Bayesian analysis of images to provide fluorescence ultramicroscopy

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Microscopy is a very powerful and important technique in modern cell biology. For example, electron microscopy allows cells to be imaged with a resolution of a few nanometres (nm) in dead cells and fluorescence microscopy allows the positions of specific proteins to be observed in live cells. In fluorescence microscopy the protein in the cell which interests us is dyed with a chemical which can glow (a fluorophore), giving us vital information about how cells function, divide and die. Fluorescence microscopy is limited in resolution by the fundamental physics light. However, recently ways have been found to bypass this limit. For example, imagine that only molecule of dye is glowing. Even though an image of it is blurred into a blob, you can find the position of the centre of the blob very accurately. But this only works if the dot is alone, and so many thousands of images of blurred dots are needed to assemble a picture. In order to spread out the emission of fluorophores over thousands of images we use fluorophores which switch between a state where they are emitting light (on) and a state where they don't (off). This technique, localisation microscopy, can achieve resolutions of a few tens of nm. Currently when a localisation microscopy image is reconstructed from a series of images all the information about when the fluorophores are emitting light is all discarded. We are proposing to create a new data analysis technique which will allow us to extract information about how often each fluorophore switches between the on and off states.In order to extract information about how the fluorophore behaves over time, we will have to create a sophisticated model of the data. To do this we will use Bayesian statistics, in which we build all the information we have about a system into a series of models, and find which is most likely. For example, you could compare one model in which a fluorophore did not switch off and on at all and another in which it switched off and on rapidly. You can gradually change the model to work out which one is the most likely to be correct. The particular power of Bayesian statistics is that you can calculate this even if you do not know what the fluorophore looks like.This information will allow cell biologists to carry out three new types of experiments:1) For some fluorophores, the rate at which it switches between on and off changes as the chemical environment round the fluorophore changes. This would allow us to form an image of how the chemical environment of the cell changes, with a resolution of tens of nm.2) Certain fluorophores change their intensity and the times at which they emit light when they are very close to a second type of fluorophore. This effect is already used to monitor when two different proteins are closer than 10 nm by looking for intensity changes. But if one of the types of fluorophore spontaneously switches between on and off, we will be able to form a localisation image of the cell, with a resolution of tens of nm, and also get information at each point about how close the second type of protein is to the first from the intensity and the times at which the fluorophores switch between on and off.3) Certain fluorophores also change the times at which they emit light when two fluorophores of the same type are close together. This will give us information about where fluorophores are from two different sources: the localisation information and the information about how far apart molecules are. By modelling the fluorophores using both types of information we will create a new ultraresolution fluorescence microscopy technique with a resolution of 5 nm, similar to the resolution achieved by electron microscopy. But unlike electron microscopy, it will be possible to do experiments in live cells, allowing us to look at life in sharper focus than ever before.