Super-resolution imaging, single cell analysis, and modelling approaches applied to the study of immune cell signaling and behaviour

超分辨率成像、单细胞分析和建模方法应用于免疫细胞信号传导和行为的研究

• 批准号: RGPIN-2020-04820
• 负责人: Coombs, Daniel
• 金额: $ 5.03万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717425
• 关键词: Super; resolution; imaging; single; cell

Cells of the immune system, such as B cells, sense their environment using an array of specialized surface receptors. Upon binding a suitable ligand, these receptors generate intracellular signals, allowing the cell to respond appropriately to a given stimulus. During cellular stimulation, surface receptors reorganize their movement and location. For example, B cell antigen receptors (BCR) are observed to change the form of their nanoscale clusters on the cell surface. Our understanding of how receptor signaling leads to changes in receptor motion, and how these dynamic changes affect subsequent signaling, is a key part of our knowledge of the processes governing lymphocyte activation. The main objective of this proposal is to perform quantitative analysis of B cell receptor motion and organization. This will be done using a combination of super-resolution imaging and other microscopic imaging approaches, supported to a great extent by mathematical modeling. We will use imaging of single labelled receptors to study the motion of chosen surface molecules on B cell lines, during different activation states of the B cell. Snapshots of their spatial distribution will also be made using super-resolution imaging of fixed cells. Using multicolour labelling we will investigate relative motion, co-localization and co-clustering of multiple receptor molecules. Photoactivatable fluorescent tags will be exploited to obtain, sequentially, a very large number of individual tracks from a single cell. Mathematical and computational tools will be developed with two main themes of analysis. The first theme is to support improved experimental interpretation, through image / movie analysis of single particle tracking and live cell imaging data, clustering analysis applied to fixed cell super-resolution images, and refined approaches to handling high-dimensional data in understanding cell phenotypes. The different parts of this theme build on extensive recent projects in my group. The second theme is to develop spatiotemporal mathematical models of immune receptor signaling, with the goal of improving our understanding of how spatial effects modulate cell signalling. This theme links back to my earlier work on T cell signalling. Overall, the proposed work seeks to bring precision to models of immune receptor motion and signaling. The future effects of this work will be in the biomedical field with eventual applications in disease process studies and drug discovery/design. Although we will use B cell signaling as the model system, our techniques and results will be generally applicable to other immune cells such as T cells and Natural Killer cells. The experimental approaches proposed are state-of-the-art and the combination with development and fitting of new mathematical models is extremely unusual. Hence, the proposed work forms a perfect basis for training of highly qualified personnel at the interface between cell biology and the mathematical sciences.

免疫系统的细胞，如B细胞，通过一系列特殊的表面受体来感知环境。在与合适的配体结合后，这些受体产生细胞内信号，使细胞对给定的刺激作出适当的反应。在细胞刺激过程中，表面受体重组其运动和位置。例如，观察到B细胞抗原受体（BCR）改变其在细胞表面的纳米级簇的形式。我们对受体信号传导如何导致受体运动变化，以及这些动态变化如何影响后续信号传导的理解，是我们了解淋巴细胞活化过程的关键部分。本提案的主要目的是对B细胞受体的运动和组织进行定量分析。这将使用超分辨率成像和其他显微成像方法的组合来完成，并在很大程度上得到数学建模的支持。