AxonQuant: A microfluidic chamber culture-coupled algorithm that allows high-throughput quantification of axonal damage

Yang Li, Mengxue Yang, Zhuo Huang, Xiaoping Chen, Michael T. Maloney, Li Zhu, Jianghong Liu, Yanmin Yang, Sidan Du, Xingyu Jiang, Jane Y. Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)14-29
Number of pages16
JournalNeuroSignals
Volume22
Issue number1
DOIs
StatePublished - May 6 2014

Keywords

  • Automated image processing and quantification
  • Axonal continuity
  • Axonal damage
  • High-throughput screening
  • Microfluidic chamber

ASJC Scopus subject areas

  • Neurology
  • Developmental Neuroscience
  • Cellular and Molecular Neuroscience

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