High-throughput gait acquisition system for freely moving mice

Normal and pathological locomotion can be discriminated by analyzing an animal's gait on a linear walkway. This step is labor intensive and introduces experimental bias due to the handling involved while placing and removing the animal between trials. We designed a system consisting of a runway embedded within a larger arena, which can be traversed ad libitum by unsupervised, freely moving mice, triggering the recording of short clips of locomotor activity. Multiple body parts were tracked using DeepLabCut and fed to an analysis pipeline (GaitGrapher) to extract gait metrics. We compared the results from unsupervised against the standard experimenter-supervised approach and found that gait parameters analyzed via the new approach were similar to a previously validated approach (Visual Gait Lab). These data show the utility of incorporating an unsupervised, automated, approach for collecting kinematic data for gait analysis.NEW & NOTEWORTHY The acquisition and analysis of walkway data is a time-consuming task. Here, we provide an unmonitored approach for collecting gait metrics that reduces the handling and stress of mice and saves time. A detailed pipeline is outlined that provides for the collection and analysis of data using an integrated suite of tools.