Quantitative Tissue Architecture

多细胞组织的健康功能是不同个体细胞活动的综合结果，在这些组织中，每个细胞的活动依赖于其微环境、位置和相互作用。为此，高维成像可以用来识别组织图像中许多单个细胞的组织，但这些分析方法目前依赖于为液体中的单个细胞开发的计算工具，而不是来自图像。为了开发理解复杂组织所需的工具，该研究计划将推动对完整组织中单个细胞及其分子成分进行空间分辨测量和准确量化所需的技术和分析。我们将进一步规范和优化高维组织染色工作流程、方法和分析，利用加拿大尖端成像质谱仪技术的优势，扩展其能力，并将其与互补技术相结合，用于组织形态的定量研究。目的1：高维免疫染色的质量控制工具作为经典成像的改编，多重染色目前针对单个靶点进行了优化。我们将优化条件和工作流程，以促进不同目标的同时染色，并为组织质量的量化、免疫染色和成像质量细胞分析数据采集确定内部标准和自动分析。目的2：空间感知图像细胞术识别完整组织中的单个细胞，细胞类型特异性标记可以帮助区分相邻细胞，但高维图像中的像素分类是手动密集的，限于单个实验，并且基于针对培养皿中的细胞而不是真正的组织设计的分析方法。为了充分利用多路复用成像数据中可用的大量信息并简化未来的分析，我们将在复杂组织的单个细胞中引入标签，以创建可用于训练灵活、准确的机器学习方法来测量单个细胞和组织形态的基本事实。目的3：跨平台、多尺度的组织测量在这里，我们将结合荧光成像和金属标签成像，利用这两种方法的优点来分析大面积组织中的数百万个细胞，并将该方法与新的空间测序技术相结合，以测量特定组织结构中的数千个分子因子和细胞表型及其组织。结合这些目标，将开发必要的工具和数据来量化和建模细胞组织和分子信号，以协调组织和器官的结构、功能和健康。

The healthy function of multi-cellular tissues is the integrated outcome of the activity of diverse individual cells, and within these tissues, the activity of each cell is dependent on its microenvironment, location and interactions. For this purpose, high dimension imaging can be used to identify the organization of many individual cells within images of tissue, but these analysis methods are currently dependent on computational tools which have been developed for single cell in liquids not from images. In order to develop the tools necessary to understand complex tissues, this research program will push forward the technologies and analysis necessary for the spatially resolved measurement and accurate quantification of single cells and their molecular components within intact tissues. We will further standardize and optimize high-dimension tissue staining workflows, methods, and analysis, to utilize the strengths of cutting edge Canadian-built imaging mass cytometry technology, extend its capabilities, and combine its use with complementary technologies for the quantitative study of tissue morphology. Aim 1: QC Tools for high-dimension immunostaining As an adaptation of classic imaging, multiplexed stains are currently optimized for single targets. We will optimize conditions and workflows to facilitate the simultaneous staining of diverse targets and identify internal standards and automated analysis for the quantification of the quality of tissues, immunostaining, and imaging mass cytometry data acquisition. Aim 2: Spatially aware image cytometry To identify individual cells within intact pieces of tissue, cell type specific markers can help to distinguish neighbouring cells, but pixel classification in high-dimension images is manually intensive, limited to a single experiment, and based on analysis methods designed for cells in a dish, not a real tissue. To fully utilize the extensive information available in multiplexed imaging data and ease future analysis, we will introduce tags into the individuals cells of complex tissues to create a ground truth that can be used to train flexible, accurate machine learning methods to measure single cells and tissue morphology. Aim 3: Cross-platform, multi-scale tissue measurements Here, we will combine fluorescence and metal tag imaging to utilize the benefits of both methods in order to analyze millions of cells across large areas of tissue, and combine this method with novel spatial sequencing technologies in order to measure thousands of molecular factors and the cell phenotypes and their organization within specific tissue architectures. Combined these aims will develop the tools and data necessary to quantify and model the cellular organization and molecular signals which coordinate the structure, function and healthy of tissues and organs.