PARTICLE ESTIMATION FOR ELECTRON TOMOGRAPHY

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。塑料切片的电子断层成像(ET)质量受到数据的低信噪比(SNR)的限制。这个问题对于冷冻水合样品的ET(低温ET)来说更严重，因为它们对比度低，对电子束的破坏非常敏感。所有EM样本的SNR通常是试图达到的分辨率的递减函数，严重限制了层析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标本，我们开发了一种算法，通过基于通常在断层图像中可见的相同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上，主要区别是对齐和平均发生在3D断层体积上。这种方法的优点是可以在现场研究感兴趣的结构，而不是必须从细胞环境中分离出来。我们估计真实3D粒子结构的算法如下。使用断层图像内人工选择的颗粒位置，切除包含每个颗粒的子体积，然后通过明确地将每个子体积与离散欧拉旋转范围内的参考体积进行比较来旋转对准。子体积比较通常使用傅里叶域局部相关系数序列函数来计算，该序列函数还提供每次旋转的最佳平移。可以从粒子集合中选择参考体积，或者可以通过粒子子集的成对二叉树比对来生成无偏参考。该对准过程通常是迭代的，减少了旋转搜索空间和粒度，并允许在每次迭代时更新参考体积。一旦我们有了每个粒子的旋转和平移估计，我们就通过平均排列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准时的傅立叶分量贡献或缺少来补偿作为单轴倾斜ET特征的缺失数据的楔形。定性上，我们的粒子估计算法允许我们可视化原始断层图中看不到的结构细节。在定量方面，光谱信噪比测量显示，SNR改善接近平均粒子数量的预期。

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.