利用深度学习推断单条轨迹的逐点扩散性质
Inferring pointwise diffusion properties of single trajectories with deep learning
AI总结：
提出一种机器学习方法，在单条轨迹层面逐点预测扩散系数和反常扩散指数，无需先验假设即可表征扩散性质的动态变化，并在合成数据与活细胞膜蛋白实验中验证了其高精度。
摘要：
为了表征生物场景中控制粒子扩散的机制，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，用于在实验时间分辨率下表征具有时间依赖性质的扩散过程。该方法在单条轨迹层面运行，在轨迹的每个时间步预测感兴趣的性质，例如扩散系数或反常扩散指数。通过这种方式，轨迹中发生的扩散性质变化在预测中自然显现，从而无需对系统有任何先验知识或假设即可进行表征。我们首先在多种条件下模拟的合成轨迹上对该方法进行了基准测试。结果表明，该方法能够成功表征扩散系数或反常扩散指数的突变和连续变化。最后，我们利用该方法分析了活细胞中两种膜蛋白的单分子扩散实验：病原体识别受体 DC-SIGN 和整合素 $\alpha5\beta1$。该分析使我们能够以前所未有的精度表征物理参数和扩散状态，为潜在机制提供了新见解。
英文摘要：
In order 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 $\alpha5\beta1$. The analysis allows us to characterize physical parameters and diffusive states with unprecedented accuracy, shedding new light on the underlying mechanisms.
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