Semi-automated quantification of axonal densities in labeled CNS tissue

被引:67
作者
Grider, Michael H.
Chen, Qin
Shine, H. David
机构
[1] Baylor Coll Med, Dept Neurosci, Houston, TX 77030 USA
[2] Baylor Coll Med, Ctr Cell & Gene Therapy, Houston, TX 77030 USA
[3] Baylor Coll Med, Dept Neurosurg, Houston, TX 77030 USA
[4] Baylor Coll Med, Dept Mol & Cellular Biol, Houston, TX 77030 USA
基金
美国国家卫生研究院;
关键词
axonal growth; axon measurement; axon detection; image analysis; semi-automated; quantification;
D O I
10.1016/j.jneumeth.2005.12.021
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Current techniques used to quantify axons often rely upon manual quantification or potentially expensive commercially available programs for automated quantification. We describe a computerized method for the detection and quantification of axons in the rat CNS using readily available free software. Feature J, a java-based plug-in to the imaging software NIH Image J, faithfully detects linear structures such as axons in confocal or bright-field images using a Hessian-based algorithm. We validated the method by comparing values obtained by manual and automated analyses of axons induced to grow in response to neurotrophin over-expression in the rat spinal cord. We also demonstrated that the pro-ram can be used to quantify neurotrophin-induced growth of lesioned serotonergic axons in the rat cortex, where manual measurement would be impractical due to dense axonal growth. The use of this software suite provided faster and less biased quantification of labeled axons in comparison to manual measurements at no cost. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:172 / 179
页数:8
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