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Serial Block-Face Scanning Electron Microscopy to Reconstruct Three-Dimensional Tissue Nanostructure

机译:串行块面扫描电子显微镜重建三维组织纳米结构。

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摘要

Three-dimensional (3D) structural information on many length scales is of central importance in biological research. Excellent methods exist to obtain structures of molecules at atomic, organelles at electron microscopic, and tissue at light-microscopic resolution. A gap exists, however, when 3D tissue structure needs to be reconstructed over hundreds of micrometers with a resolution sufficient to follow the thinnest cellular processes and to identify small organelles such as synaptic vesicles. Such 3D data are, however, essential to understand cellular networks that, particularly in the nervous system, need to be completely reconstructed throughout a substantial spatial volume. Here we demonstrate that datasets meeting these requirements can be obtained by automated block-face imaging combined with serial sectioning inside the chamber of a scanning electron microscope. Backscattering contrast is used to visualize the heavy-metal staining of tissue prepared using techniques that are routine for transmission electron microscopy. Low-vacuum (20–60 Pa H2O) conditions prevent charging of the uncoated block face. The resolution is sufficient to trace even the thinnest axons and to identify synapses. Stacks of several hundred sections, 50–70 nm thick, have been obtained at a lateral position jitter of typically under 10 nm. This opens the possibility of automatically obtaining the electron-microscope-level 3D datasets needed to completely reconstruct the connectivity of neuronal circuits.
机译:在生物学研究中,许多长度尺度上的三维(3D)结构信息至关重要。存在极好的方法来获得原子级的分子结构,电子显微镜下的细胞器以及光显微镜下的组织。但是,当需要以数百个微米的分辨率重建3D组织结构,且其分辨率足以跟踪最薄的细胞过程并识别小细胞器(例如突触小泡)时,就会存在一个空白。但是,此类3D数据对于理解蜂窝网络至关重要,尤其是在神经系统中,需要在相当大的空间范围内完全重建蜂窝网络。在这里,我们证明可以通过自动块面成像结合扫描电子显微镜腔室内的连续切片来获得满足这些要求的数据集。反向散射对比度用于可视化使用透射电子显微镜常规技术制备的组织的重金属染色。低真空(20–60 Pa H2O)条件可防止未涂层块表面带电。分辨率足以追踪最薄的轴突并识别突触。在横向位置抖动通常小于10 nm的情况下,已获得数百个截面为50-70 nm厚的堆栈。这为自动获得完全重建神经元回路连通性所需的电子显微镜级3D数据集提供了可能性。

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