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在 Dynamo 中基于自动特征点的冷冻电子断层扫描倾斜系列配准。

Automated fiducial-based alignment of cryo-electron tomography tilt series in Dynamo.

机构信息

Instituto Biofisika (Consejo Superior de Investigaciones Científicas, Universidad del País Vasco), University of Basque Country, 48940 Leioa, Spain.

Center for Cellular Imaging and NanoAnalytics (C-CINA), Biozentrum, University of Basel, Mattenstrasse 26, CH-4058 Basel, Switzerland; Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland.

出版信息

Structure. 2024 Oct 3;32(10):1808-1819.e4. doi: 10.1016/j.str.2024.07.003. Epub 2024 Jul 29.

Abstract

With the advent of modern technologies for cryo-electron tomography (cryo-ET), high-quality tilt series are more rapidly acquired than processed and analyzed. Thus, a robust and fast-automated alignment for batch processing in cryo-ET is needed. While different software packages have made available several approaches for automated marker-based alignment of tilt series, manual user intervention remains necessary for many datasets, thus preventing high-throughput tomography. We have developed a MATLAB-based framework integrated into the Dynamo software package for automatic detection of fiducial markers that generates a robust alignment model with minimal input parameters. This approach allows high-throughput, unsupervised volume reconstruction. This new module extends Dynamo with a large repertory of tools for tomographic alignment and reconstruction, as well as specific visualization browsers to rapidly assess the biological relevance of the dataset. Our approach has been successfully tested on a broad range of datasets that include diverse biological samples and cryo-ET modalities.

摘要

随着现代低温电子断层扫描(cryo-ET)技术的出现,高质量的倾斜系列比处理和分析更快地获得。因此,需要一种强大且快速的自动化对准方法,以便在 cryo-ET 中进行批量处理。虽然不同的软件包已经提供了几种基于标记的自动对准倾斜系列的方法,但对于许多数据集仍然需要手动用户干预,从而阻止了高通量断层扫描。我们开发了一个基于 MATLAB 的框架,集成到 Dynamo 软件包中,用于自动检测基准标记,该框架生成一个具有最小输入参数的稳健对准模型。这种方法允许高通量、无监督的体积重建。这个新模块通过大量用于断层扫描对准和重建的工具以及特定的可视化浏览器来扩展 Dynamo,以快速评估数据集的生物学相关性。我们的方法已经在广泛的数据集上成功测试,包括各种生物样本和 cryo-ET 模式。

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