Fischell Department of Bioengineering, University of Maryland, 3236 Kim Engineering Building, College Park, MD 20742, United States.
J Neurosci Methods. 2011 Aug 15;199(2):230-40. doi: 10.1016/j.jneumeth.2011.05.013. Epub 2011 May 20.
The biological and clinical relevance of axonal transport has driven the development of a variety of new approaches to its study, including the generation of fluorescence or brightfield movies of moving cargoes within axons. Kymograph analysis is a simple and effective tool used to analyze axonal transport in neurons. Typically, kymographs are built by having a user trace the path of the axon in one frame of a time-lapse movie and extracting intensity profiles from subsequent frames along that path. This method cannot accommodate movies in which translation of the axon, or changes in axonal orientation or geometry, occur. Both are frequently observed in long-term movies of neurons, both in vitro and in vivo. To solve this problem and automate the creation of kymographs from these movies, we developed a two step algorithm. The first step implemented a simple image registration algorithm that aligned axons based on identification of a reference point on the axon in each image. The second step used a Hough transformation (HT) to automatically detect the axonal contour in each frame. Intensity profiles along this contour were then used to construct a kymograph. This algorithm was able to build an accurate kymograph of mitochondrial and actin transport in dynamic cultured sensory neurons, which were not amenable to previously used analytical methods. Although developed as a tool for analyzing transport, this algorithm is easily modified to analyze movies for the directionality and speed of axonal outgrowth, another metric of interest to neuroscientists.
轴突运输的生物学和临床相关性推动了多种新方法的发展，以研究其功能，包括荧光或亮场电影记录轴突内运动货物。示踪分析是一种用于分析神经元轴突运输的简单而有效的工具。通常，示踪分析是通过用户在延时电影的一帧中追踪轴突的路径，并沿着该路径从后续帧中提取强度分布来构建的。这种方法无法适应于轴突发生平移、轴突方向或几何形状发生变化的电影，这两种情况在体外和体内的神经元长期电影中经常观察到。为了解决这个问题并使这些电影中的示踪分析自动化，我们开发了一个两步算法。第一步实现了一个简单的图像配准算法，该算法基于每个图像中轴突上参考点的识别来对齐轴突。第二步使用霍夫变换（HT）自动检测每个帧中的轴突轮廓。然后沿着该轮廓提取强度分布，以构建示踪图。该算法能够构建动态培养感觉神经元中线粒体和肌动蛋白运输的精确示踪图，而这些运输无法使用以前使用的分析方法来处理。尽管该算法是作为分析运输的工具开发的，但它很容易修改以分析电影中的轴突生长的方向性和速度，这是神经科学家感兴趣的另一个指标。
