Imaging Management and Analysis Core

摘要-成像管理和分析核心 成像管理和分析核心（Imaging Core）将满足三个方面的要求 测量，建模和控制异质性-癌症系统生物学联盟（M2 CH-CSBC） 通过开发和整合最先进的资源， 成像工作流程。具体来说，我们将1）使用高内容成像来测量基线和药物， 在一组细胞系中单独地和组合地观察微环境应答表型，2）优化和 部署新型、高度多参数循环多重免疫荧光（cmIF）成像工作流程， 在2D和3D中测量异质组织中的微环境和药物反应，以及3）部署 基于超分辨光学显微镜和扫描的相关光学/电子显微镜分析 电子显微镜以提供超微结构细胞背景。密切合作执行这些任务 通过这些项目，成像核心将在现有资源的基础上进行扩展， a）以自动方式从多个成像仪器采集图像数据， 精确的协议和维护完整的元数据，B）存储图像和元数据以及所有衍生的和 在高度稳健的数据库和存储库中关联测量数据，具有强大的查询、可视化 和程序员访问工具，c）通过部署和开发最先进的技术来定量分析图像 图像重建方法，图像分割算法，特征提取和分类例程， 标准化程序和机器学习方法来对多分辨率图像特征进行分类 水平，以及d）开发用于与图像和提取的特征交互的新颖的可视化方法，以及 模型与实验元数据和外部数据库的数据相结合。

ABSTRACT – Imaging Management and Analysis Core The Imaging Management and Analysis Core (Imaging Core) will meet the requirements of the three Measuring, Modeling, and Controlling Heterogeneity-Cancer Systems Biology Consortium (M2CH-CSBC) Projects and the Outreach Core by developing and integrating state-of-the-art resources for a complete imaging workflow. Specifically, we will 1) use high-content imaging to measure baseline and drug and microenvironment response phenotypes separately and in combination in a panel of cell lines, 2) optimize and deploy novel, highly multiparameter cyclic multiplexed immunofluorescence (cmIF) imaging workflows to measure microenvironment and drug responses in heterogeneous tissues in 2D and 3D, and 3) deploy correlative light/electron microscopic analysis based on super-resolution light microscopy and scanning electron microscopy to provide the ultrastructural cellular context. To execute these tasks in close collaboration with the Projects, the Imaging Core will expand on existing resources and build a customized imaging environment to a) acquire image data in automated ways from the multiple imaging instruments, following precise protocols and maintaining complete metadata, b) store images and metadata and all derived and associated measurement data in highly robust databases and repositories with powerful query, visualization, and programmer access tools, c) quantitatively analyze images by deploying and developing state-of-the-art image reconstruction methods, image segmentation algorithms, feature extraction and classification routines, normalization procedures, and machine learning approaches to classify image features at multiple resolution levels, and d) develop novel visualization methods for interacting with images and extracted features and models in combination with experimental metadata and data from external databases.