Theoretical Models of Single Molecule Dynamics from Minimal Photon Numbers

最小光子数的单分子动力学理论模型

• 批准号: 10483190
• 负责人: Steve Presse
• 金额: $ 29.02万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10483190
• 关键词: 5 year old; Accounting; Benchmarking; Biology; Blinking; Code; Complex; Confocal Microscopy; Data; Data Collection; Data Reporting; Data Science; Data Set; Development; Diffuse; Diffusion; Energy Transfer; Event; Fluorescence; Gene Expression Regulation; Genetic Transcription; Grain; Grant; Head; Image; Kinetics; Knowledge; Label; Learning; Licensing; Mathematics; Measurement; Methods; Modeling; Molecular; Monitor; Morphologic artifacts; Natural Sciences; Noise; Output; Paper; Photons; Physics; Process; Proteins; Psyche structure; Publications; Sampling; Scanning; Series; Shapes; Spectrum Analysis; Technology; Theoretical model; Time; data acquisition; data exchange; data tools; experimental study; flexibility; flu; imaging approach; in vitro testing; insight; kinetic model; novel; physical science; simulation; single molecule; statistics; temporal measurement; tool

Project Summary Fundamental intracellular processes of immediate relevance to biomedicine–such as gene regulation and transcription–often involve large clusters of proteins dynamically assembling and disassembling within small diffraction-limited volumes at timescales approaching imaging data acquisition. Despite impressive μs-ms data collection timescales achieved by many SM fluorescence methods, single molecule (SM) kinetic parameters are often instead determined from large quantities of data (millions of photons) collected and averaged over long timescales. This compromises the temporal resolution of the data that theoretically encodes information on events that may unfold and be resolved within ms. Drawing insight on complex processes resolved within ms presents a profound analysis challenge. Funda- mentally, this is because highly stochastic SMs are indirectly monitored by the equally stochastic measure- ment output to which SMs are inextricably tied: photons. Our overall objective is therefore to develop a framework to determine dynamical models–relevant downstream to complex intra-cellular processes– resolved at the SM level from very limited data (i.e., time traces tens of ms or thousand of photons). For this FTRD grant, our focus is on benchmarking our framework on simple in vitro test data sets. To resolve these fast dynamics, we will rely on cutting-edge tools from Data Science and Statistics termed Bayesian nonparametrics (BNPs) largely unknown to the Natural Sciences. Here we will adapt BNP tools– some less than five years old and proposed here for the first time for Natural Science applications–to provide a fundamentally new treatment of data derived from confocal setups (Specific Aim I) and single molecule flu- orescence resonance energy transfer termed smFRET (Specific Aim II)–both workhorses across Biology. As BNPs are highly flexible, we develop strategies to rigorously constrain them with knowledge of the measure- ment process, e.g., the shape of the point spread function. For both Specific Aims, we will develop fully-integrated and unsupervised methods to resolve SM dynamical models from ms worth of data by exploiting BNPs. In particular for Specific Aim I, we will do so starting from single photon arrivals derived from confocal experiments. We will determine diffusive species numbers (relevant in dealing with multimeric mixtures) as well as the diffusion coefficients for each species. By resolving diffusion coefficients with the same precision as FCS from just thousands (as opposed to millions) of photons, we could collect far shorter traces thereby dramatically minimizing sample photo-damage. Alternatively, we could use long traces to resolve previously indeterminable quantities, e.g., diffusion coefficient differences in multimeric mixtures. For Specific Aim II we will determine quantities normally derived from current smFRET analysis but now accounting for spectral cross-talk, label blinking and determine the number of molecular states. Accounting for such photo-physics deeply influences our ultimate interpretation of smFRET data.

项目摘要 与生物医学（例如基因调节）的基本细胞内过程 转录 - 通常涉及大量的蛋白质簇，动态组装并拆卸小 在接近成像数据采集的时标的衍射量量。尽管令人印象深刻的μs-ms数据 通过许多SM荧光方法，单分子（SM）动力学参数实现的收集时间尺度 通常是根据收集和平均的大量数据（数百万照片）确定的 长时间尺度。这损害了理论编码信息的数据的暂时分辨率 关于可能展开并在MS中解决的事件。 在MS中解决的复杂过程的了解洞察力提出了深刻的分析挑战。 funda- 在心理上，这是因为高度随机的SMS通过同样随机测量 - SMS与之息息相关的输出：照片。因此，我们的总体目标是发展 确定动态模型的框架 - 范围的下游到复杂的细胞内过程 - 从非常有限的数据（即，时间跟踪数十MS或数千张照片）以SM级别解决。 对于这项FTRD赠款，我们的重点是将我们的框架基准在简单的体外测试数据集上。 为了解决这些快速的动态，我们将依靠所谓的数据科学和统计数据的尖端工具 贝叶斯非参数（BNPS）在很大程度上是自然科学的未知数。在这里，我们将调整BNP工具 - 自然科学应用的第一次在这里提出了不到五年的历史，以提供 从根本上开始对共聚焦设置（特定目的I）和单分子旋转的数据进行的新处理 Orescence共振能量转移称为SMFRET（特定目标II） - 跨生物学的两个主力。作为 BNP具有很高的灵活性，我们制定了严格地限制它们的策略，以了解测量 指数过程，例如，点扩展函数的形状。 对于这两个特定目标，我们将开发完全集成和无监督的方法来解决SM动力学 通过利用BNP来获得MS数据的模型。特别是针对特定目标，我们将开始 来自共聚焦实验的单个光子到达。我们将确定扩散物种数量 （与处理多种药物混合物有关）以及每个物种的扩散系数。通过解决 扩散系数的精度与仅数千张照片（相反）的FC相同的精度， 我们可以收集较短的痕迹，从而极大地最大程度地减少样本损坏。另外，我们 可以使用较长的痕迹来解决以前无法确定的数量，例如，差异的系数差异 多药混合物。对于特定目标II，我们将确定通常从当前SMFRET得出的数量 分析，但现在考虑光谱串扰，标签闪烁并确定分子数量 国家。考虑到这种光物理学对我们对SMFRET数据的最终解释深深影响。

项目成果

On the statistical foundation of a recent single molecule FRET benchmark.

基于最近单分子 FRET 基准的统计基础。

• DOI: 10.1038/s41467-024-47733-3
• 发表时间: 2024
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Saurabh,Ayush;Xu,LanceWQ;Pressé,Steve
• 通讯作者: Pressé,Steve