A method and apparatus for detecting fruit fly behavior

Abstract

The application belongs to the technical field of biological information, and particularly relates to a method and device for detecting fruit fly behavior, which comprises the following steps: placing fruit flies to be detected in a multi-well culture plate, and after the fruit flies adapt to the culture plate environment, video shooting is performed on the fruit flies in each well of the multi-well culture plate from above, and a high-throughput fruit fly behavior video file is obtained, wherein the time period of video shooting is determined according to fruit fly behavior information to be detected; the video file is processed into frame-by-frame pictures, and the coordinates of each fruit fly in each well of the multi-well culture plate at each frame are extracted; the fruit fly behavior information to be detected of each fruit fly in each well of the multi-well culture plate is determined based on the coordinates of each fruit fly in each well of the multi-well culture plate at each frame according to preset behavior characteristics corresponding to the fruit fly behavior information to be detected, so that the detection of fruit fly behavior is realized. The application can realize high-throughput fruit fly behavior detection, and overcome the problems of long experimental cycle and high labor cost of existing fruit fly behavior detection methods.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology, and more specifically, relates to a method and apparatus for detecting fruit fly behavior. Background Technology

[0002] Animals rarely act alone, and their interactions with other members of the same species have a significant impact on their behavior and physiological state. Although the quantity and quality of social relationships were identified as major risk factors for health in humans and other animals more than three decades ago, the underlying mechanisms remain unclear. Fruit flies are social animals that exhibit dynamic social networks and collective behaviors that contribute to various essential life processes. Existing social experiences alter the sleep needs and structure of fruit flies, while prolonged social isolation leads to sleep deprivation and increased feeding activity. Sleep and movement behaviors within fruit fly populations also differ from those of isolated individuals, but how these behaviors are modulated in group settings with other members of the same species, and how they interact with social activities, remains to be studied.

[0003] However, the main limitations of traditional fruit fly behavioral research methods and devices are: (1) the lack of behavioral research methods that are close to natural conditions and suitable for large-scale measurement; (2) the most common behavioral measurement is time-consuming and labor-intensive, and the one-dimensional infrared ray method that can be tested on a large scale is limited to rhythm and sleep, and can only observe one-dimensional activity; (3) the existing two-dimensional culture plate method for detecting fruit fly behavior is difficult to conduct high-throughput large-scale behavioral testing because the identification of fruit fly behavior requires experienced experimentalists to spend a lot of time. Summary of the Invention

[0004] In view of the shortcomings of existing technologies and the need for improvement, this invention provides a method and apparatus for detecting fruit fly behavior, which aims to overcome the problems of long experimental cycles and high labor costs in existing fruit fly behavioral detection methods.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for detecting fruit fly behavior is provided, comprising:

[0006] The fruit flies to be tested were placed in a multi-well culture plate. After the fruit flies adapted to the culture plate environment, the fruit flies in each well of the multi-well culture plate were filmed from above to obtain a high-throughput fruit fly behavioral video file. The time period of video filming was determined based on the behavioral information of the fruit flies to be tested.

[0007] The high-throughput Drosophila behavioral video file was processed into frame-by-frame images, and the coordinates of each Drosophila in each well of the multi-well culture plate at each frame were extracted from them.

[0008] Based on the preset behavioral features corresponding to the behavior information of the fruit fly to be tested, and based on the coordinates of each fruit fly in each well of the multi-well culture plate at each frame, the behavior information of the fruit fly to be tested is determined, thereby realizing the detection of fruit fly behavior.

[0009] Furthermore, when the behavior of the fruit flies to be tested includes sleep, the culture conditions of the multi-well culture plate include 12 hours of light and 12 hours of darkness.

[0010] Furthermore, after extracting the coordinates of each fruit fly in each well of the multi-well culture plate at each frame, the method also includes:

[0011] Each fruit fly is numbered, and its coordinates in each frame are arranged and organized in chronological order to form a coordinate specification file;

[0012] The determination of the behavior information of the fruit fly to be tested is based on the coordinate specification file.

[0013] Furthermore, when the behavioral information of the fruit flies to be tested includes information related to fruit fly sleep, the preset behavioral features corresponding to the fruit fly sleep-related information are:

[0014] If the distance between two consecutive frames is less than a preset pixel, the fruit fly is considered to be in a stationary state; if the stationary state of a single fruit fly lasts for more than a preset duration, it is considered to be in a sleeping state; wherein, the preset pixel and the preset duration are determined in advance by observing the fruit flies in the multi-well culture plate.

