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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：
10524774
负责人：
Steve Presse
金额：
$ 29.96万
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 2024-11-30
项目状态：
已结题

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.

项目概要： 2014 年诺贝尔化学奖因荧光标记、仪器和分析方面的进步而被授予在过去的十年中，这些方法共同将粒子位置解析到了约 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。随后使用了超分辨率对与神经退行性疾病相关的 β-淀粉样纤维进行成像并直接观察衍射限制蛋白与癌症表型相关的聚类。虽然超分辨率定位揭示了与健康直接相关的静态细胞结构，但它确实不能直接洞察疾病动态。直接观察单分子体内动力学级别需要多粒子超分辨率粒子跟踪。超分辨跟踪比跟踪更困难超分辨率定位，因为对于收集的相同数量的光子来说，跟踪需要移动设备粒子被定位在多个图像帧上。此外，多粒子超分辨跟踪重新要求在完成这一切的同时考虑有限范围内不可避免的重叠粒子轨迹细胞体积的尺寸受到一些衍射限制体积的影响。因此，迄今为止，还没有系统的方法可以准确地在大肠杆菌细胞质大小的体积内同时追踪数百万种蛋白质中的不止一种蛋白质。因此，总体目标是：提供第一个有原则的多粒子超分辨轨迹通过利用贝叶斯非参数（BNP）的新颖工具来计算算法，这些工具已经深入人心过去十年影响了数据科学。 BNP 可以学习每帧和粒子中的粒子数以一种可计算处理的方式跨所有帧的轨迹，直接由数据（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 – 例如大肠杆菌内膜上丝氨酸化学感受器、Tsr、复合物的组装/拆卸 – 以及涉及突然动态变化的问题 – 例如束缚/非束缚状态之间的转变 RNA聚合酶——自然地在提出的原则性跟踪框架中处理。提出了两个具体目标。具体目标 I – 开发第一个、完全集成且无监督的、多个衍射极限粒子的超分辨跟踪算法，假设粒子以单一（未知）扩散系数进行扩散。具体目标 II – 针对案例重复具体目标 1 其中粒子演化的动力学模型是未知的，甚至会随时间变化（即，取消了由简单扩散控制动力学的限制）。在每个目标中，我们将：确定通过调整（非参数）伯努利过程，每帧中的粒子数；调整观察模型考虑对快速移动粒子很重要的复杂标签光物理和混叠伪影；处理颗粒限制小细菌细胞中的颗粒扩散，同时通过适应狄利克雷学习动力学模型流程；包含详细的相机噪声模型。

项目成果

期刊论文数量（26）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Direct photon-by-photon analysis of time-resolved pulsed excitation data using Bayesian nonparametrics

使用贝叶斯非参数对时间分辨脉冲激发数据进行直接逐光子分析

DOI：
10.1016/j.xcrp.2020.100234
发表时间：
2020
期刊：
Cell Reports Physical Science
影响因子：
8.9
作者：
Meysam Tavakoli;Sina Jazani;Ioannis Sgouralis;Wooseok Heo;Kunihiko Ishii;Tahei Tahara;and Steve Presse
通讯作者：
and Steve Presse

Single-Molecule Reaction-Diffusion.

单分子反应扩散。

DOI：
10.1101/2023.09.05.556378
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Xu徐伟青,LanceWQ;Jazani,Sina;Kilic,Zeliha;Pressé,Steve
通讯作者：
Pressé,Steve

BNP-Track: A framework for superresolved tracking.

BNP-Track：超分辨率跟踪框架。

DOI：
10.1101/2023.04.03.535459
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Sgouralis,Ioannis;Xu徐伟青,LanceWQ;Jalihal,AmeyaP;Walter,NilsG;Pressé,Steve
通讯作者：
Pressé,Steve

Monte Carlo samplers for efficient network inference.

DOI：
10.1371/journal.pcbi.1011256
发表时间：
2023-07
期刊：
PLoS computational biology
影响因子：
4.3
作者：
通讯作者：

Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics.

使用贝叶斯非参数分析一次一个光子进行单焦点共焦数据分析。

DOI：
10.1103/physrevx.10.011021
发表时间：
2020
期刊：
Physical review. X
影响因子：
0
作者：
Tavakoli,Meysam;Jazani,Sina;Sgouralis,Ioannis;Shafraz,OmerM;Sivasankar,Sanjeevi;Donaphon,Bryan;Levitus,Marcia;Pressé,Steve
通讯作者：
Pressé,Steve