神经元作为低温电子断层扫描的模型系统

IF 3.5 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Structural Biology: X Pub Date : 2022-01-01 DOI:10.1016/j.yjsbx.2022.100067
Benoît Zuber , Vladan Lučić
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引用次数: 3

摘要

低温电子断层扫描(Cryo-ET)提供了独特的机会,以高分辨率成像细胞成分在其原生状态和环境。冷冻电镜技术已应用于许多不同类型的细胞,本文综述其在神经元中的应用。我们表明,神经元是一个多功能系统,可以用来研究一般的细胞成分,如细胞骨架和膜结合细胞器,除了神经元特异性过程,如突触传递。此外,突触为低温et图像处理工具的开发提供了丰富的环境,这些工具适用于阐明生物化学信号级联中的组成和形态异质性大分子复合物的功能和空间组织,这些大分子复合物在其原生的拥挤的细胞环境中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Neurons as a model system for cryo-electron tomography

Cryo-electron tomography (Cryo-ET) provides unique opportunities to image cellular components at high resolution in their native state and environment. While many different cell types were investigated by cryo-ET, here we review application to neurons. We show that neurons are a versatile system that can be used to investigate general cellular components such as the cytoskeleton and membrane-bound organelles, in addition to neuron-specific processes such as synaptic transmission. Furthermore, the synapse provides a rich environment for the development of cryo-ET image processing tools suitable to elucidate the functional and spatial organization of compositionally and morphologically heterogeneous macromolecular complexes involved in biochemical signaling cascades, within their native, crowded cellular environments.

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来源期刊
Journal of Structural Biology: X
Journal of Structural Biology: X Biochemistry, Genetics and Molecular Biology-Structural Biology
CiteScore
6.50
自引率
0.00%
发文量
20
审稿时长
62 days
期刊最新文献
Corrigendum to “Minimizing ice contamination during specimen preparation for cryo-soft X-ray tomography and cryo-electron tomography” [J. Struct. Biol.: X 10(2024) 100113] Structural analysis of the stable form of fibroblast growth factor 2 – FGF2-STAB Localization of albumin with correlative super resolution light- and electron microscopy in the kidney Minimizing ice contamination during specimen preparation for cryo-soft X-ray tomography and cryo-electron tomography Assessment of submicron bone tissue composition in plastic-embedded samples using optical photothermal infrared (O-PTIR) spectral imaging and machine learning
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