Using DeepLabCut for markerless cardiac physiology and toxicity estimation in water fleas (Daphnia magna)

Daphnia magna is one species of water flea that has been used for a long time for ecotoxicity studies. In addition, Daphnia has a myogenic heart that is very useful for cardiotoxicity studies. Previous attempts to calculate the cardiac parameter endpoints in Daphnia suffer from the drawback of tedious operation and high variation due to manual counting errors. Even the previous method that utilized deep learning to help the process suffer from either overestimation of parameters or the need for specialized equipment to perform the analysis. In this study, we utilized DeepLabCut software previously used for animal pose tracking and demonstrated that ResNet_152 was the best fit for training the network. The trained network also showed comparable results with ImageJ and Kymograph, which was mostly done manually. In addition to that, several macro scripts in either Excel or Python format were developed to help summarize the data for faster analysis. The trained network was then challenged to analyze the potential cardiotoxicity of imidacloprid and pendimethalin in D. magna, and it showed that both pesticides cause alteration in their cardiac performance. Overall, this method provides a simple and automatic method to analyze the cardiac performance of Daphnia by utilizing DeepLabCut. The method proposed in this paper can contribute greatly to scientists conducting fast and accurate cardiotoxicity measurements when using Daphnia as a model.