Li Yang, Yang Mengxue, Huang Zhuo, Chen Xiaoping, Maloney Michael T, Zhu Li, Liu Jianghong, Yang Yanmin, Du Sidan, Jiang Xingyu, Wu Jane Y
School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
Neurosignals. 2014;22(1):14-29. doi: 10.1159/000358092. Epub 2014 Feb 28.
Published methods for imaging and quantitatively analyzing morphological changes in neuronal axons have serious limitations because of their small sample sizes, and their time-consuming and nonobjective nature. Here we present an improved microfluidic chamber design suitable for fast and high-throughput imaging of neuronal axons. We developed the AxonQuant algorithm, which is suitable for automatic processing of axonal imaging data. This microfluidic chamber-coupled algorithm allows calculation of an 'axonal continuity index' that quantitatively measures axonal health status in a manner independent of neuronal or axonal density. This method allows quantitative analysis of axonal morphology in an automatic and nonbiased manner. Our method will facilitate large-scale high-throughput screening for genes or therapeutic compounds for neurodegenerative diseases involving axonal damage. When combined with imaging technologies utilizing different gene markers, this method will provide new insights into the mechanistic basis for axon degeneration. Our microfluidic chamber culture-coupled AxonQuant algorithm will be widely useful for studying axonal biology and neurodegenerative disorders.
已发表的用于成像和定量分析神经元轴突形态变化的方法存在严重局限性，因为它们的样本量小，且具有耗时和非客观的性质。在此，我们展示了一种改进的微流控腔室设计，适用于对神经元轴突进行快速且高通量的成像。我们开发了AxonQuant算法，该算法适用于对轴突成像数据进行自动处理。这种与微流控腔室相结合的算法能够计算出一个“轴突连续性指数”，该指数以独立于神经元或轴突密度的方式定量测量轴突的健康状态。此方法能够以自动且无偏差的方式对轴突形态进行定量分析。我们的方法将有助于对涉及轴突损伤的神经退行性疾病的基因或治疗化合物进行大规模高通量筛选。当与利用不同基因标记的成像技术相结合时，该方法将为轴突退化的机制基础提供新的见解。我们的微流控腔室培养结合AxonQuant算法对于研究轴突生物学和神经退行性疾病将具有广泛的用途。