[0015] Furthermore, when the behavioral information of the fruit fly to be tested includes social information related to the fruit fly, the preset behavioral features corresponding to the social information related to the fruit fly are:

[0016] When the distance between two fruit flies is less than a preset number of pixels, it is considered that social behavior has occurred between the two fruit flies; each social behavior is recorded starting from when the distance between the two fruit flies is less than the preset number of pixels, until the distance between the two fruit flies exceeds the preset number of pixels; wherein, the preset number of pixels is determined in advance by observing the fruit flies in the multi-well culture plate.

[0017] Furthermore, the preset number of pixels is 40.

[0018] Furthermore, when the fruit fly behavior information to be tested includes fruit fly movement-related information, the preset behavioral features corresponding to the fruit fly movement-related information are:

[0019] The pixel distance a fruit fly travels per unit of time.

[0020] The present invention also provides an apparatus for detecting fruit fly behavior, for performing a method for detecting fruit fly behavior as described above, comprising: a conventional biochemical incubator, an infrared camera, video capture software, and a processor;

[0021] An infrared camera is installed on the upper part of each multi-well culture plate in the conventional biochemical incubator to capture video of fruit flies in each well of the multi-well culture plate. The video capture software is used to control the infrared camera to perform video capture according to the behavior information of the fruit flies to be tested, and to receive and store the high-throughput fruit fly behavioral video files captured by the infrared camera. The processor is used to process the high-throughput fruit fly behavioral video files to obtain fruit fly behavior information.

[0022] This invention also provides a method for screening genes that regulate the behavior of fruit flies. Based on fruit fly behavioral information, genes with a relatively large degree of change relative to the overall level are selected from all genes to be screened as potential behavioral regulatory genes, thus completing the screening. The fruit fly behavioral information is obtained by a method for detecting fruit fly behavior as described above.

[0023] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects:

[0024] (1) This invention proposes a method for detecting fruit fly behavior. Firstly, a conventional biochemical incubator is modified into a fruit fly trajectory tracking device by adding an infrared camera. Through the infrared camera, large-scale, high-throughput raw data on fruit fly behavior is acquired, and specific behavioral characteristics are obtained through image processing technology. This invention, combining the modified fruit fly trajectory tracking device with image processing technology, effectively avoids the workload required for experimental verification in traditional fruit fly behavioral research. It also acquires characteristic information of various fruit fly behavioral phenotypes, providing a behavioral representation information database. This provides direction and a reference for extracting various behavioral information from other insects and small animals. This method of modeling, digitizing, and extracting behavioral characteristics provides a relatively labor-saving model for large-scale testing of biological image analysis and behavioral experiments in the industry.

[0025] (2) After extracting the coordinates of each fruit fly in each well of the multi-well culture plate in each frame, the method also includes numbering each fruit fly and arranging the coordinates of the fruit fly in each frame in chronological order to form a coordinate specification file; then, when determining the fruit fly behavior information to be tested, it can be based on the coordinate specification file. The coordinate arrangement in advance can improve the efficiency of subsequent fruit fly behavior detection.

[0026] (3) The present invention adaptively defines the preset behavioral features corresponding to fruit fly sleep-related information, fruit fly social-related information, and fruit fly movement-related information according to the improved tracking device, and realizes high-throughput detection of various fruit fly behaviors.

[0027] (4) The present invention also provides a device for detecting fruit fly behavior, which is used to implement the above-mentioned method for detecting fruit fly behavior, saving time and effort, and can efficiently realize large-scale fruit fly behavior testing. Attached Figure Description

[0028] Figure 1 A flowchart illustrating a method for detecting fruit fly behavior provided in an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of the experimental procedure for large-scale screening of genes related to sleep, movement and social behavior in fruit flies using a fruit fly behavioral detection method based on a fruit fly trajectory tracking device, as provided in an embodiment of the present invention.

