A Simple Technique to Assay Locomotor Activity in Drosophila

Summary

The present protocol assesses the locomotor activity of Drosophila by tracking and analyzing the movement of flies in a hand-made arena using open-source software Fiji, compatible with plugins to segment pixels of each frame based on high-definition video recording to calculate parameters of speed, distance, etc.

Abstract

Drosophila melanogaster is an ideal model organism for studying various diseases due to its abundance of advanced genetic manipulation techniques and diverse behavioral features. Identifying behavioral deficiency in animal models is a crucial measure of disease severity, for example, in neurodegenerative diseases where patients often experience impairments in motor function. However, with the availability of various systems to track and assess motor deficits in fly models, such as drug-treated or transgenic individuals, an economical and user-friendly system for precise evaluation from multiple angles is still lacking. A method based on the AnimalTracker application programming interface (API) is developed here, which is compatible with the Fiji image processing program, to systematically evaluate the movement activities of both adult and larval individuals from recorded video, thus allowing for the analysis of their tracking behavior. This method requires only a high-definition camera and a computer peripheral hardware integration to record and analyze behavior, making it an affordable and effective approach for screening fly models with transgenic or environmental behavioral deficiencies. Examples of behavioral tests using pharmacologically treated flies are given to show how the techniques can detect behavioral changes in both adult flies and larvae in a highly repeatable manner.

Introduction

Drosophila melanogaster provides an excellent model organism for investigating cellular and molecular functions in neuronal disease models created by gene modification¹, drug treatment², and senescence³. The high conservation of biological pathways, physical properties, and disease-associated homolog genes between humans and Drosophila makes the fruit fly an ideal mimic from the molecular to the behavioral level⁴. In many disease models, behavioral deficiency is an important index, providing a helpful model for various human neuropathies^5,6. Drosophila is now used to study multiple human diseases, neurodevelopment, and neurodegenerative diseases such as Parkinson's disease and amyotrophic lateral sclerosis^7,8. Detecting the motor ability of the disease models is crucial for understanding the pathogenic progress and may provide a phenotypic correlation to the molecular mechanisms underlying the disease process.

Recently, commercially available software tools and cost-effective programs have been developed for Drosophila locomotor detection strategies, such as high-throughput testing in grouped flies^9,10 and measuring locomotion in real-time^11,12. One such conventional approach is rapid interactive negative geotaxis (RING), also called the climbing assay, which includes multiple channels that allow for a large fly population with the same gender and age to be contained, reducing variation while data collecting^9,13. Another pre-testing method for analyzing locomotor behavior is TriKinetics Drosophila activity monitor (DAM), a device that uses multiple beams to detect fly activity movement within a thin glass tube¹⁴. The device records position continuously, which represents automated locomotion by calculating the beam-crossings to study the activity and circadian rhythm of flies over a longer period of time¹⁵. Although these methods have been widely used in analyzing behavioral defects in fruit flies to determine changes in behavioral locomotion, they always require special testing equipment or complex analysis processes, and restrict their application in some models with a limited, simple device. Animal-tracing group-based strategies for testing the adult Drosophila, such as FlyGrAM¹¹ and the Drosophila island assay¹⁰, implement social recruitment and individual tracking in a predefined area. Nevertheless, social individual restriction in defied areas might have a negative effect on identifications in the images, caused by the collision or overlapping of flies. Even though some open-source materials-based methods, such as TRex¹⁶, MARGO¹², and FlyPi¹⁷, have an emergency, they can fast-track trace the flies with flexible usage in behavioral testing. These testing approaches are associated with elaborate experimental apparatus installations, special software requirements, or professional computer languages. For larvae, measuring the total distance traveled across the number of grid border lines per unit of time¹⁸, or rough counting the body wall contractions for individuals manually¹⁹, are the predominant methods for assessing their locomotor ability. Due to the lack of precision in equipment or devices and analysis methods, some behavioral locomotion of larvae might escape detection, making it difficult to accurately assess behavioral movement, especially fine movement¹⁵.

The present developed method utilizes the AnimalTracker application programming interface (API), compatible with the Fiji (ImageJ) image processing program, to systematically evaluate the locomotor activity of both adult and larval flies by analyzing their tracking behavior from high-definition (HD) videos. Fiji is an open-source software ImageJ distribution that can combine robust software libraries with numerous scripting languages, resulting in rapid prototyping of image processing algorithms, making it popular among biologists for its image analysis capabilities²⁰. In the current approach, Fiji's integration into the AnimalTracker API is exploited to develop a unique Drosophila behavioral assay with personalized algorithm insertion, and provides a useful step for detailed documentation and tutorials to support robust analytical capabilities of locomotor behavior (Figure 1). To circumvent the complication of objective identifications in the images caused by the collision or overlapping of flies, each arena is restricted to hosting only one fly. Upon assessing the tracking precision of the approach, it was implemented to trace and quantify the locomotor movements of Drosophila that were administered with the toxic drug rotenone, which is generally used for animal models of Parkinson's disease, ultimately discovering locomotion impairment in the drug treatment²¹. This methodology, which employs open-source and free software, does not necessitate high-cost instrumentation, and can precisely and reproducibly analyze Drosophila behavioral locomotion.

