Krentzel Daniel, Elphick Matouš, Domart Marie-Charlotte, Peddie Christopher J, Laine Romain F, Shand Cameron, Henriques Ricardo, Collinson Lucy M, Jones Martin L
Imaging and Modeling Unit, Institut Pasteur, Université Paris Cité, Paris, France.
Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK.
Nat Methods. 2025 Sep 10. doi: 10.1038/s41592-025-02794-0.
Volume correlative light and electron microscopy (vCLEM) is a powerful imaging technique that enables the visualization of fluorescently labeled proteins within their ultrastructural context. Currently, vCLEM alignment relies on time-consuming and subjective manual methods. This paper presents CLEM-Reg, an algorithm that automates the three-dimensional alignment of vCLEM datasets by leveraging probabilistic point cloud registration techniques. Point clouds are derived from segmentations of common structures in each modality, created by state-of-the-art open-source methods. CLEM-Reg drastically reduces the registration time of vCLEM datasets to a few minutes and achieves correlation of fluorescent signal to submicron target structures in electron microscopy on three newly acquired vCLEM benchmark datasets. CLEM-Reg was then used to automatically obtain vCLEM overlays to unambiguously identify TGN46-positive transport carriers involved in protein trafficking between the trans-Golgi network and plasma membrane. Datasets are available on EMPIAR and BioStudies, and a napari plugin is provided to aid end-user adoption.
体积相关光电子显微镜（vCLEM）是一种强大的成像技术，能够在超微结构背景下可视化荧光标记的蛋白质。目前，vCLEM对齐依赖于耗时且主观的手动方法。本文介绍了CLEM-Reg，这是一种通过利用概率点云配准技术实现vCLEM数据集三维自动对齐的算法。点云来自于每种模态中常见结构的分割，由先进的开源方法创建。CLEM-Reg将vCLEM数据集的配准时间大幅缩短至几分钟，并在三个新获取的vCLEM基准数据集上实现了荧光信号与电子显微镜中亚微米级目标结构的相关性。然后，使用CLEM-Reg自动获得vCLEM叠加图，以明确识别参与反式高尔基体网络和质膜之间蛋白质运输的TGN46阳性运输载体。数据集可在EMPIAR和BioStudies上获取，并提供了一个napari插件以帮助终端用户使用。
