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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 塑料切片的电子断层扫描 (ET) 因数据信噪比 (SNR) 较低而在质量上受到限制。 对于冷冻水合样品的 ET（冷冻 ET）来说，这个问题甚至更大，因为它们的对比度较低，并且对电子束的损坏非常敏感。 所有电磁样本的信噪比通常是人们试图实现的分辨率的递减函数，严重限制了断层图像中可访问的结构细节的数量。 对于包含给定结构的多个副本的样本，我们开发了一种算法，通过根据断层照片中经常可见的同一结构的多个副本的图像来估计细胞中存在的真实 3D 结构，从而提高 SNR 。 我们的技术建立在为单粒子电子显微镜开发的方法的基础上，主要区别在于对齐和平均发生在 3D 断层扫描体积上。这种方法的优点是可以在原位研究感兴趣的结构，而不必从其细胞环境中分离出来。 我们用于估计真实 3D 粒子结构的算法如下。 使用断层图像中手动选择的粒子位置，切除包含每个粒子的子体积，然后通过在离散欧拉旋转范围内明确比较每个子体积与参考体积来旋转对齐。 子体积比较通常使用傅立叶域局部相关系数序列函数来计算，该函数还提供每次旋转的最佳平移位移。 参考体积可以从粒子集合中选择，或者可以通过粒子子集的成对二叉树对齐来生成无偏参考。 该对准过程通常是迭代的，减少旋转搜索空间和粒度，并允许在每次迭代时更新参考体积。 一旦我们对每个粒子进行了旋转和平移估计，我们就可以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在转换为与参考对齐时的傅里叶分量贡献或缺乏傅里叶分量贡献来补偿单轴倾斜 ET 特征的缺失数据楔形。 定性地讲，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。 从数量上讲，光谱信噪比测量结果表明，SNR 的改善接近于平均粒子数量的预期。 最近，我们向该软件添加了 2 个功能：对多个断层图像中的粒子进行平均的能力，以及在计算机网络中分配最佳对准搜索的能力。 实验表明，在最终平均值中可以更好地考虑缺失的楔形，结果是可以添加具有更多方向变化的粒子。计算时间也从几天缩短到几个小时。