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: 10246250
• 负责人: Fiona Ginty
• 金额: $ 75万
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246250
• 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; 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).

项目总结/摘要 该项目的总体目标是开发用于三维重建的开源软件和算法， 正常组织的定位和多尺度映射。另一个重要的目标是评估老化和环境的影响。 皮肤的分子和结构架构的边缘因素。我们将利用我们成熟的（TRL 8）技术- 多路复用二维成像（Cell DIVE™），以及我们在二维图像分析和机器方面的丰富经验 学习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）临床上相同， 来自不同年龄组的样本更容易获得，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 致病性侮辱虽然ECM、细胞和细胞内分子组成在不同的细胞中变化很大， 各种器官，我们相信在这个计划下开发的许多工具将适用于重建和 以高（细胞/亚细胞）分辨率绘制其他器官模型。该提案将侧重于开发算法- 3-D组织图像的多尺度映射的算法和框架，这将解决HuBMAP的优先事项 围绕定量3D图像分析/映射，包括自动3D图像分割，特征提取， 牵引力和图像注释。生物分子的高分辨率（亚细胞）映射将在我们- 将用于重建3D组织并链接到较低分辨率3D光学系统的2D复用图像， calcoherence tomography（OCT）图像的正常组织。其他细胞水平的组学数据（例如，RNA FISH）将 以同样的方式映射。低分辨率图像被映射回更高级别的地标（例如，器官）作为 由HuBMAP通用坐标框架（CCF）定义。如前所述，我们提出的技术将在- 包括几个对现有HuBMAP联盟项目具有重要意义和互补性的关键功能， 将推动3D组织分析的发展提出的算法将有几个关键的创新- 这将推进在3-D多路复用组织图像分析的最新技术水平的设置。首先，由于大规模的... 对于待分析的图像，高通量将是每个图像分析算法的关键要求。这将是 这得益于我们在并行化单细胞分析管道方面的丰富经验。第二，提出的算法- Rithms将以多个尺度分割图像。创新的第三个领域将集中在高效的多- 渠道分析拟议的项目将包括创建一个易于使用的软件工具， 可视化多尺度组织数据，称为Tissue Atlas Navigation Graphical Overview（TANGO）。