Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term “diffusional fingerprinting.” This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting’s utility as a universal paradigm for SPT diffusional analysis and prediction.

单粒子跟踪 (SPT) 是动态生物过程定量分析的关键工具，为受体定位、酶推进、细菌运动和药物纳米载体递送等广泛的系统提供了前所未有的见解。这种生物系统中固有的复杂扩散在时间上和跨系统都可能发生巨大变化，因此带来了相当大的分析挑战，目前需要对系统有先验知识。在这里，我们介绍了一种用于 SPT 数据分析、处理和分类的方法，我们将其称为“扩散指纹识别”。这种方法允许剖析作为扩散行为基础的特征并建立分子身份，而不管底层的扩散类型如何。该方法通过为每个观察到的运动轨迹隔离 17 个描述性特征并为每种类型的粒子生成所有特征的扩散图来进行操作。然后通过训练一个简单的逻辑回归模型来获得扩散粒子身份的精确分类。线性判别分析生成一个特征排序，输出扩散特征之间的主要差异，提供关键的机制见解。指纹通过对实验数据进行训练和预测来进行操作，而无需对模拟数据进行预训练。我们发现这种方法适用于广泛的模拟和实验多样化系统，例如脂肪基质上的跟踪脂肪酶、细胞中扩散的转录因子以及粘液中扩散的纳米颗粒。