Protocol

W¹¹¹⁸ adult flies and third instar larvae were used for the present study.

1. Experimental preparation

NOTE: An open-field arena for Drosophila locomotion tracking is made withacolorless and odorless silica gel.

1. Mix reagent A and reagent B at a ratio of 1:10, according to the manufacturer's instructions for the silica kit (see Table of Materials). Ensure that sodium bicarbonate is added to the mixture by stirring until the color changes to white. Transfer the mixture to a clean Petri dish and place it in an oven at 40 °C for drying for 48 h.
2. Set the HD camera (see Table of Materials) on a tripod, adjusting it so that the camera lens is perpendicular to the surface of the silica arena. Adjusting the focal length and the apertures of the camera, ensure that the camera is focused on the surface of the silica and that the display is adequately illuminated. The experimental setup is illustrated in Figure 1.
3. Transfer a fly into the open-field arena to record a continuous video of at least 61 s.
   NOTE: Considering the sluggish nature of larvae, a video recording time of more than 10 min is recommended.
   1. Open the video with Fiji, drag the progress bar to the initial frame, and tacitly approve. Choose the whole body of the fly using the "freehand selection" tool (Figure 2B,C).
   2. Click image > adjust > brightness & contrast to adjust the white balance until the gray value of the selected area approaches the broad background (Figure 2D-F).
      ​NOTE: Background homogenization of the first frame enables the software to distinguish the background without any objects and create a contrast when a fly is present, thus allowing the software to track it.
4. Perform the entire experiment in a testing environment set at 25 °C and 60% relative humidity, in an area that is quiet and devoid of exposure to bright light.

2. Video recording and preprocessing

1. After a short period of anesthesia using95%carbon dioxide (CO₂), transfer a fly to the open-filed arena and press the record button on the camera application to start video recording.
   NOTE: To minimize the effect of the anesthetic on locomotion, allow the flies to recover for 10 min before initiating video recording. Cool-anesthetizing by chilling is also recommended.
   1. Once the flies recover from the anesthesia, put the arena dish containing the fly under the camera and shake the plate quickly from side to side to ensure that the fly is in motion when the recording begins.
2. Upon completion of the recording, press the stop button to terminate the video recording.
   NOTE: Ensure that the video recording time slightly exceeds the destination tracking time by a small margin. In addition, to improve the experimental efficiency, it is possible to track multiple flies spontaneously. This depends on the resolution of the camera to enable a high-quality video crop.
3. Convert the recorded videos into AVI format with MJPEG encoding, so they can be opened and analyzed using Fiji. Meanwhile, set the frames per second (fps) rate of the video to 15 fps for adult flies and 12 fps for larvae.

3. Video analysis

1. Open the video that has been transformed with "use virtual stack" and "convert to grayscale", two options in the popup window when opening the video with Fiji (Figure 2A).
2. Make a blank first frame, as mentioned above.
3. Obtain a processing window by using the "set active image" tool of the AnimalTracker plugin and create a tracking area that circles the arena in the original video window using the "oval" tool (Figure 3A).
4. Set the filters (Figure 3A,3) and the parameters of the two filters (Figure 4A-G) for the first blank frame in the processing window. Then, select the next frame in the original video window, and choose the filtered surface of the processing window (Figure 5A-C).
   NOTE: The filtering step serves to decrease image noise and/or remove the background, thus making it simpler to separate the foreground from the background in the binarization of the frames.
5. Once a filtered processing window is selected, turn the tracked fly with a red profile covered in the processing window by using the "set threshold" tool (Figure 3A,4, Figure 5D-E, and Figure 6A).
6. Use the "set blob-detector" to let the computer recognize the fly with a red profile covered in the processing window (Figure 3A,5 and Figure 6B).
7. Set frame 901 as the last frame for the adult fly, calculated by the video's recording duration and fps (Figure 3A,6, Figure 6C).
   NOTE: The following experiment with larvae has been tracked for 10 min, so frame 7200 is set as the last frame.
8. Use the "show blobs" tool to present a tracking rectangle in the original video window (Figure 3A,7 and Figure 6D,E). Then, start the tracking and export the tracking file after the monitoring is completed (Figure 3A,8,9 and Figure 7A,B).

