Abstract
Behavioural assays represent sensitive methods for detecting neuronal dysfunction in model organisms. A number of manual methods have been established for Drosophila, however these are time-consuming and generate parameter-poor phenotype descriptors. Here, we have developed an automated computer vision system to monitor accurately the three-dimensional locomotor trajectories of flies. This approach allows the quantitative description of fly trajectories, using small fly cohorts and short acquisition times. The application of this approach to a Drosophila model of Alzheimer's disease enables the early detection of progressive locomotor deficits and the quantitative assessment of phenotype severity. The approach can be widely applied to different disease models in a number of model organisms.
Highlights
Protein misfolding and aggregation underpin many disorders of ageing (Fontana et al, 2010), the most common example being Alzheimer’s disease (AD) (Mucke, 2009; Thal et al, 2004)
We describe here an automated three-dimensional tracking system that allows the rapid measurement of the locomotor behaviour of individual flies and the calculation of a range of quantitative parameters that describe accurately their movements
We show that this method detects very early abnormalities in a fly model of AD through a more sensitive phenotype analysis than conventional methods
Summary
Protein misfolding and aggregation underpin many disorders of ageing (Fontana et al, 2010), the most common example being Alzheimer’s disease (AD) (Mucke, 2009; Thal et al, 2004). We describe here an automated three-dimensional tracking system that allows the rapid measurement of the locomotor behaviour of individual flies and the calculation of a range of quantitative parameters that describe accurately their movements. We show that this method detects very early abnormalities in a fly model of AD through a more sensitive phenotype analysis than conventional methods. We have developed computational procedures to detect and track individual flies and to parameterise their trajectories (Fig. 1b) This procedure allows the triangulation of each fly image in three dimensions and is defined as successful if two out of three constructed rays cross within 100 m. The use of the fruit fly to model AD has many advantages in terms of speed and genetic flexibility; we add to the power of the model system by presenting an automated locomotor assay that generates high-throughput quantitative locomotor data, allowing the early detection of AD-related phenotypes
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