Automatic detection of diffusion modes within biological membranes using back-propagation neural network.
Automatic detection of diffusion modes within biological membranes using back-propagation neural network.
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DOI：
10.1186/s12859-016-1064-z
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发表时间：
2016-05-04
影响因子：
3
通讯作者：
Milhiet PE
中科院分区：
文献类型：
--
作者：
Dosset P;Rassam P;Fernandez L;Espenel C;Rubinstein E;Margeat E;Milhiet PE
单粒子跟踪(SPT)是目前最流行的检测蛋白质在质膜内扩散的时空动力学的技术之一。事实上，真核细胞的膜成分是非常动态的分子，可以根据不同的运动方式扩散。轨迹通常是逐帧重建的，动力学特性通常使用均方位移(MSD)分析来评估。然而，为了在跟踪实验中获得具有统计学意义的结果，需要对大量的轨迹进行分析，并且仍然需要促进这种分析的新方法。在本研究中，我们提出了一种基于反向传播神经网络(BPNN)和滑动窗口MSD分析的新算法。用较短的合成轨迹对神经网络进行了训练和交叉验证。对模拟数据和实验数据的实验结果表明，该算法能够准确区分生物膜内蛋白质扩散的三种主要扩散模式：布朗扩散、受限扩散和定向扩散。它不需要轨迹内观察到的粒子位移的最小数量来推断多个运动状态的存在。滑动窗口的大小足够小，足以测量局部行为，并检测短至20帧的片段在不同扩散模式之间的切换。它还提供了这些轨迹的每一段的定量信息。除了能够检测3种扩散模式之间的切换外，该算法还能够在很短的计算时间内同时分析数百条轨迹。该算法在功能强大、使用方便的软件中实现，为准确分析膜组件的动态行为提供了一种新的概念性和通用性的工具。本文的在线版本(doi：10.1186/s12859-0161064-z)包含补充材料，授权用户可以使用。
Single particle tracking (SPT) is nowadays one of the most popular technique to probe spatio-temporal dynamics of proteins diffusing within the plasma membrane. Indeed membrane components of eukaryotic cells are very dynamic molecules and can diffuse according to different motion modes. Trajectories are often reconstructed frame-by-frame and dynamic properties often evaluated using mean square displacement (MSD) analysis. However, to get statistically significant results in tracking experiments, analysis of a large number of trajectories is required and new methods facilitating this analysis are still needed. In this study we developed a new algorithm based on back-propagation neural network (BPNN) and MSD analysis using a sliding window. The neural network was trained and cross validated with short synthetic trajectories. For simulated and experimental data, the algorithm was shown to accurately discriminate between Brownian, confined and directed diffusion modes within one trajectory, the 3 main of diffusion encountered for proteins diffusing within biological membranes. It does not require a minimum number of observed particle displacements within the trajectory to infer the presence of multiple motion states. The size of the sliding window was small enough to measure local behavior and to detect switches between different diffusion modes for segments as short as 20 frames. It also provides quantitative information from each segment of these trajectories. Besides its ability to detect switches between 3 modes of diffusion, this algorithm is able to analyze simultaneously hundreds of trajectories with a short computational time. This new algorithm, implemented in powerful and handy software, provides a new conceptual and versatile tool, to accurately analyze the dynamic behavior of membrane components. The online version of this article (doi:10.1186/s12859-016-1064-z) contains supplementary material, which is available to authorized users.