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

基于混合模型和数据驱动的高分辨率断层成像框架

• 批准号: 10714540
• 负责人: KANUPRIYA PANDE
• 金额: $ 50.52万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714540
• 关键词: 3-Dimensional; Acceleration; Address; Algorithms; Biological; Cell Volumes; Cells; Communities; Complex; Computer software; Crowding; Cryoelectron Microscopy; Data; Data Analyses; Data Set; Electrons; Environment; Frequencies; Goals; Heterogeneity; Hybrids; Image; Imaging Techniques; In Situ; Intervention; Joints; Machine Learning; Manuals; Mechanics; Methods; Modeling; Molecular Conformation; Morphologic artifacts; Motion; Noise; Optics; Organelles; Publishing; Radiation induced damage; Resolution; Roentgen Rays; Sampling; Series; Shapes; Signal Transduction; Structure; Structure-Activity Relationship; Techniques; Visualization; X-Ray Crystallography; X-Ray Tomography; complex data; computerized tools; deep neural network; experience; improved; macromolecule; mathematical model; nanometer; neglect; novel strategies; open source; particle; radiation effect; reconstruction; structural biology; tomography; user friendly software

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.

抽象的 结构生物学的最终目标是可视化生物分子在其天然环境中的作用，并 建立它们的结构-功能关系。细胞冷冻电子和 X 射线断层扫描已成为 用于对复杂生物样品（例如完整细胞、细胞器、大分子）进行成像的强大技术 机器，并用于量化生物物体在其天然状态下的内部组织 分辨率范围从 X 射线的几微米到几十纳米到电子的几十埃。 然而，与X射线晶体学和单粒子冷冻电子技术等成熟技术相比， 由于细胞断层扫描的分辨率严重下降，细胞断层扫描尚未发挥其潜力。 由于机械失准和非刚性样本变形的影响而进行的重建 辐射损伤、楔形缺失伪影、拥挤环境中的低信噪比以及尚未解决的问题 构象异质性。为了解决这些问题，我们建议利用我们的自动化新方法 联合 3D 对齐和正则化重建，结合了迭代投影方法的进步和 凸优化可在严重失准的情况下实现优于最先进的重建分辨率 数据。将数学建模和机器学习的新进展融入我们的框架提供了 通往一系列基于模型和数据驱动的新算法的清晰道路，这些算法可以解决当前的挑战和 细胞断层扫描数据分析的瓶颈。我们特别建议（1）开发技术 改进的倾斜系列对准，可解释样品的刚体运动并恢复各向异性 使用光流对准来消除辐射引起的扭曲的影响； (2) 开发一个解码器 细胞体积中随机定向的大分子中包含的全频率信息，以约束 楔形缺失的影响； (3) 提高从细化体积中提取的子断层图的分辨率 通过开发体积编码器-变形解码器深度神经网络来建模构象 异质性。通过开发新的数据驱动方法来限制缺失的楔形信息并处理 形状变异性是非刚性变形的连续体而不是离散的簇，我们的算法 与目前相比，该框架将显着提高重建的分辨率和质量 现有的数据分析方法忽略了这些影响。随着结构生物学界越来越 专注于细胞断层扫描，对易于使用的自动化软件的需求不断增长 有经验的和新手用户。 (4) 为此，该提案产生的算法将转化为 GPU- 启用开源用户友好的软件来加速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、 不完整且复杂的数据，从而增强细胞断层扫描的预测能力来回答 重要的生物学问题。