ICFO - Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain.
Facultat de Ciències, Tecnologia I Enginyeries, Universitat de Vic - Universitat Central de Catalunya (UVic-UCC), Vic, Spain; Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC), Vic, Barcelona, Spain.
Biophys J. 2023 Nov 21;122(22):4360-4369. doi: 10.1016/j.bpj.2023.10.015. Epub 2023 Oct 17.
To characterize the mechanisms governing the diffusion of particles in biological scenarios, it is essential to accurately determine their diffusive properties. To do so, we propose a machine-learning method to characterize diffusion processes with time-dependent properties at the experimental time resolution. Our approach operates at the single-trajectory level predicting the properties of interest, such as the diffusion coefficient or the anomalous diffusion exponent, at every time step of the trajectory. In this way, changes in the diffusive properties occurring along the trajectory emerge naturally in the prediction and thus allow the characterization without any prior knowledge or assumption about the system. We first benchmark the method on synthetic trajectories simulated under several conditions. We show that our approach can successfully characterize both abrupt and continuous changes in the diffusion coefficient or the anomalous diffusion exponent. Finally, we leverage the method to analyze experiments of single-molecule diffusion of two membrane proteins in living cells: the pathogen-recognition receptor DC-SIGN and the integrin α5β1. The analysis allows us to characterize physical parameters and diffusive states with unprecedented accuracy, shedding new light on the underlying mechanisms.
为了描述生物场景中颗粒扩散的机制，准确确定其扩散性质至关重要。为此，我们提出了一种机器学习方法，能够以实验时间分辨率来描述具有时变特性的扩散过程。我们的方法在单轨迹水平上进行操作，预测轨迹的每个时间步的感兴趣的性质，例如扩散系数或反常扩散指数。通过这种方式，在轨迹中发生的扩散性质的变化在预测中自然出现，从而无需对系统进行任何先验知识或假设即可进行特征描述。我们首先在几种条件下模拟的合成轨迹上对该方法进行基准测试。我们表明，我们的方法可以成功地描述扩散系数或反常扩散指数的突然和连续变化。最后，我们利用该方法分析了两种膜蛋白在活细胞中单分子扩散的实验：病原体识别受体 DC-SIGN 和整合素 α5β1。该分析允许我们以前所未有的精度来描述物理参数和扩散状态，为潜在机制提供新的见解。
