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

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。一旦与合适的配体结合，这些受体就会产生细胞内信号，允许细胞对给定的刺激做出适当的反应。在细胞刺激过程中，表面受体重组它们的运动和位置。例如，观察到B细胞抗原受体(BCR)会改变其在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后续信号的理解，是我们对控制淋巴细胞激活过程的知识的关键部分。这项建议的主要目的是对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。我们将使用单标记受体的成像来研究在B细胞不同激活状态期间选定的表面分子在B细胞系上的运动。还将使用固定细胞的超分辨率成像来拍摄它们的空间分布快照。利用多色标记，我们将研究多个受体分子的相对运动、共定位和共聚。可光激活的荧光标签将被利用来顺序地从单个细胞获得非常大量的单独轨道。数学和计算工具的开发将有两个主要的分析主题。第一个主题是通过对单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时处理高维数据的改进方法来支持改进的实验解释。这个主题的不同部分建立在我的团队中广泛的最近项目的基础上。第二个主题是开发免疫受体信号的时空数学模型，目的是提高我们对空间效应如何调节细胞信号的理解。这个主题可以追溯到我早先在T细胞信号方面的工作。总体而言，这项拟议的工作旨在为免疫受体运动和信号传递模型带来精确度。这项工作的未来影响将在生物医学领域，最终应用于疾病过程研究和药物发现/设计。虽然我们将使用B细胞信号作为模型系统，但我们的技术和结果将普遍适用于其他免疫细胞，如T细胞和自然杀伤细胞。提出的实验方法是最先进的，与新数学模型的开发和拟合相结合是非常不寻常的。因此，拟议的工作为培养细胞生物学和数学科学之间的高素质人才奠定了完美的基础。

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.