An Automated Strategy for Unbiased Morphometric Analyses and Classifications of Growth Cones In Vitro

During neural circuit development, attractive or repulsive guidance cue molecules direct growth cones (GCs) to their targets by eliciting cytoskeletal remodeling, which is reflected in their morphology. The experimental power of in vitro neuronal cultures to assay this process and its molecular mechanisms is well established, however, a method to rapidly find and quantify multiple morphological aspects of GCs is lacking. To this end, we have developed a free, easy to use, and fully automated Fiji macro, Conographer, which accurately identifies and measures many morphological parameters of GCs in 2D explant culture images. These measurements are then subjected to principle component analysis and k-means clustering to mathematically classify the GCs as "collapsed" or "extended". The morphological parameters measured for each GC are found to be significantly different between collapsed and extended GCs, and are sufficient to classify GCs as such with the same level of accuracy as human observers. Application of a known collapse-inducing ligand results in significant changes in all parameters, resulting in an increase in 'collapsed' GCs determined by k-means clustering, as expected. Our strategy provides a powerful tool for exploring the relationship between GC morphology and guidance cue signaling, which in particular will greatly facilitate high-throughput studies of the effects of drugs, gene silencing or overexpression, or any other experimental manipulation in the context of an in vitro axon guidance assay.