Automated identification of axonal growth cones in time-lapse image sequences

The isolation and purification of axon guidance molecules has enabled in vitro studies of the effects of axon guidance molecule gradients on numerous neuronal cell types. In a typical experiment, cultured neurons are exposed to a chemotactic gradient and their growth is recorded by manual identification of the axon tip position from two or more micrographs. Detailed and statistically valid quantification of axon growth requires evaluation of a large number of neurons at closely spaced time points (e.g. using a time-lapse microscopy setup). However, manual tracing becomes increasingly impractical for recording axon growth as the number of time points and/or neurons increases. We present a software tool that automatically identifies and records the axon tip position in each phase-contrast image of a time-lapse series with minimal user involvement. The software outputs several quantitative measures of axon growth, and allows users to develop custom measurements. For, example analysis of growth velocity for a dissociated E13 mouse cortical neuron revealed frequent extension and retraction events with an average growth velocity of 0.05 ± 0.14 μm/min. Comparison of software-identified axon tip positions with manually identified axon tip positions shows that the software's performance is indistinguishable from that of skilled human users.