Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes—ranging from endoplasmic reticulum to microtubules to ribosomes—in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM)¹. We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, ‘OpenOrganelle’, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

细胞包含数百个细胞器和大分子组装体。要全面了解其错综复杂的组织结构，需要对整个细胞进行纳米级的三维重建，而这只有通过强大且可扩展的自动方法才能实现。在这里，为了支持这种方法的开发，我们注释了多达 35 种不同的细胞器类别——从内质网到微管再到核糖体——在来自多种细胞类型的不同样本体积中，以每体素 4 nm 的近各向同性分辨率成像聚焦离子束扫描电子显微镜 (FIB-SEM) ¹. 我们训练了深度学习架构，以每体素 FIB-SEM 体积 4 nm 和 8 nm 分割这些结构，验证了它们的性能，并表明自动重建可用于直接量化以前无法访问的指标，包括细胞成分之间的空间相互作用。我们还表明，这种重建可用于自动配准光学和电子显微镜图像以进行相关研究。我们创建了一个开放数据和开源网络存储库“OpenOrganelle”，用于共享数据、计算机代码和经过训练的模型，这将使​​世界各地的科学家能够查询并进一步改进这些数据集的自动重建。