4. Tracking file analysis

1. Load the track and zone files using the Animal tracker > Tracking analyzer plugin (Figure 8A).
2. Select the desired index using zone settings and alter the parameter settings (Figure 8). Calculate the time of the frame interval using the frame rate.
   NOTE: In this condition, the frame rate is 15 fps, and the frame interval is approximately 0.067 s, which is the default setting (Figure 8D).
3. Produce the quantitative analysis charts using the spreadsheet software and GraphPad Prism after being analyzed in tracking analyzer (Figure 9).

5. Analysis per frame

1. Perform speed analysis per frame interval. Analyze the track file without Fiji if more detailed research is needed.
   1. Open the track file, copy all coordinates to Microsoft Office Excel, and split the cells using the space key.
      NOTE: For example, once the file has been divided into "C" and "D" columns, the speed of Drosophila per frame interval is calculated by the formula SQRT((C5-C4)^2+(D5-D4)^2), which is shown in the "E" column (Figure 10A). The data in column "E" indicates the number of pixels that the fly moved between two frames, with the first frame not being considered. Select all calculated results and insert a line chart to exhibit an intuitive fly movement speed per frame interval, with a peak on the line chart (Figure 10B).
2. Calculate the immobility time per frame interval. After the file has been split into "C" and "D" columns, calculate the immobility status of Drosophila per frame interval using the formula IF(SQRT((C6-C5)^2+(D6-D5)^2) <20, 0, 1), which is shown in the "E" column. (Figure 10C).
   NOTE: Unlike speed analysis, the results of the first frame were defined. Flies that moved fewer than 20 pixels were considered immobile and recorded as "0" in column "E".
   1. Select all calculated results and insert a column chart to visually exhibit the immobility time by the margin of the whole column chart (Figure 10D).
3. Ensure that the angle of direction changes.
   NOTE: The angle of direction change analysis represents the flies' direction choice. Once the file has been split into "C" and "D" columns, the angle of direction change is calculated by the formula ACOS(((SQRT((C7-C6)^2+(D7-D6)^2))^2+(SQRT((C6-C5)^2+(D6-D5)^2))^2-(SQRT((C7-C5)^2+(D7-D5)^2))^2)/(2*SQRT((C6-C5)^2+(D6-D5)^2))*(SQRT((C7-C6)^2+(D7-D6)^2)))*180/PI(), which is presented in the "E" column (Figure 10E). The calculated results indicates the angle between three coordinates.
   1. Select all calculated results and insert a scatter diagram to illustrate the angle of direction change of the flies' movement (Figure 10F).

Results

In the present study, locomotor deficits in adult flies and third instar larvae treated with rotenone were examined and compared in their motor activity to that of a control fly fed with the drug solvent dimethyl sulfoxide (DMSO). Treatment with rotenone in Drosophila has been shown to cause dopaminergic neuron loss in the brain²² and lead to significant locomotor deficits²³. As shown in Figure 11 and Figure 12

Discussion

We have designed a method, based on the open-source material AnimalTracker API compatible with the Fiji image processing program, that can enable researchers to systematically evaluate locomotor activity by tracking both adult and individual larval flies. AnimalTracke is a tool written in Java that can be easily integrated into existing databases or other tools to facilitate the analysis of application-designed animal-tracking behavior²⁴. Upon a frame-by-frame analysis by a softw...

Materials

Name	Company	Catalog Number	Comments
Animal tracker	Hungarian Brain Research Program	version: 1.7	pfficial website: http://animaltracker.elte.hu/main/downloads
Camera software	Microsoft	version: 2021.105.10.0	built-in windows 10 system
Computer	DELL	Vostro-14-5480	a comupter running win 10 system is available
Drosophila carbon dioxide anesthesia workstation	Wu han Yihong technology	#YHDFPCO2-018	official website: http://www.yhkjwh.com/
Fiji software	Fiji team	version: 1.53v	official website: https://fiji.sc/
Format factory software	Pcfreetime	version: X64 5.4.5	official website: http://www.pcfreetime.com/formatfactory/CN/index.html
Graph pad prism	GraphPad Software	version: 8.0.2	official website: https://www.graphpad-prism.cn
Hight definition camera	TTQ	Jingwang2 (HD1080P F1.6 6-60mm)	official website: http://www.ttq100.com/product_show.php?id=35
Office software	Microsoft	version: office 2019	official website: https://www.microsoftstore.com.cn/software/office
Petri dish	Bkman	110301003	size: 60 mm
Silica gel	DOW	SYLGARD 184 Silicone Elastomer Kit	Mix well according to the instructions
Sodium bicarbonate	Macklin	#144-55-8	Mix well with silica gel