Multi-Scale 3-D Image Analytics for High Dimensional Spatial Mapping of Normal Tissues

用于正常组织高维空间绘图的多尺度 3D 图像分析

• 批准号: 10251375
• 负责人: Fiona Ginty
• 金额: $ 70万
• 依托单位: GENERAL ELECTRIC GLOBAL RESEARCH CTR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10251375
• 关键词: 3-Dimensional; Address; Age; Aging; Algorithmic Analysis; Algorithmic Software; Algorithms; Architecture; Area; Artificial Intelligence; Atlases; Back; Biological Markers; Biopsy; California; Cells; Cellular biology; Chemistry; Clinical; Collaborations; Computer software; Coupled; Data; Data Collection; Disease; Environment; Environmental Risk Factor; Exposure to; Extracellular Matrix; Funding; Generations; Genome; Goals; Government; Human BioMolecular Atlas Program; Human body; Image; Image Analysis; Imaging technology; Individual; Institutes; Lead; Link; Location; Machine Learning; Maps; Measures; Methods; Modeling; Molecular; Molecular Structure; Multiomic Data; Normal tissue morphology; Optics; Organ; Organ Model; Outcome; Pathogenicity; Proteomics; RNA; Recording of previous events; Research; Resolution; Sampling; Skin; Skin Aging; Skin Tissue; Software Tools; Solid; Technology; Three-Dimensional Image; TimeLine; Tissue Sample; Tissue imaging; Tissues; Traction; UV Radiation Exposure; United States National Institutes of Health; Universities; Visualization; Work; age effect; age group; analysis pipeline; data integration; data visualization; experience; extracellular; high dimensionality; image visualization; imaging Segmentation; imaging platform; innovation; member; multidimensional data; multidisciplinary; multiple omics; multiplexed imaging; multiscale data; open source; open source tool; programs; reconstruction; sample collection; single cell analysis; software development; task analysis; three-dimensional visualization; tomography; tool

PROJECT SUMMARY/ABSTRACT The overall goal of the proposed project is to develop open-source software and algorithms for 3-D reconstruc- tion and multi-scale mapping of normal tissues. Another significant goal is to evaluate effects of aging and envi- ronmental factors on molecular and structural architecture of skin. We will leverage our mature (TRL8) technol- ogy for multiplexed 2-D imaging (Cell DIVE™), and our vast experience in 2-D image analytics and machine learning. We have selected normal skin as the organ to develop these tools for several reasons, a) clinical sam- ples from different age groups are more readily available, b) it is a good model to independently capture changes in extracellular matrix (ECM) due to age and normal exposure to environmental factors as well as a variety of pathogenic insults. While the ECM, cellular and intracellular molecular composition varies considerably among various organs, we believe many of the tools developed under this program will be applicable to reconstruct and map other organ models at high (cellular/subcellular) resolution. This proposal will focus on developing algo- rithms and a framework for multi-scale mapping of 3-D tissue images, which will address HuBMAP priorities around quantitative 3-D image analysis/mapping, including automated 3-D image segmentation, feature ex- traction, and image annotation. High-resolution (subcellular) mapping of biomolecules will be implemented us- ing 2-D multiplexed images that are used to reconstruct the 3-D tissue and linked to a lower resolution 3-D opti- cal coherence tomography (OCT) image of the normal tissue. Other cell-level omic data (e.g., RNA FISH) will be mapped in the same way. The low-resolution image is mapped back to a higher-level landmark (e.g., organ) as defined by the HuBMAP common coordinate framework (CCF). As outlined, our proposed technologies will in- clude several key features that are significant and complimentary to existing HuBMAP consortium projects and will advance the state of the art in 3-D tissue analysis. The proposed algorithms will have several key innova- tions that will advance the state of the art in 3-D multiplexed tissue image analysis. First, given the large vol- umes to be analyzed, high throughput will be a key requirement of each image analysis algorithm. This will be supported by our extensive experience in parallelizing single cell analysis pipelines. Second, the proposed algo- rithms will segment the images at multiple scales. The third area of innovation will focus on efficient multi- channel analysis. The proposed project will include creation of an easy-to-use software tool for assembling and visualizing multiscale tissue data called Tissue Atlas Navigation Graphical Overview (TANGO).

项目概要/摘要 该项目的总体目标是开发用于 3D 重建的开源软件和算法 正常组织的另一个重要目标是评估衰老和环境的影响。 我们将利用我们成熟的 (TRL8) 技术。 复合二维成像 (Cell DIVE™) 技术，以及我们在二维图像分析和机器方面的丰富经验 我们选择正常皮肤作为开发这些工具的器官有几个原因：a）临床特征。 来自不同年龄段的人更容易获得，b）这是一个独立捕获变化的好模型 由于年龄和正常暴露于环境因素以及各种因素，细胞外基质（ECM）中的 病理损伤虽然 ECM、细胞和细胞内分子组成存在很大差异。 各种器官，我们相信根据该计划开发的许多工具将适用于重建和 以高分辨率（细胞/亚细胞）绘制其他器官模型该提案将重点开发算法。 用于 3D 组织图像多尺度映射的算法和框架，这将解决 HuBMAP 优先事项 围绕定量 3D 图像分析/映射，包括自动 3D 图像分割、特征提取 生物分子的高分辨率（亚细胞）绘图将通过以下方式实现： 荷兰国际集团用于重建3D组织并链接到较低分辨率3D光学的2D复用图像 正常组织的 Cal 相干断层扫描 (OCT) 图像将是其他细胞水平组学数据（例如 RNA FISH）。 以相同的方式映射低分辨率图像到更高级别的地标（例如器官）： 正如所概述的，我们提出的技术将包含在 HuBMAP 通用坐标框架 (CCF) 中。 包括几个对现有 HuBMAP 联盟项目很重要且互补的关键功能，以及 将推进 3D 组织分析的最新技术。所提出的算法将有几个关键的创新。 首先，考虑到大量的数据，这将推动 3D 多重组织图像分析的发展。 对于要分析的图像，高吞吐量将是每种图像分析算法的关键要求。 其次，我们在并行单细胞分析流程方面拥有丰富的经验。 rithms 将以多种尺度分割图像。第三个创新领域将侧重于高效的多尺度分割。 拟议的项目将包括创建一个易于使用的软件工具，用于组装和分析。 可视化多尺度组织数据，称为组织图谱导航图形概述 (TANGO)。