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.

集成计算分析核心：项目摘要/摘要 为了研究疾病相关的变化（例如阿尔茨海默氏病）如何与特定危险因素有关 （例如，与年龄相关的变化，蛋白质聚集体，血管病理等）改变空间排列 大脑中的细胞类型及其基因表达谱，我们需要构建大脑的高分辨率图 有或没有病理的个体的细胞水平的组织。构建这些地图需要正确 病理特征的表征，以及可重现的数据生成和 成像。为此，集成的计算分析核心将与空间多组学紧密合作 核心，生物传播核心以及成像和基因组数据分析的各个方面的四个项目。 这包括以下内容：1）初级分析，完成图像注册，分割和基因 表达数量，2）次级分析，完成跨样本的注册和细胞类型 使用多路复用蛋白表达和全基因组转录组学数据分配，3）第三纪 分析，涉及离散可测量属性的推导，总结了细胞类型的关键方面， 基因表达以及空间中的病理组成和分布。通过扩展并适应现有 工作流以满足这些数据分析需求，该核心在每个分析目的中集中参与 项目，以及与空间多组分核心的连续反馈以优化数据 一代工作流程。该核心的另一个目标是集成原始数据和处理的数据，以及 空间属性模型，成为一个可查询的交互式门户，称为衰老和 大脑（Maap-brain）可视化器中的病理学。这个公开访问的门户，以及传播 通过该整体U19提案的教育部分进行工作流程，将为大脑衰老和 广告科学界具有界面，以与项目产生的大规模数据互动 其他核心。最终，我们旨在进行这种传播努力，以允许外部研究人员产生或 验证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。