Topological data analysis of molecule positions from super-resolution data to map molecular nano-environments in immune cell

对超分辨率数据中的分子位置进行拓扑数据分析，以绘制免疫细胞中的分子纳米环境

• 批准号: 2450687
• 金额: --
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2450687
• 关键词: Topological; data; analysis; molecule; positions

Fluorescence microscopy is a vital and ubiquitous technique used throughout the life sciences and beyond. However, it suffers from a resolution limit of around 200 nm. In 2014, the Nobel Prize for Chemistry was awarded for the development of super-resolution microscopy which breaks this resolution barrier. At the forefront of these methods is single-molecule localisation microscopy (SMLM). Here, sample preparation and imaging are such that individual molecules can be localised with precisions around 20nm. This allows for mapping of the xy coordinates of all molecules of interest. This project is centred on developing analysis software for this type of imaging, including using topological analysis principles to analyse the nano-scale clustering of proteins on the cell surface.By combining SMLM with environmentally sensitive fluorophores, which report on their local biophysical or biochemical environments through changes in their emission spectrum, we can probe properties of the cell membrane at each localisation. There are many such probes, but we are particularly interested in those that allow the visualisation of lipid packing in the cell membranes. We are now interested in further developing this technology - including establishing its use with other probes e.g. for viscosity, pH etc, and developing the topological analysis methodology to allow us to map cellular nano-environments for the first time. Biologically, we apply these to the study of T cells - white blood cells of the immune system. T cells survey other cells in the body for signs of infection and must activate when threats are detected - and avoid activation in response to the body's own proteins. This delicate balance is achieved through the nanoscale organisation of T cell proteins and, we hypothesise, via nano-environments including nanoscale membrane lipid domains which we aim to map.In collaboration with Oxford Nanoimaging (the microscope hardware manufacturer), we aim to optimise the imaging process. We are also collaborating with Dr Maria Makarova, an expert in the genetic engineering of lipid metabolism, to be able to modify T cell nano-environments and thereby try to control immune cell function. This will afford us the opportunity to potentially apply the technology for therapeutic benefit. Finally, with the assistance of Prof. Iain Styles of the School of Computer Science, we are producing topological, machine learning and AI approaches to analysing the data. Ultimately, the biological applications of this project will provide insight and new understandings of how the immune system operates.

荧光显微镜是一种重要的和普遍存在的技术，用于整个生命科学和超越。然而，它受到约200 nm的分辨率限制。2014年，诺贝尔化学奖因超分辨率显微镜的发展而被授予，该显微镜打破了这一分辨率障碍。在这些方法的最前沿是单分子定位显微镜（SMLM）。在这里，样品制备和成像使得单个分子可以以约20 nm的精度定位。这允许映射所有感兴趣分子的xy坐标。该项目的核心是开发用于此类成像的分析软件，包括使用拓扑分析原理分析细胞表面蛋白质的纳米级聚集。通过将SMLM与环境敏感的荧光团结合，通过其发射光谱的变化报告其局部生物物理或生物化学环境，我们可以探测每个位置的细胞膜特性。有许多这样的探针，但我们特别感兴趣的是那些允许在细胞膜中的脂质包装的可视化。我们现在有兴趣进一步开发这项技术-包括建立其与其他探针的使用，例如粘度，pH值等，并开发拓扑分析方法，使我们能够首次绘制细胞纳米环境。从生物学上讲，我们将这些应用于T细胞的研究-免疫系统的白色血细胞。T细胞调查身体中的其他细胞是否有感染的迹象，并且必须在检测到威胁时激活-并避免响应身体自身蛋白质的激活。这种微妙的平衡是通过T细胞蛋白的纳米级组织实现的，我们假设，通过纳米环境，包括我们的目标是映射的纳米级膜脂质域。与牛津Nanoimaging（显微镜硬件制造商）合作，我们的目标是优化成像过程。我们还与脂质代谢基因工程专家Maria Makarova博士合作，能够修改T细胞纳米环境，从而尝试控制免疫细胞功能。这将使我们有机会潜在地应用该技术以获得治疗益处。最后，在计算机科学学院的Iain Styles教授的帮助下，我们正在开发拓扑、机器学习和人工智能方法来分析数据。最终，该项目的生物学应用将为免疫系统如何运作提供新的见解和理解。