Integrated Array Tomography for 3D Correlative Light and Electron Microscopy.
Integrated Array Tomography for 3D Correlative Light and Electron Microscopy.
复制标题
DOI：
10.3389/fmolb.2021.822232
复制
发表时间：
2021
影响因子：
5
通讯作者：
Hoogenboom JP
中科院分区：
文献类型：
--
作者：
Lane R;Wolters AHG;Giepmans BNG;Hoogenboom JP
近年来，由于创新的大规模成像方法，生物系统的体积电子显微镜（EM）呈指数级增长。然而，作为一种独立的成像方法，大规模EM通常具有两个主要限制：采集速率慢和难以提供目标生物信息。我们开发了一种3D图像采集和重建管道，通过使用集成在扫描电子显微镜内部的宽视场荧光显微镜克服了这两个限制。工作流程包括采集大视场荧光显微镜（FM）图像，这些图像引导感兴趣的区域进行连续EM（集成相关光和电子显微镜）。使用阴极发光标记实现高精度EM-FM叠加。我们对免疫标记的连续组织切片进行了综合工作流程的概念验证。采集仅限于包含生物目标的区域，加快了总采集时间，并将多余数据的负担减少了数十或数百GB。
Volume electron microscopy (EM) of biological systems has grown exponentially in recent years due to innovative large-scale imaging approaches. As a standalone imaging method, however, large-scale EM typically has two major limitations: slow rates of acquisition and the difficulty to provide targeted biological information. We developed a 3D image acquisition and reconstruction pipeline that overcomes both of these limitations by using a widefield fluorescence microscope integrated inside of a scanning electron microscope. The workflow consists of acquiring large field of view fluorescence microscopy (FM) images, which guide to regions of interest for successive EM (integrated correlative light and electron microscopy). High precision EM-FM overlay is achieved using cathodoluminescent markers. We conduct a proof-of-concept of our integrated workflow on immunolabelled serial sections of tissues. Acquisitions are limited to regions containing biological targets, expediting total acquisition times and reducing the burden of excess data by tens or hundreds of GBs.
登录
查看更多内容
影响因子：
48
作者：
Buhmann J;Sheridan A;Malin-Mayor C;Schlegel P;Gerhard S;Kazimiers T;Krause R;Nguyen TM;Heinrich L;Lee WA;Wilson R;Saalfeld S;Jefferis GSXE;Bock DD;Turaga SC;Cook M;Funke J
通讯作者：
Funke J
影响因子：
2.3
作者：
Liou, W;Geuze, HJ;Slot, JW
通讯作者：
Slot, JW
影响因子：
4.6
作者：
Haring, Martijn T.;Liv, Nalan;Hoogenboom, Jacob P.
通讯作者：
Hoogenboom, Jacob P.
影响因子：
64.8
作者：
Heinrich, Larissa;Bennett, Davis;Weigel, Aubrey V.
通讯作者：
Weigel, Aubrey V.
通过深度学习自动重建电子显微镜体积中的线粒体和内质网
影响因子：
4.3
作者：
Liu, Jing;Li, Linlin;Han, Hua
通讯作者：
Han, Hua