Hybrid Model-Based and Data-Driven Frameworks for High-Resolution Tomographic Imaging

摘要 结构生物学的最终目标是可视化生物分子在其自然环境中的作用，并 建立它们的结构-功能关系。细胞冷冻电子和X射线断层扫描已经出现 强大的复杂生物样品成像技术，如完整的细胞、细胞器、大分子 机器，并用于在原位量化处于其自然状态的生物对象的内部组织 分辨率从几微米到几十纳米的X射线到几十埃的电子。 然而，与成熟的X射线结晶学和单粒子低温电子技术相比， 在显微镜下，细胞断层扫描由于分辨率的严重下降而尚未发挥其潜力。 由于机械错位和非刚性试件变形的影响而进行的重建 辐射损伤、缺失楔形伪影、拥挤环境中的低信噪比以及未解决的问题 构象非均质性。为了解决这些问题，我们建议利用我们的新方法实现自动化 联合3D对齐和正则化重建，结合了迭代投影方法和 凸优化以获得比最先进的重建分辨率更好的严重未对准 数据。在我们的框架中注入数学建模和机器学习方面的新进展提供了一个 为一系列新的基于模型和数据驱动的算法扫清道路，这些算法可以解决当前的挑战，并 细胞断层扫描数据分析中的瓶颈。特别是，我们建议(1)开发技术用于 改进的倾斜序列对齐，考虑了样品的刚体运动并恢复了各向异性 通过使用光流对准来实现辐射诱导翘曲的效果；(2)开发一种利用 包含在细胞体积中随机取向的大分子中的全频率信息，以限制 楔形缺失的影响；以及(3)提高从精化体提取的亚断层图像的分辨率 通过开发体积编码-变形-解码深度神经网络对构象进行建模 异质性。通过开发新的数据驱动方法来约束缺失楔形信息并处理 形状可变性作为非刚性变形的连续体，而不是离散的簇，我们的算法 框架将在分辨率和重建质量方面比目前有显著改进 现有的数据分析方法忽略了这些影响。随着结构生物界越来越多地 专注于细胞断层扫描，对易于使用的自动化软件的需求日益增长 有经验和新手的用户。(4)为此，由该提议产生的算法将被转化为GPU- 使开源的用户友好软件能够加快对不断增长的成像数据池的分析。 最终，我们的算法框架将能够从噪声、 不完整和复杂的数据，从而增强了细胞断层扫描的预测能力 重要的生物学问题。

ABSTRACT The ultimate goal of structural biology is to visualize biomolecules in action in their native environment and to establish their structure-function relationship. Cellular cryo-electron and X-ray tomography have emerged as powerful techniques for imaging complex biological samples such as intact cells, organelles, macromolecular machines, and for quantifying the internal organization of biological objects in their native states in situ at resolutions ranging from a few microns to tens of nanometers with X-rays to tens of angstroms with electrons. However, compared to the mature techniques of X-ray crystallography and single-particle cryo-electron microscopy, cellular tomography is yet to reach its potential due to a severe degradation in the resolution of reconstructions because of the effects of mechanical misalignment and non-rigid sample deformation due to radiation damage, missing-wedge artifacts, low signal-noise ratio in a crowded environment, and unresolved conformational heterogeneity. To address these issues, we propose to leverage our new approach for automated joint 3D alignment and regularized reconstruction that combines advances in iterative projection methods and convex optimization to achieve better than state-of-the-art reconstruction resolution from severely misaligned data. Infusing our framework with new advances in mathematical modeling and machine learning provides a clear path to a host of new model-based and data-driven algorithms that could address current challenges and bottlenecks in the analysis of cellular tomography data. In particular we propose to (1) develop techniques for improved tilt-series alignment that account for rigid-body motion of the sample and recover the anisotropic effects of radiation-induced warping by using optical flow alignment; (2) develop a decoder that leverages the full frequency information contained in randomly oriented macromolecules in the cell volume to constrain the effects of the missing-wedge; and (3) improve the resolution of subtomograms extracted from the refined volume by developing a volume-encoder--deformation-decoder deep neural network to model conformational heterogeneity. By developing new data-driven methods that constrain the missing-wedge information and treat shape variability as a continuum of non-rigid deformations rather than discrete clusters, our algorithmic framework will provide significant improvements in the resolution and quality of reconstructions over currently existing methods for data analysis that neglect these effects. As the structural biology community is increasingly focusing on cellular tomography, there is a growing need for easy to use, automated software amenable to both experienced and novice users. (4) To this end, algorithms resulting from this proposal will be turned into GPU- enabled open-source user-friendly software to accelerate the analysis of the growing pool of imaging data. Ultimately, our algorithmic framework will be capable of yielding high-resolution structures from noisy, incomplete and complex data, thereby enhancing the predictive power of cellular tomography to answer important biological questions.