Quantitative Tissue Architecture

定量组织结构

• 批准号: RGPIN-2021-03404
• 负责人: Jackson, Hartland
• 金额: $ 2.19万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761240
• 关键词: Quantitative; Tissue; Architecture

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.

多细胞组织的健康功能是多种单个细胞活动的综合结果，在这些组织中，每个细胞的活动取决于其微环境、位置和相互作用。为此，高维成像可用于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已开发的用于液体中单细胞的计算工具，而不是来自图像。为了开发理解复杂组织所需的工具，该研究项目将推动对完整组织内单细胞及其分子成分的空间分辨测量和精确定量所需的技术和分析。我们将进一步标准化和优化高维组织染色工作流程、方法和分析，利用加拿大先进的成像细胞计数技术的优势，扩展其功能，并将其与组织形态定量研究的互补技术相结合。作为经典成像的一种改进，多重染色目前针对单一目标进行了优化。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分析，以定量组织质量，免疫染色和成像细胞计数数据采集。为了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，但是高维图像中的像素分类是人工密集型的，仅限于单个实验，并且基于为培养皿中的细胞设计的分析方法，而不是真实组织。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础事实。在这里，我们将结合荧光和金属标签成像来利用这两种方法的优势来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，以测量数千个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，以量化和模拟细胞组织和分子信号，协调组织和器官的结构，功能和健康。