AxonQuantifier: A semi-automated program for quantifying axonal density from whole-mounted optic nerves.
Journal of neuroscience methods
BACKGROUND: Here, we present a semi-automated method for quantifying retinal ganglion cell (RGC) axon density at different distances from the optic nerve crush site using longitudinal, confocal microscopy images taken from whole-mounted optic nerves. This method employs the algorithm AxonQuantifier which operates on the freely available program, ImageJ.
NEW METHOD: To validate this method, seven adult male Long Evans rats underwent optic nerve crush injury followed by in vivo treatment with electric fields of varying strengths for 30 days to produce optic nerves with a wide range of axon densities distal to the optic nerve crush site. Prior to euthanasia, RGC axons were labelled with intravitreal injections of cholera toxin B conjugated to Alexa Fluor 647. After dissection, optic nerves underwent tissue clearing, were whole-mounted, and imaged longitudinally using confocal microscopy.
COMPARISON WITH EXISTING METHODS: Five masked raters quantified RGC axon density at 250, 500, 750, 1000, 1250, 1500, 1750, and 2000 µm distances past the optic nerve crush site for the seven optic nerves manually and using AxonQuantifier. Agreement between these methods was assessed using Bland-Altman plots and linear regression. Inter-rater agreement was assessed using the intra-class coefficient.
RESULTS: Semi-automated quantification of RGC axon density demonstrated improved inter-rater agreement and reduced bias values as compared to manual quantification, while also increasing time efficiency 4-fold. Relative to manual quantification, AxonQuantifier tended to underestimate axon density.
CONCLUSIONS: AxonQuantifier is a reliable and efficient method for quantifying axon density from whole mount optic nerves.
Peng MG, Lee J, Ho W, Kim T, Yao P, Medvidovic S, et al [Runner MM] AxonQuantifier: a semi-automated program for quantifying axonal density from whole-mounted optic nerves. J Neurosci Methods. 2023 Jul 1;394:109895. doi: 10.1016/j.jneumeth.2023.109895. PMID: 37315846.