NEST Laboratory, Scuola Normale Superiore, Piazza San Silvestro 12, I-56127 Pisa, Italy.
NEST Laboratory, Istituto Nanoscienze-CNR, Piazza San Silvestro 12, I-56127 Pisa, Italy.
Int J Mol Sci. 2024 Aug 8;25(16):8660. doi: 10.3390/ijms25168660.
Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field-trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.
单粒子追踪是一种强大的技术，可以用来研究分子或粒子的运动。在这里，我们回顾了分析重构轨迹的方法，这是破译驱动运动的潜在机制的基本步骤。首先，我们回顾了基于均方根位移（MSD）的传统分析方法，强调了可能影响结果准确性的一些经常被忽视的因素。然后，我们报告了利用除位移以外的参数分布的方法，例如角度、速度、到达目标的时间和概率，讨论了它们如何更敏感地描述在 MSD 分析中被掩盖的异质性和瞬态行为。隐马尔可夫模型也用于此目的，这些模型允许识别不同的状态、它们的种群和转换动力学。最后，我们讨论了基于机器学习的轨迹分析这一快速发展的领域。从随机森林到深度学习，各种方法都被用于分类轨迹运动，这些运动可以通过运动模型或通过无模型的轨迹特征集来识别，这些特征集可以是预先定义的，也可以由算法自动识别。我们还回顾了一些分析方法的免费软件。我们强调，基于不同方法的组合，包括经典统计学和机器学习的方法，可能是获得最具信息量和最准确结果的方法。
