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

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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.