A Bayesian nonparametric approach to superresolved tracking of multiple molecules inside living cells

贝叶斯非参数方法对活细胞内多个分子进行超分辨跟踪

基本信息

批准号：
10294246
负责人：
Steve Presse
金额：
$ 30.06万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-02-01 至 2023-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10294246
关键词：
Accounting Algorithms Amyloid beta-Protein Amyloid fibers Award Binding Budgets Cells Cellular Structures Chemistry Chemoreceptors Code Complex Cytoplasm DNA-Directed RNA Polymerase Data Data Science Diffuse Diffusion Disease Escherichia coli Goals Hand Health Image Instruction Label Laws Learning Lifting Light Link Malignant Neoplasms Manuals Mathematics Medical Membrane Methods Modeling Molecular Structure Morphologic artifacts Neurodegenerative Disorders Nobel Prize Noise Optics Outcome Phenotype Photons Positioning Attribute Process Proteins Publications Resolution Serine Structure Technology Time cost imaging capabilities in vivo innovation insight instrumentation novel particle physical science single molecule tool web site

Accounting Algorithms Amyloid beta-Protein Amyloid fibers Award Binding Budgets Cells Cellular Structures Chemistry Chemoreceptors Code Complex Cytoplasm DNA-Directed RNA Polymerase Data Data Science Diffuse Diffusion Disease Escherichia coli Goals Hand Health Image Instruction Label Laws Learning Lifting Light Link Malignant Neoplasms Manuals Mathematics Medical Membrane Methods Modeling Molecular Structure Morphologic artifacts Neurodegenerative Disorders Nobel Prize Noise Optics Outcome Phenotype Photons Positioning Attribute Process Proteins Publications Resolution Serine Structure Technology Time cost imaging capabilities in vivo innovation insight instrumentation novel particle physical science single molecule tool web site

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项目摘要

Project Summary: The 2014 Chemistry Nobel Prize was awarded for advances in ﬂuorescent labeling, instrumentation and anal- ysis methods which together, over the last decade, have resolved particle positions to within ≈20-30 nm. That is, below the diffraction limit of light used to excite them. Superresolution has subsequently been used to image β-amyloid ﬁbers tied to neurodegenerative disorders and directly observe diffraction limited protein clustering linked to cancer phenotypes. While superresolved localization reveals static cellular structures of immediate relevance to health, it does not provide direct insight into disease dynamics. Directly observing in vivo dynamics at the single molecule level demands multi-particle superresolved particle tracking. Superresolved tracking is more difﬁcult than superresolved localization because – for the same number of photons collected – tracking requires mobile particles to be localized over multiple image frames. Furthermore, multi-particle superresolved tracking re- quires that this all be done while accounting for unavoidable overlapping particle trajectories within a conﬁned cellular volume a few diffraction limited volumes in size. Thus, to date, there is no systematic way to accurately track more than one protein, of the millions of proteins, inside a volume the size of E. coli’s cytoplasm at once. The overarching goal is therefore: To provide the ﬁrst principled multi-particle superresolved track- ing algorithm by exploiting the novel tools of Bayesian nonparametrics (BNPs) that have already deeply impacted Data Science over the last decade. BNPs can learn particle numbers in each frame and particle trajectories across all frames in a computationally tractable manner in a way that is directly informed by the data (photons collected per pixel). The tracking method developed will be applied to multi-particle problems – such as the assembly/disassembly of serine chemoreceptor, Tsr, complexes on E. coli’s inner membrane – and problems involving abrupt dynamical changes – such as transitions between bound/unbound states of RNA polymerases – naturally dealt with in the principled tracking framework proposed. Two Speciﬁc Aims are proposed. Speciﬁc Aim I – Develop the very ﬁrst, fully-integrated and unsupervised, superresolved tracking algorithm for multiple diffraction-limited particles under the assumption that particles diffuse with a single (unknown) diffusion coefﬁcient. Speciﬁc Aim II – Repeat Speciﬁc Aim 1 for the case where dynamical models according to which particles evolve are unknown or even changing in time (that is, the restriction that dynamics be governed by simple diffusion is lifted). Within each Aim, we will: determine particle numbers in each frame by adapting (nonparametric) Bernoulli processes; adapt observation models to account for complex label photophysics and aliasing artifacts important for fast-moving particle; treat particle conﬁnement for particle diffusion in small bacterial cells while learning dynamical models by adapting Dirichlet processes; incorporate detailed camera noise models.

项目摘要： 2014年化学诺贝尔奖因进步的富有效果标签，仪器和肛门奖而获得在过去的十年中，YSIS方法共同将粒子位置解析为≈20-30nm之内。也就是说，低于用于激发它们的光的衍射极限。后来使用了超分辨率成像与神经退行性疾病有关的β-淀粉样蛋白纤维，并直接观察到衍射有限的蛋白与癌症表型相关的聚类。叠加的定位揭示了与健康直接相关的静态细胞结构，但它确实如此不能直接了解疾病动态。直接观察单分子的体内动力学水平需要多粒子叠加粒子跟踪。叠加的跟踪比叠加的本地化是因为 - 对于收集的相同数量的照片 - 跟踪需要移动粒子要在多个图像帧上定位。此外，多粒子叠加的跟踪重复要求所有这些都在考虑不可避免的重叠粒子轨迹时完成所有这些细胞体积几个衍射有限的体积。迄今为止，没有系统的方法可以准确跟踪多个蛋白质的数百万蛋白质，在大肠杆菌细胞质大小的体积内。因此，总体目标是：提供第一个主要的多粒子叠加轨道 - ING算法通过利用已经深入的贝叶斯非参数（BNP）的新颖工具在过去十年中影响了数据科学。 BNP可以在每个框架和粒子中学习粒子数以计算方式的方式以一种直接告知所有框架的轨迹数据（每个像素收集的光子）。开发的跟踪方法将应用于多粒子问题 - 例如丝氨酸化学感受器的组装/拆卸TSR，大肠杆菌内膜上的复合物 - 以及涉及突然动态变化的问题 - 例如在绑定/未结合状态之间的过渡 RNA聚合酶 - 在提出的主要跟踪框架中自然处理。提出了两个特定目标。特定目标I - 开发非常第一个，完全集成且无监督的，在颗粒的假设下，用于多个衍射限制颗粒的叠加跟踪算法用单个（未知）扩散系数扩散。特定目标II - 重复该案件的特定目标1 如果粒子进化的动态模型未知甚至在时间上变化（也就是说，解除了由简单扩散支配的动力学的限制）。在每个目标中，我们将：确定通过适应（非参数）Bernoulli过程中的每个帧中的粒子数；适应观察模型考虑复杂的标签摄影和混叠伪像，对快速移动粒子很重要；处理粒子通过调整Dirichlet来学习动态模型的粒子扩散的结果过程；合并详细的相机噪声模型。