, along with dissemination of workflows through the educational component of this overall U19 proposal, will provide the brain aging and AD scientific community with an interface to engage with the large-scale data generated by the Projects and other Cores. Ultimately, we aim for this dissemination effort to allow external researchers to generate or validate their own hypotheses about changes in cellular composition and arrangement associated with aging and human brain pathology.

综合计算分析核心：项目摘要/摘要 研究疾病相关变化（例如阿尔茨海默病）如何与特定风险因素相关 （例如，与年龄相关的变化、蛋白质聚集体、血管病理学等）来改变空间排列 为了了解大脑中的细胞类型及其基因表达谱，我们需要构建高分辨率的大脑图谱 构建这些图谱需要适当的方法。 病理特征的表征，以及用于数据生成和分析的可重复工作流程 为此，集成计算分析核心将与空间多组学密切合作。 Core、Biospecimen Core 以及涉及成像和基因组数据分析各个方面的四个项目。 这包括以下内容：1）初步分析，包括图像配准、分割和基因 表达定量，2) 二次分析，包括跨样本配准和细胞类型 使用多重蛋白质表达和全基因组转录组数据进行分配，以及 3) 第三级 分析，涉及总结细胞类型关键方面的离散可测量属性的推导， 通过扩大和适应现有的基因表达、病理组成和分布。 工作流程来满足这些数据分析需求，该核心集中参与每个工作流程的所有分析目标 项目，以及利用空间多组学核心进行持续反馈以优化数据 该核心的另一个目标是集成原始数据和处理后的数据。 空间属性模型，进入一个可查询的交互式门户，称为多维老龄化图谱， 大脑病理学 (MAAP-Brain) 可视化工具，可公开访问的门户网站，以及传播。 通过整个 U19 提案的教育部分的工作流程，将提供大脑老化和 AD 科学界有一个接口来处理项目生成的大规模数据， 最终，我们的目标是让外部研究人员能够生成或传播这些内容。 验证他们自己关于与衰老相关的细胞组成和排列变化的假设 和人脑病理学。