Optimising acquisition speed in localisation microscopy

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Fluorescence microscopy is a crucial tool for cell biologists because it allows them to label different proteins with fluorescent molecules (fluorophores) and observe them in live cells. This yields information which can help us to understand diseases, and find new drugs to treat them. Until recently fluorescence microscopy had a major flaw: it could not resolve any features below 200nm. While human cells are at least twenty times this size, there are many parts of a cell which are much smaller. Over the last ten years a number of methods have been developed which allow fluorescence microscopes to image structures below 200nm, and these methods are now becoming standard in fixed (dead) cells. A major challenge in microscopy development is how to apply these methods in live cells, in a way that is reproducible enough that it can be used in cell biology laboratories where there are no experts in the technique. In this proposal we attack this problem with two approaches.Firstly, we will investigate the theoretical limits of localisation microscopy. Localisation microscopy works by taking many images of the sample. The behaviour of the fluorophores is controlled so that in each image only a few of the fluorophores are emitting light. Even though each fluorophore results in a blurred spot, we can find the position of the centre of the spot very accurately. The image of the sample is then built up by putting a point down at the position of all the fluorophores we identify across all the frames. At the moment, people think about localisation microscopy as being similar to other microscopy techniques; you illuminate with light and you get an image, with the quality of the image depending on how good your microscope is and how bright your light is. However, for a localisation image to achieve a high resolution, you have to find the position of lots of fluorophores. Less obviously, the number of frames it takes to get a certain number of fluorophores depends on the structure of your sample, since you cannot image two fluorophores which are too close together. This means that the maximum speed depends on the structure of your sample. We will carry out simulations to work out how the maximum speed depends on the structure, which will allow cell biologists to know in advance what speed can be achieved for a given sample.Secondly, we will develop a method which can examine the raw data from an experiment and determine whether, if you analyse it, you will get an image which reflects the structure of the sample, or if you will get an image with features caused by fitting the positions of fluorophores inaccurately. Currently, it is very hard to work out if this has happened, particularly if you try to get data quickly, which is necessary for live cell experiments. It may be possible to perform a quick test by looking at how the number of fluorophores which is detected changes over time. However, we are likely to need a more sophisticated test. We will use the images from an experiment and create a simulated image where we add a single fluorophore at a known position. We can then run the data analysis and see if the new fluorophore is correctly detected. By moving the fluorophore round, and performing the test on different frames, we will determine if there are particular times or places in the images where the data analysis is not working well. By taking these two approaches, we will give every cell biologist with a localisation microscopy system the tools they need to calculate the maximum speed at which they can image the structure they are interested in. This will bring live cell localisation microscopy out of specialist labs and into the reach of cell biologists. Fixed cell localisation microscopy has already shown us many new and unexpected structures in the cell; by extending this technique into live cells, we will be able to see how these structures change and evolve over time.