Deciphering anomalous heterogeneous intracellular transport with neural networks
Deciphering anomalous heterogeneous intracellular transport with neural networks
复制标题
DOI：
10.7554/elife.52224
复制
发表时间：
2020-03-24
期刊：
影响因子：
7.7
通讯作者：
Waigh, Thomas A.
中科院分区：
文献类型：
--
作者：
Han, Daniel;Korabel, Nickolay;Waigh, Thomas A.
细胞内运输在时间和空间上都是不均匀的，表现出不同的非布朗行为。通过在轨迹集合上或在单个轨迹的过程中的平均方法来表征这种运动通常不能捕获这种异质性。在这里，我们开发了一种基于分数布朗运动训练的深度学习前馈神经网络，为解决细胞内运输在空间和时间上的异质行为提供了一种新颖，准确和有效的方法。与现有方法相比，神经网络需要的数据点显著减少。这使得能够对非常短的时间序列数据进行Hurst指数的鲁棒估计，从而可以直接动态分割和分析快速移动的细胞结构（例如内体和溶酶体）的实验轨迹。通过使用这种分析，分数布朗运动与随机赫斯特指数被用来解释，第一次，异常的细胞内动力学，揭示了意想不到的差异密切相关的内吞细胞器之间的行为。
Intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behavior. Characterization of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity. Here, we developed a deep learning feedforward neural network trained on fractional Brownian motion, providing a novel, accurate and efficient method for resolving heterogeneous behavior of intracellular transport in space and time. The neural network requires significantly fewer data points compared to established methods. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, fractional Brownian motion with a stochastic Hurst exponent was used to interpret, for the first time, anomalous intracellular dynamics, revealing unexpected differences in behavior between closely related endocytic organelles.