AxonTracer: a novel ImageJ plugin for automated quantification of axon regeneration in spinal cord tissue

BMC Neuroscience, 2018 · DOI: https://doi.org/10.1186/s12868-018-0409-0 · Published: February 27, 2018

Spinal Cord Injury Neurology Medical Imaging

Simple Explanation

The study introduces AxonTracer, an open-source ImageJ plugin designed for automated tracing and quantification of regenerating axons in spinal cord tissue sections. It addresses the need for rapid and accurate analysis of axon regeneration, especially in axon-dense tissue sections following spinal cord injury. AxonTracer allows users to analyze individual images or batches, automatically identify regions of interest (ROIs) based on staining, adjust detection parameters, and normalize data. It streamlines the process of quantifying axon regeneration compared to manual methods. The software enables automated, unbiased analysis of numerous axon-dense images, making it a useful tool for in vivo screens of axon regeneration after spinal cord injury. The interactive interface and data output features facilitate visual verification and data interpretation.

Study Duration

4 weeks

Participants

Mice (n=4), Rats (n=9)

Evidence Level

Not specified

Key Findings

1
AxonTracer correlates strongly with semi-manual quantification by NeuronJ, a widely used ImageJ plugin, but with full automation and reduced user input.
2
AxonTracer can be used for automatic quantification of corticospinal axon sprouting in spinal cord grey matter using the neuronal cell body marker NeuN to generate automatic ROIs.
3
AxonTracer identifies a significant 37% increase in total axon length in the injured group compared to the uninjured group, demonstrating its ability to detect injury-induced CST sprouting above the level of injury.

Research Summary

The study presents AxonTracer, a novel open-source ImageJ plugin for automated analysis of regenerating axons in spinal cord tissue. AxonTracer provides an interactive user interface, allowing for parameter adjustment and does not require prior image analysis experience. The software allows for automated, unbiased analysis of axon-dense images. AxonTracer's application is demonstrated in quantifying regenerating axons into cell grafts and collateral axon sprouting into spinal cord grey matter, showing comparable results to NeuronJ with complete automation.

Practical Implications

Accelerated Research

The software enables researchers to rapidly and accurately quantify axon regeneration, accelerating the development of therapies for spinal cord injury.

Unbiased Analysis

The automated and unbiased nature of AxonTracer ensures consistent quantification across experimental groups, reducing subjective errors.

Broad Applicability

While designed for spinal cord tissue, AxonTracer can be adapted for image-based quantification of fibrous structures in other tissues, expanding its utility to the broader scientific community.

Study Limitations

1
For images with a high dynamic range that contain both dense axons with high fluorescent signal intensity as well as sparse axons with low fluorescent signal, detection accuracy is reduced.
2
The user needs to define the detection parameters and adjust them in an interactive process.
3
AxonTracer is implemented as an ImageJ macro, and therefore easily modifiable by the user.

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