Department of Biological Sciences, Eastern Kentucky University, Richmond, KY 40475.
Department of Psychology, Birmingham-Southern College, Birmingham, AL 35254.
eNeuro. 2022 Mar 18;9(2). doi: 10.1523/ENEURO.0327-21.2022. Print 2022 Mar-Apr.
Neurite degeneration is associated with early stages of neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease (PD), and amyotrophic lateral sclerosis. One method that is commonly used to analyze neurite degeneration involves calculation of a Degeneration Index (DI) following utilization of the Analyze Particles tool of ImageJ to detect neurite fragments in micrographs of cultured cells. However, DI analyses are prone to several types of measurement error, can be time consuming to perform, and are limited in application. Here, we describe an improved method for performing DI analyses. Accuracy of measurements was enhanced through modification of selection criteria for detecting neurite fragments, removal of image artifacts and non-neurite materials from images, and optimization of image contrast. Such enhancements were implemented into an ImageJ macro that enables rapid and fully automated DI analysis of multiple images. The macro features operations for automated removal of cell bodies from micrographs, thus expanding the application of DI analyses to use in experiments involving dissociated cultures. We present experimental findings supporting that, compared with the conventional method, the enhanced analysis method yields measurements with increased accuracy and requires significantly less time to perform. Furthermore, we demonstrate the utility of the method to investigate neurite degeneration in a cell culture model of PD by conducting an experiment revealing the effects of c-Jun N-terminal kinase (JNK) on neurite degeneration induced by oxidative stress in human mesencephalic cells. This improved analysis method may be used to gain novel insight into factors underlying neurite degeneration and the progression of neurodegenerative disorders.
神经突退化与神经退行性疾病的早期阶段有关，如阿尔茨海默病、帕金森病 (PD) 和肌萎缩性侧索硬化症。一种常用于分析神经突退化的方法是在培养细胞的显微照片中利用 ImageJ 的 Analyze Particles 工具检测神经突碎片后计算退化指数 (DI)。然而，DI 分析容易出现多种类型的测量误差，执行起来耗时且应用受限。在这里，我们描述了一种改进的 DI 分析方法。通过修改检测神经突碎片的选择标准、从图像中去除图像伪影和非神经突材料以及优化图像对比度，提高了测量的准确性。这些改进被纳入到一个 ImageJ 宏中，该宏能够快速且全自动地对多个图像进行 DI 分析。该宏具有从显微照片中自动去除细胞体的操作，从而扩展了 DI 分析在涉及分离培养的实验中的应用。我们提供了实验结果，支持与传统方法相比，增强的分析方法具有更高的测量准确性，并且执行时间大大缩短。此外，我们通过进行一项实验来证明该方法在 PD 细胞培养模型中研究神经突退化的实用性，该实验揭示了 c-Jun N-末端激酶 (JNK) 对人中脑细胞中氧化应激诱导的神经突退化的影响。这种改进的分析方法可用于深入了解神经突退化和神经退行性疾病进展的潜在因素。
