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).

项目总结/文摘