Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking.
Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking.
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DOI：
10.1186/s12859-016-1283-3
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发表时间：
2016-10-05
影响因子：
3
通讯作者：
Alber F
中科院分区：
文献类型：
--
作者：
Pei L;Xu M;Frazier Z;Alber F
冷冻电子断层扫描是研究接近天然状态的大分子复合物结构的重要工具。全细胞冷冻电子断层扫描包含其所有大分子复合物的结构信息。然而，提取这些信息仍然具有挑战性，并且依赖于复杂的图像处理，特别是无模板的粒子提取、分类和平均。为了开发这些方法，至关重要的是真实地模拟拥挤细胞环境的断层图，然后将其用作评估和优化细胞断层图中复合物检测方法的地面实况模型。我们提出了一个框架来生成大分子复合物的拥挤混合物，用于真实地模拟低温电子断层扫描，包括由于楔形缺失效应导致的噪声和图像失真。然后使用模拟断层图来评估无模板高斯差分 (DoG) 粒子选取方法，以检测不同拥挤和噪声水平下不同形状和大小的复合物。我们确定了 DoG 参数设置，可以最大限度地提高检测各种尺寸和形状的颗粒的精度和召回率。我们观察到中等大小的 DoG 缩放因子显示出整体最佳性能。为了进一步提高性能，我们提出了一种整合多个参数设置结果的组合策略。随着大分子拥挤程度的增加，粒子拾取的精度仍然较高，而召回率却急剧下降，这限制了在拥挤环境中检测足够的复合物拷贝数。在噪声水平不断增加的大范围内，DoG 粒子拾取性能保持稳定，但在超过特定噪声阈值时会显着降低。自动且无参考的颗粒拾取是细胞断层图可视化蛋白质组学分析的重要第一步。然而，细胞的细胞质非常拥挤，这使得颗粒检测具有挑战性。因此，在现实的拥挤环境中测试粒子拾取方法非常重要。在这里，我们提出了一个用于模拟高拥挤水平下细胞环境断层图的框架，并评估 DoG 粒子拾取方法。我们确定了最佳参数设置，以最大限度地提高 DoG 粒子拾取方法的性能。本文的在线版本 (doi:10.1186/s12859-016-1283-3) 包含补充材料，可供授权用户使用。
Cryo-electron tomography is an important tool to study structures of macromolecular complexes in close to native states. A whole cell cryo electron tomogram contains structural information of all its macromolecular complexes. However, extracting this information remains challenging, and relies on sophisticated image processing, in particular for template-free particle extraction, classification and averaging. To develop these methods it is crucial to realistically simulate tomograms of crowded cellular environments, which can then serve as ground truth models for assessing and optimizing methods for detection of complexes in cell tomograms. We present a framework to generate crowded mixtures of macromolecular complexes for realistically simulating cryo electron tomograms including noise and image distortions due to the missing-wedge effects. Simulated tomograms are then used for assessing the template-free Difference-of-Gaussian (DoG) particle-picking method to detect complexes of different shapes and sizes under various crowding and noise levels. We identified DoG parameter settings that maximize precision and recall for detecting particles over a wide range of sizes and shapes. We observed that medium sized DoG scaling factors showed the overall best performance. To further improve performance, we propose a combination strategy for integrating results from multiple parameter settings. With increasing macromolecular crowding levels, the precision of particle picking remained relatively high, while the recall was dramatically reduced, which limits the detection of sufficient copy numbers of complexes in a crowded environment. Over a wide range of increasing noise levels, the DoG particle picking performance remained stable, but dramatically reduced beyond a specific noise threshold. Automatic and reference-free particle picking is an important first step in a visual proteomics analysis of cell tomograms. However, cell cytoplasm is highly crowded, which makes particle detection challenging. It is therefore important to test particle-picking methods in a realistic crowded setting. Here, we present a framework for simulating tomograms of cellular environments at high crowding levels and assess the DoG particle picking method. We determined optimal parameter settings to maximize the performance of the DoG particle-picking method. The online version of this article (doi:10.1186/s12859-016-1283-3) contains supplementary material, which is available to authorized users.
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影响因子：
48
作者：
Beck, Martin;Malmstroem, Johan A.;Lange, Vinzenz;Schmidt, Alexander;Deutsch, Eric W.;Aebersold, Ruedi
通讯作者：
Aebersold, Ruedi
影响因子：
2.2
作者：
McMullan, G.;Chen, S.;Henderson, R.;Faruqi, A. R.
通讯作者：
Faruqi, A. R.
DOI：
10.1073/pnas.1114477108
发表时间：
2011-11-01
影响因子：
11.1
作者：
Dill, Ken A.;Ghosh, Kingshuk;Schmit, Jeremy D.
通讯作者：
Schmit, Jeremy D.
使用相关的活细胞显微镜和冷冻电子断层扫描直接可视化HIV-1。
影响因子：
5.7
作者：
Jun, Sangmi;Ke, Danxia;Debiec, Karl;Zhao, Gongpu;Meng, Xin;Ambrose, Zandrea;Gibson, Gregory A.;Watkins, Simon C.;Zhang, Peijun
通讯作者：
Zhang, Peijun
影响因子：
9.8
作者：
Russel D;Lasker K;Webb B;Velázquez-Muriel J;Tjioe E;Schneidman-Duhovny D;Peterson B;Sali A
通讯作者：
Sali A