Abstract
The aging of the human brain in the absence of diseases is accompanied by subtle changes of neuronal morphology, such as dendrite restructuring, neuronal sprouting, and synaptic deteriorations, rather than neurodegeneration or gross deterioration. Similarly, the nervous system of Caenorhabditis elegans does not show neurodegeneration or gross deterioration during normal aging, but displays subtle alterations in neuronal morphology. The occurrence of these age-dependent abnormalities is stochastic and dynamic, which poses a major challenge to fully capture them for quantitative comparison. Here, we developed a semi-automated pipeline for quantitative image analysis of these features during aging. We employed and evaluated this pipeline herein to reproduce findings from previous studies using visual inspection of neuronal morphology. Importantly, our approach can also quantify additional features, such as soma volume, the length of neurite outgrowths, and their location along the aged neuron. We found that, during aging, the soma of neurons decreases in volume, whereas the number and length of neurite outgrowths from the soma both increase. Long-lived animals showed less decrease in soma volume, fewer and shorter neurite outgrowths, and protection against abnormal sharp bends preferentially localized at the distal part of the dendrites during aging. We found a correlation of sharp bends with neurite outgrowth, suggesting the hypothesis that sharp bends might proceed neurite outgrowths. Thus, our semi-automated pipeline can help researchers to obtain and analyze quantitative datasets of this stochastic process for comparison across genotypes and to identify correlations to facilitate the generation of novel hypothesis.