Core D: Integrated Computational Analysis Core

综合计算分析核心：项目摘要/摘要 研究阿尔茨海默氏症等疾病相关变化与特定风险因素之间的关系 (例如，与年龄相关的变化、蛋白质聚集体、血管病理等)改变空间排列的步骤 对于大脑中的细胞类型及其基因表达谱，我们需要建立高分辨率的大脑地图 有病理和无病理个体的细胞水平的组织。构建这些地图需要适当的 病理特征的表征，以及用于数据生成和复制的可重复工作流 成像。为此，综合计算分析核心将与空间多重组学密切合作 CORE、BIOSPECIMEN CORE和四个关于成像和基因组数据分析各方面的项目。 这包括以下几个方面：1)初步分析，包括图像配准、分割和基因 表达量化，2)二次分析，包括交叉样本配准和细胞类型 使用多路蛋白质表达和全基因组转录组学数据进行分配，以及3)第三代 分析，包括推导总结细胞类型关键方面的离散可测量属性， 基因表达、病理组成和空间分布。通过纵向扩展和调整现有 解决这些数据分析需求的工作流，此核心集中参与每个 项目，以及参与空间多组学核心的持续反馈，以优化数据 生成工作流。此核心的另一个目标是集成原始数据和已处理数据，以及 空间属性模型，到一个可查询的交互门户，称为多维老龄化地图集和 大脑中的病理(MAAP-Brain)可视化工具。这个可公开访问的门户网站，以及 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科学社区，有一个界面来参与项目产生的大规模数据和 其他核心。最终，我们的目标是使外部研究人员能够产生或 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。

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.