[0030] Figure 3The following is a graph showing the results of a confirmatory experiment on whether the selected Drosophila sleep-related gene mre11 regulates sleep, provided in an embodiment of the present invention. In this graph, a is a line graph comparing the sleep characteristics of RNAi Drosophila with the mre11 gene and wild-type control flies containing the mre11 gene; b is a bar graph showing the total daily sleep duration of RNAi Drosophila with the mre11 gene and wild-type control flies containing the mre11 gene, as shown in a; c is a graph showing the total sleep duration of RNAi Drosophila with the mre11 gene and wild-type control flies containing the mre11 gene, monitored using the device of the present invention, with one fly per well. Bar chart of daily sleep time in Drosophila under isolation conditions; d is a bar chart of daily sleep time in RNAi Drosophila with the mre11 gene and wild-type control Drosophila containing the mre11 gene under isolation conditions (1 fly per well) monitored using a conventional one-dimensional Drosophila behavioral detection device (DAM); e is a bar chart of the average duration of each sleep in RNAi Drosophila with the mre11 gene and wild-type control Drosophila containing the mre11 gene; f is a bar chart of the number of sleeps in RNAi Drosophila with the mre11 gene and wild-type control Drosophila containing the mre11 gene; g is... Bar chart showing the sleep latency time of RNAi flies containing the mre11 gene and wild-type control flies containing the mre11 gene; bar chart showing the total daily sleep duration of RNAi flies containing the mre11 gene and wild-type control flies containing the mre11 gene; bar chart showing the total daily social interaction time of RNAi flies containing the mre11 gene and wild-type control flies containing the mre11 gene; bar chart showing the number of daily social interactions of RNAi flies containing the mre11 gene and wild-type control flies containing the mre11 gene; bar chart showing the mre11 gene... A bar chart showing the total daily social interaction time of RNAi fruit flies and wild-type control flies containing the mre11 gene; a bar chart showing the daily social interaction frequency of RNAi fruit flies with the mre11 gene and wild-type control flies containing the mre11 gene; a graph showing the performance index of RNAi fruit flies with the mre11 gene and wild-type control flies containing the mre11 gene in the fruit fly stress odor (dSO) test; and a graph showing the preference index of RNAi fruit flies with the mre11 gene and wild-type control flies containing the mre11 gene for the 3-octanol (OCT) response. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0032] Example 1

[0033] A method for detecting fruit fly behavior, such as Figure 1 As shown, it includes:

[0034] The fruit flies to be tested were placed in a multi-well culture plate. After the fruit flies adapted to the culture plate environment, the fruit flies in each well of the multi-well culture plate were filmed from above to obtain a high-throughput fruit fly behavioral video file. The time period of video filming was determined based on the behavioral information of the fruit flies to be tested.

[0035] The high-throughput Drosophila behavioral video files were processed into frame-by-frame images, and the coordinates of each Drosophila in each well of the multi-well culture plate at each frame were extracted from them.

[0036] Based on the preset behavioral features corresponding to the behavior information of the fruit fly to be tested, and based on the coordinates of each fruit fly in each well of the multi-well culture plate at each frame, the behavior information of the fruit fly to be tested is determined, thereby realizing the detection of fruit fly behavior.

[0037] Traditional methods for detecting fruit fly behavior are either limited or affected by unnatural conditions, or require experienced laboratory scientists to spend a lot of time identifying fruit fly behavior, making it difficult to conduct high-throughput, large-scale detection. The method proposed in this invention, for example, uses a four-layer incubator, with eight twelve-well culture plates in each layer, and can monitor 96 wells simultaneously, enabling the detection of a large number of fruit flies at once. Therefore, the method in this embodiment can achieve large-scale detection of fruit fly behavior.

[0038] Male fruit flies aged 3 to 5 days can be placed in a culture plate for 1.5 to 2 days to acclimatize before video recording. The culture medium in the plate can consist of 2% agar, 5% sucrose, and 0.1% propionic acid. Each well has a diameter of 35 mm and a height of 5 mm, and is covered with a 0.1 mm thick transparent glass slide (35 mm × 34 mm). Approximately 2 mm of space is left to allow the fruit flies to crawl freely (this space meets the basic locomotion requirements of fruit flies).

[0039] When the behavior of the fruit flies being tested includes sleep, the culture conditions of the above-mentioned multi-well culture plate need to include 12 hours of light and 12 hours of darkness to simulate the day-night cycle.

[0040] As a preferred embodiment, after extracting the coordinates of each fruit fly in each well of the multi-well culture plate at each frame, the method further includes:

[0041] Each fruit fly is numbered, and its coordinates in each frame are arranged and organized in chronological order to form a coordinate specification file. When determining the behavior information of the fruit fly to be tested, the coordinate specification file is used as the basis, which improves the detection efficiency.

[0042] In this embodiment, a fruit fly trajectory tracking device is first constructed. An infrared camera is added to a conventional biochemical incubator, and a high-throughput fruit fly culture and detection system is achieved by accommodating eight 12-well culture plates in each of a four-layer incubator. Male Drosophila melanogaster of a specific genotype, aged 3 to 5 days, are selected and placed in the 12-well culture plates for 1.5 to 2 days to acclimatize. Then, raw behavioral video files of the fruit flies are captured, lasting approximately 24.5 hours. After obtaining the behavioral video file, image processing techniques were used to convert the video into frame-by-frame image data. Based on the center coordinates of the individual wells in the twelve-well culture plate, the complete image was segmented into twelve individual culture dish images. After grayscale conversion, binarization, and noise reduction, the contours in the images were extracted. Considering the size of the fruit flies, the fruit fly contour was limited to 6 to 10 pixels wide and 26 to 50 pixels long, and the center point of the fruit fly contour was defined as the fruit fly's position, thus obtaining the fruit fly's coordinate information. The distance between each coordinate in adjacent frames was calculated, and the closest coordinate was considered to be the position of the same fruit fly in the adjacent frame. Missing or falsely detected coordinates were added or removed based on the number of coordinates in the previous frame. Finally, the coordinate information was converted into behavioral feature information according to the behavioral definitions given by biologists.

