EasyFlyTracker: A Simple Video Tracking Python Package for Analyzing Adult Drosophila Locomotor and Sleep Activity to Facilitate Revealing the Effect of Psychiatric Drugs

An automatic system for recognizing fly courtship patterns via an image processing method

Fruit fly courtship behaviors composed of a series of actions have always been an important model for behavioral research. While most related studies have focused only on total courtship behaviors, specific courtship elements have often been underestimated. Identifying these courtship element details is extremely labor intensive and would largely benefit from an automatic recognition system. To address this issue, in this study, we established a vision-based fly courtship behavior recognition system. The system based on the proposed image processing methods can precisely distinguish body parts such as the head, thorax, and abdomen and automatically recognize specific courtship elements, including orientation, singing, attempted copulation, copulation and tapping, which was not detectable in previous studies. This system, which has high identity tracking accuracy (99.99%) and high behavioral element recognition rates (> 97.35%), can ensure correct identification even when flies completely overlap. Using this newly developed system, we investigated the total courtship time, and proportion, and transition of courtship elements in flies across different ages and found that male flies adjusted their courtship strategy in response to their physical condition. We also identified differences in courtship patterns between males with and without successful copulation. Our study therefore demonstrated how image processing methods can be applied to automatically recognize complex animal behaviors. The newly developed system will largely help us investigate the details of fly courtship in future research.

The effects of methylphenidate and atomoxetine on Drosophila brain at single-cell resolution and potential drug repurposing for ADHD treatment

The stimulant methylphenidate (MPH) and the non-stimulant atomoxetine (ATX) are frequently used for the treatment of attention-deficit/hyperactivity disorder (ADHD); however, the function of these drugs in different types of brain cells and their effects on related genes remain largely unknown. To address these questions, we built a pipeline for the simultaneous examination of the activity behavior and transcriptional responses of Drosophila melanogaster at single-cell resolution following drug treatment. We selected the Drosophila with significantly increased locomotor activities (hyperactivity-like behavior) following the administration of each drug in comparison with the control (same food as the drug-treated groups with 5% sucrose, yeast, and blue food dye solution) using EasyFlyTracker. Subsequently, single cell RNA sequencing (scRNASEQ) was used to capture the transcriptome of 82,917 cells, unsupervised clustering analysis of which yielded 28 primary cell clusters representing the major cell types in adult Drosophila brain. Indeed, both neuronal and glial cells responded to MPH and ATX. Further analysis of differentially expressed genes (DEGs) revealed distinct transcriptional changes associated with these two drugs, such as two well-studied dopamine receptor genes (Dop2R and DopEcR) were responsive to MPH but not to ATX at their optimal doses, in addition to genes involved in dopamine metabolism pathways such as Syt1, Sytalpha, Syt7, and Ih in different cell types. More importantly, MPH also suppressed the expression of genes encoding other neurotransmitter receptors and synaptic signaling molecules in many cell types, especially those for Glu and GABA, while the responsive effects of ATX were much weaker. In addition to monoaminergic neuronal transmitters, other neurotransmitters have also shown a similar pattern with respect to a stronger effect associated with MPH than with ATX. Moreover, we identified four distinct glial cell subtypes responsive to the two drugs and detected a greater number of differentially expressed genes associated with ensheathing and astrocyte-like glia. Furthermore, our study provides a rich resource of candidate target genes, supported by drug set enrichment analysis (P = 2.10E-4; hypergeometric test), for the further exploration of drug repurposing. The whole list of candidates can be found at ADHDrug ( http://adhdrug.cibr.ac.cn/ ). In conclusion, we propose a fast and cost-efficient pipeline to explore the underlying molecular mechanisms of ADHD drug treatment in Drosophila brain at single-cell resolution, which may further facilitate drug repurposing applications.

DVT: a high-throughput analysis pipeline for locomotion and social behavior in adult Drosophila melanogaster

BACKGROUND

Drosophila melanogaster is excellent animal model for understanding the molecular basis of human neurological and motor disorders. The experimental conditions and chamber design varied between studies. Moreover, most previously established paradigms focus on fly trace detection algorithm development. A comprehensive understanding on how fly behaves in the chamber is still lacking.

RESULTS

In this report, we established 74 unique behavior metrics quantifying spatiotemporal characteristics of adult fly locomotion and social behaviors, of which 49 were newly proposed. By the aiding of the developed analysis pipeline, Drosophila video tracking (DVT), we identified siginificantly different patterns of fly behavior confronted with different chamber height, fly density, illumination and experimental time. Meanwhile, three fly strains which are widely used as control lines, Canton-S(CS), w1118 and Oregon-R (OR), were found to exhibit distinct motion explosiveness and exercise endurance.

CONCLUSIONS

We believe the proposed behavior metrics set and pipeline should help identify subtle spatial and temporal differences of drosophila behavior confronted with different environmental factors or gene variants.

Protocol for recording and analyzing spontaneous locomotion in Drosophila

The quantitative analysis of locomotion is used to study many biological processes. Here, we describe how to record the locomotion of up to 50 Drosophila individuals and process the resulting video files using FlyTracker. We detail the use of modifiable MatLab scripts to process structure array files generated by FlyTracker. We have applied this to study Drosophila movement during aging, but it could be used to address a variety of research questions. For complete details on the use and execution of this protocol, please refer to Barwell et al. (2021).¹.

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a “Wearables” R package and a Shiny “E4 dashboard” application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.