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AxoMetric: A Rapid and Unbiased Tool for Automated Quantification of Axon Regeneration in Tissue Sections

Finneran, M. C.; Rhamani, T.; Salioski, I. V.; Schmitd, L. B.; Passino, R.; Johnson, C. N.; Giger, R.

2025-07-03 neuroscience
10.1101/2025.07.02.662816 bioRxiv
Show abstract

Recent advances in experimental strategies that promote axon regeneration in adult mammals lay the foundation for future therapies. Reliable and unbiased quantification of regenerated axons is challenging, yet essential for comparing the efficacy of individual treatments and identification of most efficacious combinatorial therapies. Here, we introduce AxoMetric, a user-friendly and freely available software for the rapid quantification of regenerated axons in longitudinal nerve tissue sections. AxoMetric automatically identifies and traces regenerated axons, generating quantitative measurements that closely match conventional manual quantification but with significantly greater speed. Key features include length-dependent axon quantification at defined intervals from the injury site and normalization of axon density to nerve diameter to account for anatomical variability. To facilitate high-throughput analysis, the software includes an image queuing function. Additional features of AxoMetric allow quantification of a range of labeled cellular structures. As a proof of concept, we demonstrate accurate quantification of regenerated axons in the optic nerve, retinal ganglion cells density in retinal flat-mounts, and regenerated axon bundles in injured sciatic nerves. Collectively, we introduce a new platform that is expected to streamline and standardize regenerative outcome assessments across diverse experimental conditions and laboratories. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/662816v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@162d692org.highwire.dtl.DTLVardef@1f52ed4org.highwire.dtl.DTLVardef@1d58f76org.highwire.dtl.DTLVardef@1bdabb1_HPS_FORMAT_FIGEXP M_FIG C_FIG

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