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: 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=758499
• 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（例如B细胞）使用一系列专门的表面受体感知其环境。结合合适的配体后，这些受体会产生细胞内信号，从而使细胞对给定的刺激做出适当反应。在细胞刺激期间，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）改变了细胞表面的纳米级簇的形式。我们对受体信号如何导致受体运动的变化以及这些动态变化如何影响随后的信号传导的理解是我们了解淋巴细胞激活过程的过程的关键部分。该提案的主要目的是对B细胞受体运动和组织进行定量分析。这将通过超分辨率成像和其他微观成像方法的组合来完成，并通过数学建模在很大程度上支持。 我们将使用单个标记受体的成像研究B细胞的不同激活状态下，在B细胞系上选择的表面分子的运动。它们的空间分布的快照也将使用固定细胞的超分辨率成像进行。使用多色标记，我们将研究多个受体分子的相对运动，共定位和共聚类。将利用可光活化的荧光标签从单个单元格获得大量的单个轨道。 数学和计算工具将通过两个主要主题开发。第一个主题是通过对单个粒子跟踪和活细胞成像数据的图像 /电影分析，应用于固定细胞超分辨率图像的聚类分析以及在理解细胞表型中处理高维数据的精制方法。该主题的不同部分建立在我小组的最新项目上。第二个主题是开发免疫受体信号传导的时空数学模型，目的是提高我们对空间效应如何调节细胞信号传导的理解。该主题链接到我早期在T细胞信号传导方面的工作。总体而言，拟议的工作旨在将免疫受体运动和信号传导模型带来精确性。这项工作的未来影响将在生物医学领域，最终在疾病过程研究和药物发现/设计中应用。尽管我们将使用B细胞信号作为模型系统，但我们的技术和结果通常适用于其他免疫细胞，例如T细胞和天然杀伤细胞。提出的实验方法是最先进的方法，与新数学模型的开发和拟合结合非常不寻常。因此，所提出的工作构成了在细胞生物学和数学科学之间接口上训练高素质人员的理想基础。