Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Institute of Molecular Biology and Biophysics, ETH Zürich, Zürich, Switzerland.
Nat Methods. 2024 Sep;21(9):1612-1615. doi: 10.1038/s41592-024-02373-9. Epub 2024 Aug 8.
In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time to carefully select targets for high-throughput tilt series acquisition. Here, we developed smart parallel automated cryo-electron tomography (SPACEtomo), a workflow using machine learning approaches to fully automate the entire cryo-electron tomography process, including lamella detection, biological feature segmentation, target selection and parallel tilt series acquisition, all without the need for human intervention. This degree of automation will be essential for obtaining statistically relevant datasets and high-resolution structures of macromolecules in their native context.
原位冷冻电子断层成像技术使研究人员能够在其天然的细胞环境中研究大分子。由于最近软件和硬件的进步,样品变得更容易获得。然而,数据收集仍然需要有经验的操作人员和相当多的显微镜时间来仔细选择用于高通量倾斜系列采集的目标。在这里,我们开发了智能平行自动化冷冻电子断层成像(SPACEtomo),这是一种使用机器学习方法的工作流程,可以完全自动化整个冷冻电子断层成像过程,包括薄片检测、生物特征分割、目标选择和并行倾斜系列采集,而无需人工干预。这种自动化程度对于获得具有统计学意义的大分子天然环境数据集和高分辨率结构至关重要。