Particle classification and identification in cryoET of crowded cellular environments

原位低温电子断层扫描(CryoET)有望在对其自然环境的干扰最小的情况下揭示整个细胞中大分子复合体的分布和结构。已经有几项原则验证研究，但这项技术的常规应用受到相对嘈杂的数据、拥挤的蜂窝环境和可以收集的数据集的大小的限制。这个问题非常适合人工智能，因为人工智能可以从大数据集中学习，并对断层图像进行无偏见的解释。然而，训练模型的通用性和研究科学家的可用性仍然存在问题。在这个建议中，我们的目标是研究用于从原位断层图像中进行3D粒子分类和识别的人工智能技术。具体地说，我们希望与卡内基梅隆大学的徐敏团队建立合作关系，他们在这一领域工作了10多年。我们将在模拟和真实数据集上对他的方法选择进行基准测试，考虑从准确性到易用性的各种因素。在CCP-EM项目中，我们正在为CryoET开发软件流水线，因此我们特别寻找能够增强这些流水线的人工智能工具。我们评估的一部分将是量化下游结果的改善，例如更高分辨率的亚断层图像平均值，为徐提供必要的反馈。我们还旨在加强与匹兹堡大学的Zachary Freyberg的合作，我们正在与他一起处理与疾病相关的细胞系和组织的原位冷冻数据。这些数据集将被用来帮助对人工智能工具进行基准测试，同时可能本身就会导致重要的研究成果。通过将新型人工智能工具集成到我们的CCP-EM层析成像管道中，这项工作将产生更大的影响。简而言之，我们将执行三项任务：(1)在模拟和真实数据集上安装从Xu的AITom包和基准程序中选择的模块；(2)将这些工具集成到CCP-EM层析成像流水线中，并在充分调查的情况下研究如何优化工具；以及(3)研究使软件可用于一般用途的实用性，与类似工具进行比较，并举办了一次传播研讨会。显然，将原位低温ET发展成一项常规技术需要大量的工作。这项建议侧重于一个具体方面，即人工智能方法的应用和调整，以提高可获得的信息的质量。作为IPAP计划的一项提议，我们希望扩大我们现有的英国和欧洲合作者网络，引入领先的美国集团。虽然CCP-EM财团也在开发人工智能工具，但徐的团队的专业知识是互补的，涵盖不同的具体人工智能方法，并更专注于原位断层扫描。

In situ cryogenic electron tomography (cryoET) promises to reveal the distribution and structures of macromolecular complexes across the cell with minimal disturbance to their native context. There have been several proof-of-principle studies but the routine application of this technology is limited by the relatively noisy data, the crowded cellular environment, and the size of the datasets that can be collected. The problem is ideally suited to AI which can learn from the large datasets and give bias-free interpretations of tomograms. There are nevertheless issues with generalisability of trained models and useability by research scientists.In this proposal, we aim to look into AI techniques for 3D particle classification and identification from in situ tomograms. Specifically, we wish to establish a collaboration with the group of Min Xu at Carnegie Mellon University, who has worked in this area for more than 10 years. We will benchmark a selection of his methods on simulated and real datasets, considering factors from accuracy through to ease-of-use. Within the CCP-EM project, we are developing software pipelines for cryoET, and so we are particularly looking for AI tools that can enhance these pipelines. Part of our evaluation will be to quantify the improvement in downstream results, for example higher resolution sub-tomogram averages, providing essential feedback to Xu.We also aim to strengthen our collaboration with Zachary Freyberg at the University of Pittsburgh, with whom we are processing in situ cryoET data on disease-associated cell lines and tissues. These datasets will be used to help benchmark the AI tools, while potentially leading to important research outcomes in their own right. By integrating novel AI tools in our CCP-EM tomography pipelines, this work will have a much larger impact. This depends partly on practicalities such as the robustness of the software and the ease with which we can make trained models available, and this will form an important part of the project.Briefly, we will carry out three tasks: (1) Install selected modules from Xu's AITom package and benchmark on simulated and real datasets, (2) Integrate these tools into the CCP-EM tomography pipeline, and investigate how to optimise the tools in the context of a full investigation, and (3) look into the practicalities of making the software available for general usage, compare with similar tools, and host a workshop for dissemination.There is obviously a significant amount of work needed to develop in situ cryoET into a routine techqniue. This proposal focusses on one specific aspect, namely the application and adaptation of AI approaches to improve the quality of information that can be obtained. As a proposal to the IPAP scheme, we look to expand our existing network of UK and European collaborators to bring in leading US groups. While the CCP-EM consortium is also developing AI tools, the expertise of Xu's group is complementary, covering different specific AI approaches and with a stronger focus on in situ tomography.