Automated image analysis for tracking cargo transport in axons.
Automated image analysis for tracking cargo transport in axons.
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DOI：
10.1002/jemt.20934
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发表时间：
2011-07
影响因子：
2.5
通讯作者：
Cui, Bianxiao
中科院分区：
文献类型：
--
作者：
Zhang, Kai;Osakada, Yasuko;Xie, Wenjun;Cui, Bianxiao
轴突中货物运动的动力学编码了关于神经元中轴突运输过程的潜在调控机制的关键信息，这是理解许多神经退行性疾病的核心问题。轴突中货物动力学的定量分析通常包括三个步骤：（1）获取延时图像序列，（2）在每个时间步定位单个货物，（3）构建动力学分析的动态轨迹。目前，后两个步骤通常在大量人为干预下进行。本文提出了一种自动图像分析方法，旨在构建具有更高的数据处理吞吐量，更好的空间分辨率和最小的人为干预的货物轨迹。该方法是基于几种算法的新应用，包括2-D kymograph建设，种子点检测，轨迹曲线跟踪，反投影提取空间信息，并使用2D高斯拟合位置细化。该方法对于低信噪比的图像（例如来自单分子实验的图像）具有足够的鲁棒性。通过跟踪背根神经节神经元中量子点和DiI荧光标记囊泡的轴突运输，实验验证了该方法。
The dynamics of cargo movement in axons encodes crucial information about the underlying regulatory mechanisms of the axonal transport process in neurons, a central problem in understanding many neurodegenerative diseases. Quantitative analysis of cargo dynamics in axons usually includes three steps: (1) acquiring time-lapse image series, (2) localizing individual cargos at each time step, and (3) constructing dynamic trajectories for kinetic analysis. Currently, the later two steps are usually carried out with substantial human intervention. This paper presents a method of automatic image analysis aiming for constructing cargo trajectories with higher data processing throughput, better spatial resolution and minimal human intervention. The method is based on novel applications of several algorithms including 2-D kymograph construction, seed points detection, trajectory curve tracing, back-projection to extract spatial information, and position refining using a 2D Gaussian fitting. This method is sufficiently robust for usage on images with low signal-to-noise ratio, such as those from single molecule experiments. The method was experimentally validated by tracking the axonal transport of quantum dot and DiI fluorophore-labeled vesicles in dorsal root ganglia neurons.
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影响因子：
3.7
作者：
Chetverikov, D;Verestóy, J
通讯作者：
Verestóy, J
DOI：
10.1073/pnas.0808084105
发表时间：
2008-10-14
影响因子：
11.1
作者：
Ittner, Lars M.;Fath, Thomas;Goetz, Juergen
通讯作者：
Goetz, Juergen
DOI：
10.1073/pnas.0706192104
发表时间：
2007-08-21
影响因子：
11.1
作者：
Cui, Bianxiao;Wu, Chengbiao;Chu, Steven
通讯作者：
Chu, Steven
影响因子：
10.6
作者：
Smal, Ihor;Grigoriev, Ilya;Meijering, Erik
通讯作者：
Meijering, Erik
影响因子：
3.3
作者：
Lochner, JE;Kingma, M;Scalettar, BA
通讯作者：
Scalettar, BA