Supplementary Material for: AxonQuant: A Microfluidic Chamber Culture-Coupled Algorithm That Allows High-Throughput Quantification of Axonal Damage
datasetposted on 28.02.2014 by Li Y., Yang M., Huang Z., Chen X., Maloney M.T., Zhu L., Liu J., Yang Y., Du S., Jiang X.
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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. © 2014 S. Karger AG, Basel