EAGER: Computed Tomography (CT) as a Novel Platform for Presenting Four-Dimensional (4D) 'omics' Expression Data: Tomato Fruit Development as Test Case

EAGER：计算机断层扫描 (CT) 作为呈现四维 (4D)“组学”表达数据的新颖平台：番茄果实发育作为测试案例

• 批准号: 1313887
• 负责人: Jocelyn Rose
• 金额: $ 29.75万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1313887&HistoricalAwards=false
• 关键词: EAGER; Computed; Tomography; CT; Novel

PI: Jocelyn K. Rose (Cornell University)CoPI: Zhangjun Fei (Cornell University)The application of 'omics' (next generation nucleic acid sequencing, proteomics etc.) approaches in the study of multicellular organisms have generated an enormous amount of data that is difficult to comprehend visually. This proposal outlines a strategy to create a novel, high resolution graphical interface to display 'omics' data in a scalable format that will allow easy visualization and interrogation in four dimensions (three dimensions through time). In this project tomato fruit development will be studied as a model system to generate tools to project data sets onto a digitized computed tomography (CT) composite image of the fruit and, thereby, create a 4D platform to spatially and temporally examine the regulatory and structural pathways underlying fruit development and metabolism. An important utility of this platform is that it is readily adaptable to any complex multicellular organisms (plants, animals etc.) whose structure is amenable to CT scans. The specific aims are to (1) generate a digitized CT based profile of tomato fruit development and ripening at the micron and/or submicron level of resolution, (2) develop laser capture microdissection RNA-Seq profiles of the five principal cell/tissue types in tomato fruit pericarp across the same developmental gradient and, (3) create a bioinformatics/analytical pipeline to integrate the data sets derived from aims 1 and 2.The project will train two postdoctoral research associates in modern biological and bioinformatics approaches. If successful, the imaging and RNA-Seq data will be combined with existing proteomic and metabolomic data and made publicly available as a Tomato Expression Atlas, which will be accessible through the Sol Genomics Network website (http://solgenomics.net). Opportunities for training in the use of the bioinformatics tools developed by the project will be made available at the annual Workshop in Plant Genomics and Systems Biology held at Cornell University.

PI：Jocelyn K. Rose（康奈尔大学）CoPI：Zhangjun Fei（康奈尔大学）“组学”（下一代核酸测序、蛋白质组学等）方法在多细胞生物研究中的应用产生了大量数据，很难从视觉上理解。 该提案概述了创建新颖的高分辨率图形界面的策略，以可扩展的格式显示“组学”数据，从而可以轻松地在四个维度（通过时间的三个维度）进行可视化和询问。在该项目中，番茄果实发育将作为一个模型系统进行研究，以生成工具将数据集投影到果实的数字化计算机断层扫描 (CT) 合成图像上，从而创建一个 4D 平台来在空间和时间上检查调控和结构水果发育和代谢的途径。该平台的一个重要用途是它很容易适应任何结构适合 CT 扫描的复杂多细胞生物体（植物、动物等）。 具体目标是 (1) 以微米和/或亚微米分辨率生成基于数字化 CT 的番茄果实发育和成熟图谱，(2) 开发五种主要细胞/组织类型的激光捕获显微切割 RNA-Seq 图谱在番茄果实果皮中跨越相同的发育梯度，(3) 创建一个生物信息学/分析管道来整合来自目标 1 和 2 的数据集。该项目将培训两名现代生物学和生物信息学方法。 如果成功，成像和 RNA-Seq 数据将与现有的蛋白质组学和代谢组学数据相结合，并作为番茄表达图谱公开发布，可通过 Sol Genomics Network 网站 (http://solgenomics.net) 访问。 在康奈尔大学举办的植物基因组学和系统生物学年度研讨会上将提供使用该项目开发的生物信息学工具的培训机会。