[0043] As a preferred implementation, when the fruit fly behavior information to be tested includes fruit fly sleep-related information, the preset behavioral features corresponding to the fruit fly sleep-related information are:

[0044] If the distance between two consecutive frames is less than a preset pixel, the fly is considered to be in a stationary state; if the stationary state of a fly continues for more than a preset duration, it is considered to be in a sleeping state. The preset pixel and the preset duration are determined by observing the flies in the multi-well culture plate in advance.

[0045] Information related to fruit fly sleep includes, for example, the total duration of sleep, the number of sleep segments, the duration of sleep per half hour, and the time when fruit flies first enter sleep after entering darkness 12 hours later.

[0046] As a preferred implementation, when the fruit fly behavior information to be tested includes fruit fly social information, the preset behavioral features corresponding to the fruit fly social information are:

[0047] When the distance between two fruit flies is less than a preset number of pixels, it is considered that social behavior has occurred between the two fruit flies. Each social behavior is recorded starting from when the distance between the two fruit flies is less than the preset number of pixels, until the distance between the two fruit flies exceeds the preset number of pixels. The preset number of pixels is determined in advance by observing the fruit flies in the multi-well culture plate. In this embodiment, the preset number of pixels is set to 40.

[0048] Information related to fruit fly social interaction could include the total duration of social interaction, the average number of social interactions per pair, and the average number of social interactions per fly.

[0049] As a preferred implementation, when the fruit fly behavior information to be tested includes fruit fly movement-related information, the preset behavioral feature corresponding to the fruit fly movement-related information is: the pixel distance the fruit fly moves per unit time.

[0050] To better illustrate the present invention, the following examples are provided:

[0051] Example 1: Detecting behavioral information in fruit flies and conducting large-scale screening for genes related to sleep, movement, and social behavior in fruit flies, such as... Figure 1 and Figure 2 As shown, it includes the following steps:

[0052] First, a Drosophila trajectory tracking device was constructed. An infrared camera was added to a conventional biochemical incubator, and a high-throughput Drosophila culture and detection system was achieved by accommodating eight 12-well culture plates in each of a four-layer incubator. Using the uas-GAL4 system combined with RNA interference technology, Drosophila strains with 5588 genes selectively knocked out in all neurons were constructed, resulting in a total of 6885 RNAi lines or mutant lines. Male Drosophila melanogaster of all strains, aged 3 to 5 days, were monitored for at least 24 hours in the constructed Drosophila trajectory tracking device under 25°C, 12 hours of light, and 12 hours of darkness.

[0053] The detection video is processed. The specific image processing steps for the behavioral video used in this example are as follows:

[0054] (1) Use the cv2.VideoCapture() function to read the video file frame by frame, process each frame of the video in a loop, and use the cv2.resize() function to uniformly scale the images to a standard aspect ratio of 430 pixels * 430 pixels.

[0055] (2) Use the cv2.cvtColor() function to convert the image to grayscale. The specific parameter used is code = cv2.COLOR_BGR2GRAY;

[0056] (3) Average the grayscale image to reduce any noise that may be present in the image;

[0057] (4) Use the cv2.adaptiveThreshold() function to perform adaptive binarization on the grayscale image. The specific parameters used are maxValue = 255, adaptiveMethod = cv2.ADAPTIVE_THRESH_MEAN_C, thresholdType = cv2.THRESH_BINARY_INV;

[0058] (5) Use the cv2.findContours() function to extract the outline of the fruit fly from the binarized image. The specific parameters used are mode = cv2.RETR_EXTERNAL, method = cv2.CHAIN_APPROX_SIMPLE;

[0059] (6) Considering the size of the fruit fly, the outline of the fruit fly is limited to 6 to 10 pixels wide and 26 to 50 pixels long, and the center point of the outline of the fruit fly is defined as the position of the fruit fly, so as to transform the outline of the fruit fly into the coordinates of the fruit fly.

