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Cryo-electron Microscopy

Cryo-electron Microscopy

Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...

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相关实验视频

Updated: Jun 7, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

通过自我监督的深度学习来克服冷EM中偏好导向的问题.

Yun-Tao Liu^1,2, Hongcheng Fan^1,2, Jason J Hu^1,2,3

¹Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA.

|November 18, 2024

概括

spIsoNet是一个新的深度学习软件,它通过计算方式解决了冷电子显微镜中首选的方向问题. 它通过改进粒子对齐和地图等性来增强3D重建,而无需复杂的样本准备.

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Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

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Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Published on: July 1, 2022

相关实验视频

Last Updated: Jun 7, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Published on: July 1, 2022

科学领域:

结构生物学结构生物学
生物物理学的生物物理.
计算生物学计算生物学

背景情况:

单粒子冷电子显微镜 (cryo-EM) 允许原子分辨率结构确定宏分子复合体.
粒子定向偏差,或"偏好的方向"问题,使冷电磁数据分析复杂化,导致地图异性和粒子不对齐.
目前的解决方案涉及复杂的生化和物理样本操纵.

研究的目的:

开发一个计算解决方案来解决地图异质性和粒子不对齐,这是由冷EM中首选的方向问题引起的.
推出spIsoNet,一个端到端自主监督的深度学习软件,用于改进冷EM中的3D重建.

主要方法:

开发spIsoNet,一个自我监督的深度学习算法.
使用首选定向视图来从样本不足的定向中恢复分子信息.
适用于各种生物系统,包括核糖体,β-galactosidases和血质素三元体.

主要成果:

在3D重建过程中,spIsoNet显著提高了角同位素和粒子对齐精度.
从具有有限视图的数据集中生成了近同otropic 的重建.
该软件在多种生物样本上表现出有效性,包括先前具有挑战性的血质素片剂数据集.

结论:

spIsoNet为冷EM中首选方向问题提供了一个通用的计算解决方案.
该软件增强了地图同变性和粒子对齐,而不需要额外的样本准备.
spIsoNet适用于单颗粒冷电磁波和偏向导向分子的亚图平均值.