Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level

项目摘要 组织切片的数字扫描，包括苏木精和伊红(H&E)染色和 免疫组织化学(IHC)染色的切片，正在成为一种常规的临床程序。技术进步 在成像方面，计算和分子图谱使单细胞的深入组织表征成为可能 分辨率，同时保留细胞的空间信息及其组织学背景。这些因素的汇合 发展为研究肿瘤形态之间的关系创造了前所未有的机会， 分子事件和临床结果。然而，目前还缺乏能够充分利用 在单细胞水平上的组织图像中的全面信息。这项提议的首要目标是 开发ISEE-Cell(基于图像的细胞空间模式浏览器)，这是一套支持图像的信息学工具 以单单元分辨率进行数据分析、空间建模和数据集成。为了实现这一目标，我们 建立了一支强大的研究团队，在图像分析、机器学习、空间分析等方面具有互补的专业知识 建模、单细胞基因组学、癌症病理学和软件开发。具体来说，我们将：1.发展 基于细胞核形态对不同类型的细胞进行分类的算法，将适用于所有类型的 组织图像。2.开发强大的图像修复工具和质量增强器，用于修复模糊区域。 将低分辨率/Magnifi阳离子增强到高分辨率，并对染色颜色进行归一化。3.发展和发展 将组织图像分析、空间建模和可视化工具集成到ISEE-Cell平台中。我们会 让用户，包括信息学家、肿瘤学家、病理学家、外科医生和癌症生物学家参与这一过程 负责算法和工具开发，为拟议的信息学工具收集反馈。所有建议的方法 是由现实世界的生物学和临床应用所推动的。如果成功实施，拟议的研究 将方便用户研究肿瘤微环境，并改进癌症风险评估、诊断、 和结果预测。

Project Summary Digital scanning of tissue slides, including both hematoxylin and eosin (H&E)-stained and immunohistochemistry (IHC)-stained slides, is becoming a routine clinical procedure. Technological advances in imaging, computing and molecular profiling have enabled in-depth tissue characterization at single-cell resolution while retaining the cell spatial information and its histological context. The confluence of these developments has created unprecedented opportunities for studying the relationships among tumor morphology, molecular events, and clinical outcomes. However, there is a lack of computational tools that can fully utilize the comprehensive information in tissue images at the single-cell level. The overarching goal of this proposal is to develop iSEE-Cell (image-based Spatial pattern ExplorEr for Cells), a suite of informatics tools to enable image data analysis, spatial modeling and data integration at single-cell resolution. In order to achieve this goal, we have built a strong research team with complementary expertise in image analysis, machine learning, spatial modelling, single cell genomics, cancer pathology and software development. Specifically, we will: 1. Develop algorithms to classify different types of cells based on nucleus morphology, that will be applicable to all types of tissue images. 2. Develop a powerful image restoration tool and quality enhancer for restoring blurred regions, enhancing low resolution/magnification into high resolution, and normalizing staining colors. 3. Develop and integrate tissue image analysis, spatial modeling and visualization tools into the iSEE-Cell platform. We will engage users, including informaticians, oncologists, pathologists, surgeons and cancer biologists, in the process of algorithm and tool development to collect feedback for the proposed informatics tools. All proposed methods were motivated by real-world biological and clinical applications. If implemented successfully, the proposed study will facilitate users in studying the tumor microenvironment and in improving cancer risk assessment, diagnosis, and outcome prediction.