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

荧光显微镜是一项重要而普遍的技术，在整个生命科学和其他领域都在使用。然而，它的分辨率限制在200纳米左右。2014年，诺贝尔化学奖因超分辨率显微镜的发展而获奖，该技术打破了这一分辨率障碍。这些方法的前沿是单分子定位显微镜(SMLM)。在这里，样品制备和成像是这样的，单个分子可以定位在20纳米左右的精度。这允许映射所有感兴趣的分子的xy坐标。这个项目的中心是开发这类成像的分析软件，包括使用拓扑分析原理来分析蛋白质在细胞表面的纳米级聚集。通过将SMLM与环境敏感的荧光团相结合，通过发射光谱的变化来报告他们当地的生物物理或生化环境，我们可以探测每个位置的细胞膜的性质。有许多这样的探针，但我们特别感兴趣的是那些允许可视化细胞膜中的脂质堆积的探针。我们现在有兴趣进一步开发这项技术-包括建立它与其他探针的用途，例如粘度、pH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图。在生物学上，我们将这些应用于T细胞--免疫系统的白细胞--的研究。T细胞检测体内其他细胞是否有感染迹象，当检测到威胁时必须激活--并避免对身体自身蛋白质的激活。这种微妙的平衡是通过T细胞蛋白质的纳米级组织实现的，我们假设，通过纳米环境，包括我们要绘制的纳米级膜脂域。我们与牛津纳米成像公司(显微镜硬件制造商)合作，目标是优化成像过程。我们还与脂质代谢基因工程专家玛丽亚·马卡洛娃博士合作，以能够改变T细胞纳米环境，从而尝试控制免疫细胞功能。这将使我们有机会将这项技术潜在地应用于治疗方面的好处。最后，在计算机科学学院的Iain Styles教授的帮助下，我们正在开发拓扑学、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洞察力和新的理解。

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.