Methods for generating high-resolution structural models from electron microscope tomography data.
Methods for generating high-resolution structural models from electron microscope tomography data.
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DOI：
10.1016/j.str.2004.07.022
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发表时间：
2004-10
期刊：
影响因子：
5.7
通讯作者：
D. Ress;M. Harlow;R. Marshall;U. J. McMahan
中科院分区：
文献类型：
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作者：
D. Ress;M. Harlow;R. Marshall;U. J. McMahan
由倾斜成像电子显微镜断层扫描生成的重建体提供了目前可用于原位研究细胞结构的最佳空间分辨率。分析通常是通过创建描绘灰度对比边界的表面模型来完成的。在这里，我们引入了一个专门的、方便的分割操作序列来制作这种模型，与现有的方法相比，大大提高了模型的可靠性和空间分辨率，为精确测量提供了基础。为了评估地表模型的可靠性，我们引入了基于灰度梯度尺度长度的空间不确定性测量。通过将模型生成和测量方法应用于合成数据，验证了模型生成和测量方法的有效性，并通过使用它们表征青蛙神经肌肉连接处活性区材料的大分子结构来证明其实用性。
Reconstructed volumes generated by tilt-image electron-microscope tomography offer the best spatial resolution currently available for studying cell structures in situ. Analysis is often accomplished by creating surface models that delineate grayscale contrast boundaries. Here, we introduce a specialized and convenient sequence of segmentation operations for making such models that greatly improves their reliability and spatial resolution as compared to current approaches, providing a basis for making accurate measurements. To assess the reliability of the surface models, we introduce a spatial uncertainty measurement based on grayscale gradient scale length. The model generation and measurement methods are validated by applying them to synthetic data, and their utility is demonstrated by using them to characterize macromolecular architecture of active zone material at the frog's neuromuscular junction.