Mesoscale structural biology using deep learning

使用深度学习的介观结构生物学

• 批准号: BB/T011823/1
• 负责人: Susan Cox
• 金额: $ 19.04万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT011823%2F1
• 关键词: Mesoscale; structural; biology; using; deep

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.

细胞中有许多结构，它们每次形成时都被认为是相同的（或几乎相同的）。例如核孔复合体和中心粒。从大约30纳米到一微米的长度尺度的结构可以通过一种称为定位显微镜的荧光显微镜成像，其中每个单独荧光团的位置被发现到高精度。最容易分析且最不可能产生伪影的局部化显微镜方法创建图像，其中3D结构向下投影到2D图像上。这意味着很难推断出3D结构是什么。还有许多其他显微镜技术，特别是低温电子显微镜，也面临着类似的挑战。一般来说，这是通过将图像分成若干类来实现的，然后对这些类进行平均以提高信噪比，然后优化模型以拟合所有信息。然而，局部化显微镜的特性意味着我们可以采取不同的方法，这有可能更好地拟合数据。在定位显微镜中，找到每个单独荧光团的位置，然后通过在每个荧光团的位置处显示高斯来重建样品的图像。这意味着用于显示数据的系统可以很容易地创建为可微分渲染器（即可以计算每个点的一阶导数的显示系统）。我们将使用此属性创建基于深度学习的优化系统，该系统将生成优化的点的3D模型，以描述具有许多结构的2D图像的数据集。该模型将从点的随机分布开始。在优化的每个阶段，模型将与所有2D图像进行比较，并为每个图像找到与数据最佳拟合的角度。然后将改变模型，重复该过程，逐渐优化模型以拟合数据。最终的结果将是一个3D模型，它包含了来自不同2D图像的所有信息。这将是深度学习的一个不寻常的应用，因为网络的训练将导致最终模型的创建，而不是训练一个对人们直接使用有用的网络。由于我们拟合每个单独的图像，因此没有必要对图像进行平均以提高信噪比。对于相对较大的结构，例如我们正在考虑的结构，这是一个优点，因为这些结构可能会在一定程度上弯曲或变形。因此，平均化会淘汰结构。相比之下，我们可以在模型中构建变形，因此即使结构的不同实例之间存在细微变化，也可以获得准确的结构。我们将在模拟和实验数据上测试我们的方法的性能。模拟将使我们能够评估实验效应对我们结果的影响。特别是，存在与每个荧光团的定位相关的不确定性，并且一定比例的蛋白质未被标记或未被检测到。然后，该方法将在不同中心粒蛋白质的实验数据集上进行测试，每个数据集都有数千个中心粒的图像。由于这还不足以训练深度学习网络，我们将进行数据增强，其中图像在x，y平面中轻微移动和旋转以创建新图像。这人为地创建了更多的数据，并帮助网络学习小的移位和旋转。拟合实验数据的结果将与使用另一种超分辨率显微镜技术成像的相同结构的图像进行比较，其中样品嵌入凝胶中，然后扩展。这将使我们有信心，我们的方法是能够重现真实的结构从实验数据。