Quantitative Tissue Architecture

多细胞组织的健康功能是不同个体细胞活性的综合结果，在这些组织中，每个细胞的活性取决于其微环境，位置和相互作用。为此目的，可以使用高维度成像来识别组织图像内的许多单个细胞的组织，但是这些分析方法目前依赖于已经针对液体中的单个细胞而不是从图像开发的计算工具。 为了开发了解复杂组织所需的工具，该研究计划将推动空间分辨测量和准确定量完整组织内单细胞及其分子组分所需的技术和分析。我们将进一步标准化和优化高维组织染色的工作流程、方法和分析，以利用尖端的成像质谱细胞术技术的优势，扩展其功能，并将其与互补技术相结合，用于组织形态的定量研究。 目标1：用于高维免疫染色的QC工具作为经典成像的一种改进，多重染色目前针对单一靶标进行了优化。我们将优化条件和工作流程，以促进不同目标的同时染色，并确定内部标准和自动化分析，用于组织质量的定量，免疫染色和成像质谱细胞术数据采集。 目标二：空间感知图像细胞术为了识别完整组织块中的单个细胞，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型的，仅限于单个实验，并且基于针对培养皿中的细胞而不是真实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广泛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，以创建一个基础事实，可用于训练灵活，准确的机器学习方法来测量单细胞和组织形态。目标3：跨平台、多尺度组织测量在这里，我们将联合收割机荧光和金属标签成像，利用两种方法的优势，分析大面积组织中的数百万个细胞，并将这种方法与新型空间测序技术相结合，以测量数千种分子因子和细胞表型及其在特定组织结构中的组织。结合这些目标，将开发必要的工具和数据，以量化和模拟协调组织和器官的结构，功能和健康的细胞组织和分子信号。

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.