AxonTracer: a novel ImageJ plugin for automated quantification of axon regeneration in spinal cord tissue.
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BACKGROUND: Quantification of axon regeneration in spinal cord tissue sections is a fundamental step to adequately determine if an applied treatment leads to an anatomical benefit following spinal cord injury. Recent advances have led to the development of therapies that can promote regeneration of thousands of injured axons in vivo. Axon labeling methods and in the application of regeneration-enabling stem cell grafts have increased the number of detectable regenerating axons by orders of magnitudes. Manual axon tracing in such cases is challenging and laborious, and as such there is a great need for automated algorithms that can perform accurate tracing and quantification in axon-dense tissue sections. RESULTS: We developed "AxonTracer", a fully automated software algorithm that traces and quantifies regenerating axons in spinal cord tissue sections. AxonTracer is an open source plugin for the freely available image-processing program ImageJ. The plugin identifies transplanted cells grafts or other regions of interest (ROIs) based on immunohistological staining and quantifies regenerating axons within the ROIs. Individual images or groups of images (batch mode) can be analyzed sequentially. In batch mode, a unique algorithm identifies a reference image for normalization, as well as a suitable image for defining detection parameters. An interactive user interface allows for adjustment of parameters defining ROI size, axon detection sensitivity and debris cleanup. Automated quantification of regenerating axons by AxonTracer correlates strongly with semi-manual quantification by the widely-used ImageJ plugin NeuronJ. However, quantification with AxonTracer is automated and reduces the need for user input compared to alternative methods. CONCLUSIONS: AxonTracer is a freely available open-source tool for automated analysis of regenerating axons in the injured nervous system. An interactive user interface provides detection-parameter adjustment, and usage does not require prior image analysis experience. Raw data as well as normalized results are stored in spreadsheet format and axon tracings are superimposed on raw images allowing for subjective visual verification. This software allows for automated, unbiased analysis of hundreds of axon-dense images, thus providing a useful tool in enabling in vivo screens of axon regeneration following spinal cord injury.
