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

项目摘要/摘要 拟议项目的总体目标是开发用于3D重建的开源软件和算法- 并对正常组织进行多尺度标测。另一个重要目标是评估老龄化和环境的影响。 心理因素对皮肤分子和结构结构的影响。我们将利用我们成熟的(TRL8)技术- 用于多路复用2-D成像的OGEM(CELL Dive™)，以及我们在2-D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器官来开发这些工具有几个原因，a)临床应用 来自不同年龄段的PLE更容易获得，b)这是独立捕捉变化的好模型 在细胞外基质(ECM)中由于年龄和正常暴露于环境因素以及各种 病原性的侮辱。虽然细胞外基质、细胞和细胞内分子组成在不同的 对于各种器官，我们相信在该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图。该提案将重点发展算法-- Rithms和一个多尺度绘制3-D组织图像的框架，它将解决HuBMAP的优先事项 围绕定量的3-D图像分析/映射，包括自动3-D图像分割、除 牵引力和图像注释。生物分子的高分辨率(亚细胞)作图将使用- ING 2-D多路传输图像，用于重建3-D组织，并链接到较低分辨率的3-D光学元件- 正常组织的CAL相干断层扫描(OCT)图像。其他细胞级别的基因组数据(例如，RNA FISH)将是 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公共坐标框架(CCF)定义。正如所概述的，我们提议的技术将在- 包括几个重要的关键功能，这些功能对现有HuBMAP联盟项目和 将推动3D组织分析的最新技术。拟议的算法将有几个关键的创新- 这将推动3-D多路组织图像分析的技术水平。首先，考虑到巨大的体积- 要分析的UMES，高吞吐量将是每个图像分析算法的关键要求。这将是 由我们在单细胞分析流水线并行化方面的丰富经验支持。第二，建议的算法如下： Rithms将以多个比例对图像进行分割。第三个创新领域将重点放在高效的多领域 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来组装和 可视化多尺度组织数据，称为组织图谱导航图形概述(Tango)。

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).