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
基本信息
- 批准号:10714540
- 负责人:
- 金额:$ 50.52万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:3-DimensionalAccelerationAddressAlgorithmsBiologicalCell VolumesCellsCommunitiesComplexComputer softwareCrowdingCryoelectron MicroscopyDataData AnalysesData SetElectronsEnvironmentFrequenciesGoalsHeterogeneityHybridsImageImaging TechniquesIn SituInterventionJointsMachine LearningManualsMechanicsMethodsModelingMolecular ConformationMorphologic artifactsMotionNoiseOpticsOrganellesPublishingRadiation induced damageResolutionRoentgen RaysSamplingSeriesShapesSignal TransductionStructureStructure-Activity RelationshipTechniquesVisualizationX-Ray CrystallographyX-Ray Tomographycomplex datacomputerized toolsdeep neural networkexperienceimprovedmacromoleculemathematical modelnanometerneglectnovel strategiesopen sourceparticleradiation effectreconstructionstructural biologytomographyuser friendly software
项目摘要
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.
摘要
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,
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,
用于成像复杂生物样品的强大技术,例如完整细胞、细胞器、大分子
机器,并用于量化生物物体在其自然状态下的内部组织,
分辨率范围从几微米到几十纳米的X射线到几十埃的电子。
然而,与X射线晶体学和单粒子低温电子学的成熟技术相比,
显微镜,细胞断层扫描尚未达到其潜力,由于严重退化的分辨率,
由于机械错位和非刚性样品变形的影响,
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
构象异质性为了解决这些问题,我们建议利用我们的新方法,
联合3D对齐和正则化重建,结合了迭代投影方法的进步,
凸优化,以实现优于最先进的重建分辨率,
数据将我们的框架注入数学建模和机器学习的新进展,
一系列新的基于模型和数据驱动的算法,可以解决当前的挑战,
细胞断层扫描数据分析中的瓶颈。具体而言,我们建议(1)开发技术,
改进的倾斜系列对准,其考虑样品的刚体运动并恢复各向异性
辐射引起的扭曲的影响,通过使用光流对准;(2)开发一个解码器,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,
改进了从细化体中提取的子断层图像的分辨率
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的方法,限制缺失的楔形信息,
形状可变性作为一个连续的非刚性变形,而不是离散的集群,我们的算法
框架将提供显着改善的分辨率和重建质量比目前
现有的数据分析方法忽略了这些影响。随着结构生物学社区越来越多地
关注细胞断层扫描,对易于使用的自动化软件的需求日益增长,
有经验的和新手用户。(4)为此,从这个建议产生的算法将变成GPU-
启用了开源用户友好型软件,以加速对不断增长的成像数据库的分析。
最终,我们的算法框架将能够从嘈杂的,
不完整和复杂的数据,从而提高细胞断层扫描的预测能力,
重要的生物学问题。
项目成果
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