Mesoscale structural biology using deep learning

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There are many structures in the cell which are thought to be the same (or almost the same) every time they form. Examples include the nuclear pore complex and the centriole. Structures from a lengthscale from around 30nm to a micron can be imaged by a form of fluorescence microscopy called localisation microscopy, where the position of each individual fluorophore is found to high precision. The localisation microscopy methods which are simplest to analyse and least likely to produce artifacts create images where the 3D structure is projected down onto a 2D image. This means that it is difficult to deduce what the 3D structure is. There are a number of other microscopy techniques, particularly cryo-electron microscopy, which have faced similar challenges. In general, this is approached by putting images into a number of classes which are then averaged to improve signal to noise and a model is then optimised to fit all of the information. However, there is a property of localisation microscopy which means that we can take a different approach, which has the potential to fit the data much better. In localisation microscopy the position of each individual fluorophore is found, and the image of the sample is then reconstructed by displaying a Gaussian at the location of each fluorophore. This means that the system used to display the data can be easily created as a differentiable renderer (i.e. a system of display where the first derivative at each point can be calculated).We will use this property to create a deep learning based optimisation system which will generate an optimised 3D model of points to describe a dataset with many 2D images of the structure. The model will start off as a random distribution of points. At each stage of the optimisation the model will be compared to all the 2D images, and for each of them the angle which produces the best fit to the data will be found. The model will then be changed and the process repeated, gradually optimising the model fit the data. The final result will be a 3D model which incorporates all the information from the different 2D images. This will be an unusual application of deep learning, since instead of training a network which will be useful for people to use directly, the training of the network will lead to the creation of the final model. Since we are fitting to each individual image, it will not be necessary to perform averaging of the images to improve the signal to noise. For relatively large structures such as the ones we are considering, this is an advantage because the structures are likely to flex or deform to some extent. Averaging would therefore wash out structure. In contrast, we can build deformation into our model and therefore will get an accurate structure back even if there are slight variations between different instances of the structure.We will test the performance of our method on simulations and experimental data. Simulations will allow us to assess the impact that experimental effects will have on our results. In particular, there is an uncertainty associated with the localisation of each fluorophore, and a certain proportion of the proteins are either not labelled or not detected. The method will then be tested on experimental datasets of different centriole proteins, each with several thousand images of individual centrioles. Since this is not enough to train a deep learning network, we will carry out data augmentation, in which the image is shifted slightly and rotated in the x,y plane to create new images. This artificially creates more data and assists the network in learning small shifts and rotations. The results of fitting to experimental data will be compared to images of the same structures imaged using another super-resolution microscopy technique where the sample is embedded in a gel which is then expanded. This will allow us to be confident that our method is able to reproduce real structure from experimental data.