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

项目总结： 2014年诺贝尔化学奖被授予在fl荧光标记、仪器和肛门- 在过去的十年中，这些方法一起已经将粒子的位置解析到≈20-30 nm范围内。 也就是说，低于用于激发它们的光的衍射极限。超分辨率随后被使用 成像与神经退行性疾病有关的β-淀粉样蛋白fi并直接观察衍射限制蛋白 聚集性与癌症表型有关。 虽然超分辨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 不能直接洞察疾病动态。在单分子上直接观察体内动力学 Level需要多粒子超分辨粒子跟踪。超分辨跟踪比fi邪教更不同 超分辨定位，因为对于收集的相同数量的光子，跟踪需要移动 要在多个图像帧上定位的粒子。此外，多粒子超分辨跟踪也是一种新的跟踪方法。 要求在考虑connfiNed内不可避免地重叠的粒子轨迹的同时完成所有这些操作 细胞体积大小有限的几个衍射体。因此，到目前为止，还没有系统的方法来准确地 在数百万种蛋白质中，一次追踪在大肠杆菌细胞质大小的体积内的不止一种蛋白质。 因此，总体目标是：提供fi第一个原则性多粒子超分辨径迹- 利用已经深入研究的贝叶斯非参数(BNPS)的新工具实现ING算法 在过去的十年里对数据科学产生了影响。BNPS可以学习每帧中的粒子数和粒子 以一种计算上易于处理的方式跨所有帧的轨迹，该方式由 数据(每像素收集的光子数)。所开发的跟踪方法将应用于多粒子问题 -例如在大肠杆菌内膜上组装/拆卸丝氨酸化学受体、TSR、复合体 -以及涉及突然动态变化的问题--例如 RNA聚合酶--在提出的原则性跟踪框架中自然处理。 提出了两个具体的fic目标。SPECIfic Aim i-开发完全集成且无监控的fiRST， 多个绕射受限粒子的超分辨跟踪算法 弥漫性与单一(未知)扩散系数fi有关。规格fic目标II-针对案例重复规格fic目标1 其中根据哪些粒子演化的动力学模型是未知的，甚至是随时间变化的(即， 动力学受简单扩散支配的限制被取消了)。在每个目标中，我们将：确定 通过适应(非参数)Bernoulli过程来调整每帧中的粒子数量；调整观测模型以 考虑到复杂的标签光物理和混叠伪影对快速移动的粒子很重要；处理粒子 用Dirichlet学习动力学模型时细菌小细胞内颗粒扩散的fi方法 流程；纳入详细的摄像机噪声模型。

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.