Imaging/Bioinformatics Core

成像/生物信息学核心

• 批准号: 7615726
• 负责人: Bahram A. Parvin
• 金额: $ 27.05万
• 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/7615726
• 关键词: Adipose tissue; Bioinformatics; 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; Numbers; Ontology; Patients; Pattern; Recording of previous events; Research Personnel; Resolution; Sampling; Slice; Specimen; Structure; System; Techniques; Tissues; Trees; base; breast density; comparative; data space; density; in vivo; intracellular protein transport; programs; protein expression; protein localization location; response; software development; three-dimensional modeling; tool; 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加上体内研究，每个拟议的项目将产生 大量的标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一组注释的代表性样本，以及（2）填充数据库 将匿名患者数据与乳房X光检查、乳腺密度和表达谱数据以及获得的数据联系起来， 组织学分析。为此，客观标注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。第一个目标是针对项目2、3和4，第二个目标是 目标将针对所有项目和核心。数据的详细定量表示有助于进行比较分析 同时链接来自不同模态的数据能够实现事件关联， 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）来自高乳房和低乳房区域的导管树结构的低分辨率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 以及低密度和高密度区域的脂肪。导管树结构的3D表示使得能够进行比较 乳腺组织不同区域之间的形态学分析和高分辨率的定量分析 成像数据使得能够使用靶向特定感兴趣和 在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 利用原始数据及其计算的注释，以（1）实现不同数据模态之间的导航，（2） 提供基于图形的查询，以及（3）通过基于Web的界面以图、散点图的形式查看结果 图表或图像。该核心支持与项目外的合作研究者共享数据 项目核心将利用BioSig框架（在LBNL开发）和GeneTraffic平台（在 lobion），以支持通过显微镜和微阵列研究分析图像。核心将扩展 用于管理放射学数据的当前本体，从埃根切片构建乳房的3D模型，并开发 软件工具将基因表达和蛋白质表达模式叠加到这个3D空间上， 信息可视化Core将通过以下方式实现对这种异构数据空间的导航和查询： 图形模型、公共模式和受控词汇表。图像的定量表示及其 注释将可访问BioStatistics Core，以进行详细的敏感性分析。