Imaging/Bioinformatics Core

成像/生物信息学核心

• 批准号: 7866586
• 负责人: Bahram A. Parvin
• 金额: $ 27.78万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7866586
• 关键词: Adipose tissue; Bioinformatics; Biological; Biological Models; Biostatistics Core; Breast; Cells; Complement; Controlled Vocabulary; Data; Databases; Ductal; Epithelial; Event; Gene Expression; Genome; Goals; Graph; Heterogeneity; Image; Image Analysis; Imagery; Link; Mammary Gland Parenchyma; Mammography; Microscopy; Modality; Modeling; Molecular Profiling; Ontology; Patients; Pattern; Proteins; Recording of previous events; Research Personnel; Resolution; Sampling; Slice; Software Tools; Specimen; Structure; System; Techniques; Tissues; Trees; base; breast density; cancer risk; comparative; data sharing; data space; density; in vivo; programs; protein expression; response; software development; three-dimensional modeling; web based interface

Cellular responses are heterogeneous, tissue specific, and a function of the history of a cell and its genome. In dealing with the heterogeneity of multiple model systems plus in-vivo studies, each proposed project will generate a large number of specimens for detailed quantitative and correlative analyses. The Imaging Bioinformatics Core will complement and extend the presently developed BioSig framework with two objectives: (1) to provide a fully annotated set of representative samples that are imaged at different resolutions, and (2) to populate databases that link anonymous patient data to mammography, breast density and expression profile data plus data obtained from histological analyses. For this objective annotation refers to user's input and feature-based representations that are computed using image analysis techniques. The first goal will target Projects 2, 3, and 4, and the second goal will target all Projects and Cores. Detailed quantitative representation of data enables comparative analysis of images based on their content, while linking data from different modalities enables event correlation and information visualization. Quantitative representation will be applied to (1) low-resolution compositional analysis of breast density, (2) low-resolution 3D modeling of ductal tree structures from regions of high and low breast density, (3) high-resolution 2D and 3D morphological and protein localization studies, and (4) analysis of expression profiles in support of Project 2. Compositional analysis will investigate the ratio of epithelial, stroma and adipose in low- and high-density regions. 3D representation of ductal tree structures enables comparative morphological analysis between different regions of breast tissue and quantitative analysis of high-resolution image data enables morphological and protein expression analysis using markers that target specific inter- and intracellular activities in tissue or cultured multicellular systems. The Core will couple user-defined annotations with the raw data and their computed annotations to (1) enable navigation between different data modalities, (2) provide graph-based queries, and (3) view the results through a Web-based interface in the form of plots, scatter diagrams, or images. This core enables sharing of data with collaborating investigators outside of the program project. The core will leverage the BioSig framework (developed at LBNL) and GeneTraffic platform (developed at lobion) in support of analysis of images through microscopy and microarray studies. The Core will extend the current ontology for managing radiological data, construct 3D models of the breast from Egan slices, and develop software tools to overlay gene expression and patterns of protein expression onto this 3D space for meaningful information visualization. The Core will enable navigation and query of this heterogeneous data space through graphical model, common schema, and controlled vocabulary. Quantitative representation of images and their annotation will be accessible to the BioStatistics Core for detailed sensitivity analysis.

细胞反应是异质的、组织特异性的，并且是细胞及其基因组历史的函数。在 处理多个模型系统的异质性以及体内研究，每个提议的项目都会产生 大量样本进行详细的定量和相关分析。成像生物信息学核心 将补充和扩展目前开发的 BioSig 框架，有两个目标：(1) 提供一个完整的 以不同分辨率成像的带注释的代表性样本集，以及 (2) 填充数据库 将匿名患者数据与乳房X光检查、乳房密度和表达谱数据以及获得的数据联系起来 从组织学分析。为此，客观注释指的是用户的输入和基于特征的表示 使用图像分析技术计算得出。第一个目标将针对项目 2、3 和 4，第二个目标将针对项目 2、3 和 4 目标将针对所有项目和核心。数据的详细定量表示可以进行比较分析 基于图像的内容，同时链接来自不同模式的数据可以实现事件关联和 信息可视化。定量表示将应用于（1）低分辨率成分分析 乳房密度，(2) 对高乳房和低乳房区域的导管树结构进行低分辨率 3D 建模 密度，(3) 高分辨率 2D 和 3D 形态学和蛋白质定位研究，以及 (4) 分析 支持项目 2 的表达谱。成分分析将研究上皮、基质的比例 以及低密度和高密度区域的脂肪。导管树结构的 3D 表示可以进行比较 乳腺组织不同区域的形态分析及高分辨率定量分析 图像数据可以使用针对特定内部和外部的标记进行形态学和蛋白质表达分析 组织或培养的多细胞系统中的细胞内活动。核心将耦合用户定义的注释 使用原始数据及其计算注释来 (1) 实现不同数据模式之间的导航，(2) 提供基于图形的查询，以及 (3) 通过基于 Web 的界面以绘图、散点图的形式查看结果 图表或图像。该核心可以与项目外的合作研究人员共享数据 项目。核心将利用 BioSig 框架（在 LBNL 开发）和 GeneTraffic 平台（在 LBNL 开发） lobion）支持通过显微镜和微阵列研究进行图像分析。核心将扩展 用于管理放射学数据的当前本体，从 Egan 切片构建乳房 3D 模型，并开发 软件工具可将基因表达和蛋白质表达模式叠加到该 3D 空间上，以获得有意义的结果 信息可视化。核心将通过以下方式实现异构数据空间的导航和查询： 图形模型、通用模式和受控词汇。图像及其定量表示 BioStatistics Core 可以访问注释以进行详细的敏感性分析。