Bayesian and machine-learning-based analysis of high-volume super-resolution microscopy data for molecular-level cell phenotyping

单分子定位显微镜（SMLM）的使用在生物研究界正在蓬勃发展。通过利用荧光分子的时间分离，蛋白质可以定位到纳米尺度的空间精度，使研究人员能够以前所未有的细节水平探索细胞过程的结构和动力学。然而，对这些数据的分析仍然局限于很大程度上的定性和基于重建图像的视觉检查。鉴于贝叶斯和机器学习统计方法最近取得的惊人进展，有机会开发一种完全定量的替代方法。事实上，我们建议我们可以学习分子水平细胞结构的完整生成模型。这种方法将使我们能够回答以前不可能的与亚细胞结构的组织和共同组织有关的表型生物学问题，例如：“膜蛋白的聚集是由底层肌动蛋白细胞骨架引起的吗？”（假设检验问题）“药物是否会改变膜蛋白和细胞骨架之间的关系”（分类问题）“癌细胞在正常人群中的出现是否可以从其纳米级结构的变化中检测到？”（异常检测问题）我们假设复杂的结构安排可以通过更简单的形态元素的组成和协调来描述。在最基本的情况下，它们可能是简单的分子簇或分子聚集。更复杂的元素可能包括复杂的分支纤维结构。我们一直处于为SMLM开发聚类分析方法的前沿。我们将通过首先为2和3D SMLM数据构建一个完整的集群和光纤分析工具包来解决描述蜂窝架构的（更大）问题。接下来，我们将获取大量多色SMLM数据，并开发贝叶斯机器学习方法，以学习更简单形态元素的概率组成和协调（例如，通过我们的工具包识别），从而为细胞结构提供完整的生成模型。一旦实现，我们将进一步开发统计和机器学习层的推理方法，以解决上述表型问题。在这个项目中，我们将研究的主要生物学应用是T细胞免疫突触的结构，这是调节T细胞激活和免疫反应的关键。这为与临床医生和药物开发人员合作测试治疗剂如何影响分子水平的细胞结构提供了令人兴奋的机会。

The use of Single Molecule Localisation Microscopy (SMLM) is booming within the biological research community. By exploiting the temporal separation of fluorescent molecules, proteins can be localised to nano-scale spatial precision allowing researchers to explore the structure and dynamics of cellular processes at unprecedented levels of detail. However, analysis of such data is still restricted to be largely qualitative and based around visual inspection of reconstructed images. Given the fantastic recent progress of Bayesian and machine-learning statistical methodology, there is the opportunity to develop a fully quantitative alternative approach. In fact, we propose that we can learn a full generative model for molecular-level cellular architecture. This approach will allow us to answer previously impossible phenotypical biological questions relating to the organisation and co-organisation of sub-cellular structures, for example: 1. "Is the clustering of a membrane protein caused by the underlying actin cytoskeleton?" (the hypothesis testing problem)2. "Does a drug change the relationship between a membrane protein and the cytoskeleton" (the classification problem)3. "Is the emergence of a cancerous cell in a normal population detectable from changes in its nanoscale architecture" (the anomaly detection problem)We postulate that complex architectural arrangements can be described by the composition and coordination of simpler morphological elements. At their most basic, these might be simple clusters or aggregates of molecules. More complicated elements could include a complex, branched fibrous structure. We have been at the forefront of developing cluster analysis methodology for SMLM. We will approach the (much) larger problem of describing cellular architecture by first building a full cluster and fibre analysis toolkit for 2 and 3D SMLM data. Next, we will acquire high-volume multi-colour SMLM data and develop Bayesian machine-learning methodology to learn the probabilistic composition and coordination of simpler morphological elements (e.g. as identified through our toolkit) to give a full generative model for cellular architecture. Once achieved, we will further develop a statistical and machine-learning layer of inference methodology to address phenotypical questions such as those given above. The main biological application we will examine during this project is the architecture of the T cell immunological synapse which is key for regulating T cell activation and hence the immune response. This opens the exciting opportunity to collaborate with clinicians and drug developers to test how therapeutic agents affect molecular-level cellular architecture.