Groundwork for a Synchrotron MicroCT Imaging Resource for Biology (SMIRB)

生物同步加速器 MicroCT 成像资源 (SMIRB) 的基础

• 批准号: 10558057
• 负责人: Keith Chi Cheng
• 金额: $ 2.56万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10558057
• 关键词: 3-Dimensional; Address; Adult; Affect; Anatomy; Animal Disease Models; Animal Model; Animals; Biological; Biological Models; Biological Process; Biological Sciences; Biology; Biopsy Specimen; Cell Differentiation process; Cell physiology; Cells; Cellular Morphology; Cellular Structures; Characteristics; Child; Cladocera; Collaborations; Collection; Communities; Complement; Computer Models; Computer software; Computing Methodologies; Daphnia; Data; Data Set; Detection; Devices; Diagnosis; Disease; Epithelial Cells; Fiji; Foundations; Goals; Health; Histologic; Histology; Human; Image; Imaging technology; Intestines; Invertebrates; Investigation; Knowledge; Label; Laboratories; Larva; Learning; Machine Learning; Manuals; Mathematics; Metals; Methodology; Microscope; Microscopic; Modeling; Morphologic artifacts; Morphology; Normal Cell; Normalcy; Organism; Pathologic Processes; Pathology; Pattern; Phenotype; Pilot Projects; Process; Publishing; Reference Standards; Research; Resolution; Resources; Roentgen Rays; S-nitro-N-acetylpenicillamine; Sampling; Scanning; Slice; Spatial Distribution; Specificity; Specimen; Stains; Statistical Distributions; Structure; Supervision; Synchrotrons; Techniques; Technology; Testing; Tissue Sample; Tissue imaging; Tissues; Training; Update; Validation; Variant; Visualization; Whole Organism; Work; X-Ray Computed Tomography; Xenopus; Zebrafish; automated segmentation; base; cell type; comparative; disease diagnosis; gastrointestinal epithelium; imaging system; interest; machine learning algorithm; machine learning pipeline; microCT; multidisciplinary; novel; open source; open source tool; prevent; screening; soft tissue; supervised learning; three-dimensional visualization; tomography; tool

Summary/Abstract of the Proposed Project Phenotypes provide a classical biological approach to studying morphological changes. Under a microscope, adults and children alike can distinguish a normal cell arrangement like a single row of columnar-shaped gut cells, and differentiate this normal presentation from an abnormal clustered group of the same columnar-shaped cells. This distinction occurs by descriptively comparing the abnormal phenotype against normality. For a computer- based model to make the same distinction, a wild-type reference must be used for comparison. Studies on cellular phenotyping widely use histological examination to visualize cell morphological changes; however, it induces sectioning artifacts, thereby preventing the visualization of 3-dimensional (3D) tissue volumes. To circumvent these challenges, we plan to take advantage of an unbiased form of tissue imaging, termed X-ray computed microtomography (microCT). MicroCT will be used to develop a standard reference of normal cellular phenotypes needed to lay a foundation for automated segmentation and computational phenotyping. Unlike conventional histology, the imaging technology of microCT, which amounts to a non-destructive form of histology for small metal- stained biological samples, will enable 3D visualization of cell types in the micrometer range. The novel scale and resolution of microCT will thereby reveal the 3D morphology and spatial distribution of cells to quantify and characterize phenotypic variations across vertebrate and invertebrate models. Combining the resolution of microCT imaging with machine learning, we will apply supervised-manual segmentation to microCT datasets to create computational cell recognition mechanisms for morphological assessment. This integration will include open-source tools and devices that will enable community-driven research and data-sharing. As an added benefit of this AI integration, our computational approach will further assist in augmenting large amounts of data required to examine the statistical association of cellular phenotypes. To demonstrate the applicability of our methodology for phenotypic characterization in any given cell type or organism, our pilot for this project will focus on the single-variable approach of ‘gut epithelial cells’ across three organisms (i.e., zebrafish, daphnia, froglets). By prioritizing phenotypic investigation of these specialized cells to the analogous relationship of mammalian intestinal cells, this work will establish a practical foundation to increase our understanding of the 3D biological structure of wild-type cells, and advance morphological cellular phenotyping in whole-organisms for quantitative, histological, disease recognition.

建议项目的摘要/摘要 表型为研究形态学变化提供了经典的生物学方法。在显微镜下， 成人和儿童都可以区分正常的细胞排列，如单行柱状肠细胞， 并将这种正常表现与异常的相同柱状细胞簇群区分开来。 这种区别是通过将异常表型与正常表型进行对比来实现的。对于电脑来说- 为了进行相同的区分，必须使用野生型引用进行比较。细胞研究 表型分型广泛使用组织学检查来可视化细胞形态学变化;然而，它诱导 切片伪影，从而阻止三维（3D）组织体积的可视化。为了规避这些 挑战，我们计划利用一种无偏见的组织成像形式，称为X射线计算 显微断层扫描（microCT）。MicroCT将用于开发正常细胞表型的标准参考 需要为自动分割和计算表型奠定基础。不同于常规 组织学，microCT的成像技术，相当于一种非破坏性的组织学形式，用于小金属， 染色的生物样品，将能够在微米范围内的细胞类型的3D可视化。新的规模和 因此，microCT的分辨率将揭示细胞的3D形态和空间分布， 表征脊椎动物和无脊椎动物模型中的表型变异。结合分辨率 通过机器学习的microCT成像，我们将对microCT数据集应用监督手动分割， 创建用于形态评估的计算细胞识别机制。这一整合将包括 开源工具和设备，将使社区驱动的研究和数据共享。作为一个额外的好处 在这种人工智能集成中，我们的计算方法将进一步帮助增加所需的大量数据 来检验细胞表型的统计学关联。 为了证明我们的方法在任何给定的细胞类型或 生物体，我们的试点项目将集中在单变量的方法'肠道上皮细胞'跨越三个 生物体（即，斑马鱼、水蚤、蛙类）。通过优先对这些特化细胞进行表型研究， 哺乳动物肠道细胞的类似关系，这项工作将建立一个实际的基础，以增加 我们对野生型细胞的3D生物学结构的理解，以及先进的形态学细胞表型 用于定量、组织学、疾病识别。