PARTICLE ESTIMATION FOR ELECTRON TOMOGRAPHY

电子断层扫描的粒子估计

• 批准号: 8170828
• 负责人: CINDY L SCHWARTZ
• 金额: $ 4.98万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8170828
• 关键词: Accounting; Algorithms; Cells; Characteristics; Collection; Computer Retrieval of Information on Scientific Projects Database; Computer software; Data; Electron Beam; Electron Microscopy; Freezing; Funding; Grant; Hour; Image; In Situ; Institution; Location; Measurement; Noise; Plastics; Procedures; Research; Research Personnel; Resolution; Resources; Rotation; Sampling; Signal Transduction; Source; Specimen; Structure; Techniques; Time; Tomogram; Translations; Trees; United States National Institutes of Health; Update; Variant; base; computer network; electron tomography; improved; interest; particle; research study; three dimensional structure

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Electron tomography (ET) of plastic sections is limited in quality by the low signal-to-noise ratio (SNR) of the data. This problem is even greater for ET of frozen-hydrated samples (cryo ET) because they have low contrast and are very sensitive to damage by the electron beam. SNR for all EM samples is typically a decreasing function of resolution one is trying to achieve, severely limiting the amount of structural detail that is accessible in a tomogram. For specimens containing multiple copies of a given structure, we have developed an algorithm to improve the SNR by estimating the true 3D structure that is present in the cell, based on the images of multiple copies of the same structure that are often visible in a tomogram. Our technique builds on the approach developed for single-particle electron microscopy, with the primary difference that alignment and averaging occur over the 3D tomographic volume. The advantage of this approach is that the structure of interest can be studied in situ instead of having to be isolated from its cellular context. Our algorithm for estimating the true 3D particle structure is as follows. Using manually selected particle locations within the tomogram, a sub volume containing each particle is excised and then aligned rotationally by explicitly comparing each sub volume with a reference volume over a range of discrete Euler rotations. Sub volume comparison is typically computed using a Fourier domain local correlation coefficient sequence function, which also provides the optimal translational shift for each rotation. The reference volume can be chosen from the collection of particles, or an unbiased reference can be generated by a pair-wise binary tree alignment of a subset of particles. This alignment procedure is typically iterated, reducing the rotational search space and granularity, and allowing an update of the reference volume at each iteration. Once we have rotation and translation estimates for each particle we estimate the particle volume by averaging the aligned sub volumes. We compensate for the wedge of missing data that is characteristic of single-axis tilting ET by accounting for the Fourier component contribution, or lack thereof, from each particle as it is transformed into alignment with the reference. Qualitatively, our particle estimation algorithm allows us to visualize structural details that are not visible in the original tomogram. Quantitatively, the spectral-signal-to-noise ratio measurements show SNR improvements close to that expected for the number of particles averaged. During the recent past, we have added 2 functions to this software: the ability to average particles from multiple tomograms and the ability to distribute the search for optimal alignment across a network of computers. Experiments have shown that the missing wedge is better accounted for in the final average with the result that particles with more orientational variations can be added. The computation time has also been cut from a few days to a few hours.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 塑料部分的电子断层扫描（ET）受数据的低信噪比（SNR）的质量限制。 对于冷冻水合样品（冷冻ET）的ET，此问题甚至更大，因为它们的对比度较低，并且对电子束的损伤非常敏感。 所有EM样品的SNR通常是一个试图实现的分辨率的降低功能，严重限制了在断层图中可访问的结构细节量。 对于包含给定结构的多个副本的样品，我们根据估计单元格中存在的真实3D结构来改善SNR，以改善SNR，这是基于通常在Tomogram中可见的相同结构的多个副本的图像。 我们的技术基于用于单粒子电子显微镜开发的方法，其主要差异是在3D断层扫描量中进行比对和平均。这种方法的优点是，可以在原位研究感兴趣的结构，而不必与其细胞环境隔离。 我们用于估计真实3D粒子结构的算法如下。 使用在断层图内手动选择的粒子位置，切除包含每个粒子的子体积，然后通过将每个子体积与参考音量与一系列离散的EULER旋转范围的参考音量明确比较，然后将旋转对齐。 通常使用傅立叶域局部相关系数序列函数来计算子体积比较，这也为每个旋转提供了最佳的转换。 可以从粒子的集合中选择参考量，也可以通过一对颗粒子集的成对二进制树比对生成无偏的参考。 通常会迭代此对齐过程，从而减少旋转搜索空间和粒度，并允许在每次迭代时更新参考量。 一旦我们进行了每个粒子的旋转和翻译估计，我们通过平均对齐的子体积来估计粒子体积。 我们通过考虑每个粒子的傅立叶成分贡献或缺乏单轴倾斜ET的特征来补偿单轴倾斜ET的特征，因为它们与参考转换为对齐。 定性地，我们的粒子估计算法使我们能够可视化原始断层图中不可见的结构细节。 数量上，光谱信号与噪声比的测量表明，SNR的改进接近于平均粒子数量的预期。 在最近的过去，我们在此软件中添加了2个功能：从多个断层图中平均粒子的能力，以及在计算机网络上分配搜索最佳对齐的能力。 实验表明，最终平均值可以更好地解释缺失的楔形物，结果可以添加具有更大定向变化的粒子。计算时间也已从几天减少到几个小时。