Abstract
The climbing assay, used to assess locomotive capabilities of Drosophila, takes advantage of the negative geotropism reflex. After a vial is knocked at the bench and flies are brought down to the bottom by that movement, they exhibit the instinct of climbing up away from gravity. Measuring how high flies can climb (performance index) provides a measure of their locomotion. However, the performance index is only a rough estimation of average height, while manual data analysis is slow and prone to human error/bias. The method has been modernised, and semi-automated but current protocols have several drawbacks. Programs are data intensive, requiring the storage of many videos/photos with large file sizes. In this context, we produced FlyClimber, a user and set-up friendly Python-based program. FlyClimber requires minimal hardware set up, reducing the required data storage space while providing accurate and robust data with high sensitivity. Employment of Statistical Parametric Mapping is novel for climbing assays, providing a time series of data with more potential for analyses than can currently be achieved by existing programs or manual methods.
