Quantitative Tissue Architecture

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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.