A photo-switchable assay system for dendrite degeneration and repair in Drosophila melanogaster

Neurodegeneration arising from aging, injury or disease has devastating health consequences. Whereas neuronal survival and axon degeneration have been studied extensively, much less is known about how neurodegeneration impacts dendrites. To develop an assay for dendrite degeneration and repair in the Drosophila peripheral nervous system, we used photo-switchable caspase-3 (caspase-LOV) to induce neuronal damage with tunable severity by adjusting illumination duration, thereby revealing cell type-specific responses to caspase-3 induced dendrite degeneration in dendrite arborization (da) neurons. To ask whether mechanisms underlying axon degeneration also govern dendrite degeneration, we tested the involvement of the Wallerian degeneration pathway by examining the effects of expressing the mouse Wallerian degeneration slow (WldS) protein and knockdown of the Drosophila sterile alpha/Armadillo/Toll-Interleukin receptor homology domain protein (dSarm1) and Axundead (Axed) in class 4 da neurons. Here we report WldS expression or knockdown of dSarm1 improved dendrite repair following caspase-3 induced dendrite degeneration. Whereas both dSarm1 and Axed were required for thermal nocifensive behavior in uninjured animals, WldS expression improved the recovery of thermal nocifensive behavior that was impaired by chronic low-level of caspase-LOV activity. By establishing ways to induce graded dendrite degeneration, we uncover a protective role of WldS in caspase-3 induced dendrite degeneration and repair.

Machine learning-based automated phenotyping of inflammatory nocifensive behavior in mice

The discovery and development of new and potentially nonaddictive pain therapeutics requires rapid, yet clinically relevant assays of nociception in preclinical models. A reliable and scalable automated scoring system for nocifensive behavior of mice in the formalin assay would dramatically lower the time and labor costs associated with experiments and reduce experimental variability. Here, we present a method that exploits machine learning techniques for video recordings that consists of three components: key point detection, per frame feature extraction using these key points, and classification of behavior using the GentleBoost algorithm. This approach to automation is flexible as different model classifiers or key points can be used with only small losses in accuracy. The adopted system identified the behavior of licking/biting of the hind paw with an accuracy that was comparable to a human observer (98% agreement) over 111 different short videos (total 284 min) at a resolution of 1 s. To test the system over longer experimental conditions, the responses of two inbred strains, C57BL/6NJ and C57BL/6J, were recorded over 90 min post formalin challenge. The automated system easily scored over 80 h of video and revealed strain differences in both response timing and amplitude. This machine learning scoring system provides the required accuracy, consistency, and ease of use that could make the formalin assay a feasible choice for large-scale genetic studies.