Department of Biophysics, University of Michigan, Ann Arbor, MI 48104 USA.
Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA.
Methods. 2021 Sep;193:16-26. doi: 10.1016/j.ymeth.2020.03.008. Epub 2020 Apr 2.
Single-molecule fluorescence microscopy probes nanoscale, subcellular biology in real time. Existing methods for analyzing single-particle tracking data provide dynamical information, but can suffer from supervisory biases and high uncertainties. Here, we develop a method for the case of multiple interconverting species undergoing free diffusion and introduce a new approach to analyzing single-molecule trajectories: the Single-Molecule Analysis by Unsupervised Gibbs sampling (SMAUG) algorithm, which uses nonparametric Bayesian statistics to uncover the whole range of information contained within a single-particle trajectory dataset. Even in complex systems where multiple biological states lead to a number of observed mobility states, SMAUG provides the number of mobility states, the average diffusion coefficient of single molecules in that state, the fraction of single molecules in that state, the localization noise, and the probability of transitioning between two different states. In this paper, we provide the theoretical background for the SMAUG analysis and then we validate the method using realistic simulations of single-particle trajectory datasets as well as experiments on a controlled in vitro system. Finally, we demonstrate SMAUG on real experimental systems in both prokaryotes and eukaryotes to measure the motions of the regulatory protein TcpP in Vibrio cholerae and the dynamics of the B-cell receptor antigen response pathway in lymphocytes. Overall, SMAUG provides a mathematically rigorous approach to measuring the real-time dynamics of molecular interactions in living cells.
单分子荧光显微镜实时探测纳米级、亚细胞生物学。现有的分析单粒子追踪数据的方法提供了动力学信息，但可能存在监督偏见和高度不确定性。在这里，我们针对多种相互转化的物种在自由扩散过程中的情况开发了一种方法，并引入了一种新的分析单分子轨迹的方法：无监督 Gibbs 采样的单分子分析（SMAUG）算法，该算法使用非参数贝叶斯统计来揭示单粒子轨迹数据集中包含的所有信息。即使在复杂的系统中，多个生物状态导致多个观察到的流动性状态，SMAUG 也可以提供流动性状态的数量、该状态中单分子的平均扩散系数、该状态中单分子的分数、定位噪声以及两个不同状态之间的转换概率。在本文中，我们提供了 SMAUG 分析的理论背景，然后使用单粒子轨迹数据集的真实模拟以及在体外控制实验系统上的实验对该方法进行了验证。最后，我们在原核生物和真核生物的真实实验系统上展示了 SMAUG，以测量霍乱弧菌中调节蛋白 TcpP 的运动和淋巴细胞中 B 细胞受体抗原反应途径的动力学。总的来说，SMAUG 为测量活细胞中分子相互作用的实时动态提供了一种数学严谨的方法。
