Particle classification and identification in cryoET of crowded cellular environments

拥挤细胞环境中 CryoET 中的颗粒分类和识别

• 批准号: BB/Y514007/1
• 负责人: Martyn Winn
• 金额: $ 18.6万
• 依托单位: Science and Technology Facilities Council
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY514007%2F1
• 关键词: Particle; classification; identification; cryoET; crowded

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.

原位低温电子断层扫描（cryoET）有望揭示细胞内大分子复合物的分布和结构，同时对其天然环境的干扰最小。已经有几项原理验证研究，但该技术的常规应用受到相对嘈杂的数据、拥挤的蜂窝环境以及可以收集的数据集大小的限制。这个问题非常适合人工智能，它可以从大型数据集中学习并给出断层图的无偏差解释。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。在本提案中，我们的目标是研究用于 3D 粒子分类和原位断层扫描识别的人工智能技术。具体来说，我们希望与卡内基梅隆大学的徐敏团队建立合作，他在这一领域已经工作了十多年。我们将在模拟和真实数据集上对他选择的方法进行基准测试，考虑从准确性到易用性等因素。在 CCP-EM 项目中，我们正在开发冷冻胚胎移植的软件管道，因此我们特别寻找可以增强这些管道的人工智能工具。我们评估的一部分将是量化下游结果的改进，例如更高分辨率的亚断层图平均值，为 Xu 提供重要的反馈。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有关疾病相关细胞系和组织的原位冷冻电子发射数据。这些数据集将用于帮助对人工智能工具进行基准测试，同时可能本身就产生重要的研究成果。通过将新颖的人工智能工具集成到我们的 CCP-EM 断层扫描管道中，这项工作将产生更大的影响。这部分取决于实用性，例如软件的鲁棒性以及我们可以轻松地提供经过训练的模型，这将构成该项目的重要组成部分。 简而言之，我们将执行三项任务：（1）安装选定的模块来自 Xu 的 AITom 包以及模拟和真实数据集的基准测试，(2) 将这些工具集成到 CCP-EM 断层扫描管道中，并研究如何在全面调查的背景下优化这些工具，以及 (3) 研究使开发可通用的软件，与类似工具进行比较，并举办研讨会进行传播。显然，将原位冷冻电子断层扫描发展成常规技术还需要大量工作。该提案侧重于一个具体方面，即人工智能方法的应用和适应，以提高可以获得的信息的质量。作为 IPAP 计划的一项提案，我们希望扩大现有的英国和欧洲合作者网络，以引入领先的美国集团。虽然 CCP-EM 联盟也在开发人工智能工具，但徐团队的专业知识是互补的，涵盖了不同的特定人工智能方法，并且更加关注原位断层扫描。