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).

项目摘要/摘要 拟议项目的总体目标是为3-D重建 - 开发开源软件和算法 - 正常组织的多尺度映射。另一个重要的目标是评估衰老和环境的影响 皮肤分子和结构结构的隆起因素。我们将利用我们的成熟（TRL8）技术 - 用于多路复用的二维成像（Cell Dive™）的OGY，以及我们在二维图像分析和机器上的丰富经验 学习。我们选择了正常的皮肤作为有几个原因开发这些工具的器官，a）临床SAM- 来自不同年龄段的元素更容易获得，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各样的细胞外基质（ECM） 致病性侮辱。而ECM，细胞和细胞内分子组成在 我们认为，该程序下开发的许多工具都适用于重建和 绘制高（细胞/亚细胞）分辨率的其他器官模型。该提案将着重于开发算法 - RITHM和一个用于三d组织图像的多尺度映射的框架，该图像将解决Hubmap优先级 围绕定量的3-D图像分析/映射，包括自动3-D图像分割，特征Ex- 牵引和图像注释。将实施生物分子的高分辨率（亚细胞）映射。 用于重建3-D组织并与较低分辨率3-D光学链接的2-D多路复用图像 正常组织的Cal相干断层扫描（OCT）图像。其他细胞级的OMIC数据（例如RNA鱼）将是 以相同的方式映射。低分辨率图像被映射到更高级别的地标（例如器官） 由Hubmap公共坐标框架（CCF）定义。如概述，我们拟议的技术将 包括几个关键功能，这些功能与现有的Hubmap联盟项目和合适 将在3-D组织分析中推进艺术状态。拟议的算法将具有几个关键的Innova- 将在3-D多路复用组织图像分析中推进最新技术的影响。首先，鉴于大量的 要分析的UME，高吞吐量将是每种图像分析算法的关键要求。这将是 我们在平行单元分析管道方面的丰富经验的支持下。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于有效的多种 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，用于组装和 可视化多尺度组织数据称为组织图图图形概述（Tango）。