[0060] (7) Observe whether there are obvious dead fruit flies or stains in the fruit fly video file, delete them from the extracted fruit fly coordinates, and at the same time, correct the number of fruit fly coordinates appearing in the current frame according to the number of fruit fly coordinates and the fruit fly sorting, and sort the coordinate positions of each fruit fly appearing in the current frame.

[0061] After organizing the coordinates, further statistical analysis was conducted on the total sleep duration (sleep characteristics), total movement distance (movement characteristics), and total social duration (social characteristics) of Drosophila. Thresholds were set at the mean minus two standard deviations, 1.5 times the mean, and 0.5 times the mean, respectively. From 6885 Drosophila systems, 285 genes leading to sleep deprivation were identified, of which 20 are known to be involved in Drosophila sleep regulation; 310 genes significantly increasing movement distance by 50%, including 29 whose involvement in Drosophila movement behavior is supported by existing literature; and 359 genes reducing social behavior by 50%, of which 4 are known. These findings provide preliminary results for subsequent experiments and reduce the time required for experimentalists to establish large-scale screening systems.

[0062] Example 2: This example verifies the effect of the mre11 gene on the sleep characteristics of fruit flies, such as... Figure 3As shown in the figure. It should be noted that a total of five different types of fruit flies were used in the experiment: a blank control group (wild-type fruit flies containing the mre11 gene); RNAi control group 1 (fruit flies treated with RNAi technology but without mre11 knockdown); mre11 RNAi experimental group 1 (fruit flies with mre11 gene expression knocked down using RNAi technology); RNAi control group 2 (fruit flies treated with another RNAi technology but without mre11 knockdown); and mre11 RNAi experimental group 2 (fruit flies with mre11 gene expression knocked down using another RNAi technology). In the figure, 'a' is a line graph comparing the sleep characteristics of mre11 gene RNAi fruit flies and wild-type control fruit flies containing the mre11 gene. Five types of fruit flies were used, with five flies of each type. The gray shaded area represents the line graph showing the change in sleep duration during the dark period. Figure 3 b in Figure 3 The bar chart shows the total daily sleep duration of RNAi Drosophila with the mre11 gene in (a) and wild-type Drosophila control group containing the mre11 gene. Five Drosophila species were used, with five of each species. This chart is used to jointly demonstrate with chart a that the mre11 gene affects the sleep of Drosophila in a colony state, and that a decrease in the expression level of the mre11 gene reduces the sleep time of Drosophila. Figure 3 c in the figure is a bar chart of the daily sleep time of RNAi fruit flies with the mre11 gene and wild-type control fruit flies containing the mre11 gene under isolation conditions of 1 fly per well, monitored using the device of the present invention. The above five types of fruit flies were used, with 1 fly of each type. Figure 3 Figure d is a bar chart of the daily sleep time of RNAi fruit flies with the mre11 gene and wild-type control fruit flies containing the mre11 gene, monitored by the traditional one-dimensional fruit fly behavioral detection device DAM, under isolation conditions with one fruit fly per well. The above five types of fruit flies were used, with one fruit fly of each type. This figure is used to jointly demonstrate with figure c that, regardless of whether the method of the present invention or the traditional one-dimensional method is used, it can be proved that the reduced expression level of the mre11 gene in the isolated state of fruit flies has no effect on the sleep of fruit flies. Figure 3 The "e" in the figure is a bar chart showing the average sleep duration per episode in RNAi fruit flies containing the mre11 gene and wild-type control fruit flies containing the mre11 gene. Five fruit fly species were used, with five of each species. Figure 3 f in the figure is a bar chart of the number of sleep cycles in RNAi fruit flies containing the mre11 gene and wild-type control fruit flies containing the mre11 gene. Five fruit flies of the above five types were used, with five of each type. Figure 3In the figure, g is a bar chart of the sleep latency time of RNAi fruit flies containing the mre11 gene and wild-type control fruit flies containing the mre11 gene. Five fruit flies of the above five species were used, with five of each species. This figure is used to jointly demonstrate with figures e and f that when fruit flies are in a colony, the expression level of the mre11 gene decreases, which leads to a decrease in the duration and number of sleeps. At the same time, the sleep latency of fruit flies is prolonged when the lights are turned off. Figure 3 The 'h' in the figure is a bar chart of the total daily sleep duration of RNAi fruit flies with the mre11 gene and wild-type control fruit flies containing the mre11 gene. The above five types of fruit flies were used, with two of each type. This chart was used to investigate the minimum number of fruit flies that the population has an impact on fruit fly sleep. It was found that the presence of two fruit flies can affect fruit fly sleep. Figure 3 In the bar chart, 'i' represents the total daily social time of RNAi fruit flies with the mre11 gene and wild-type control fruit flies containing the mre11 gene, with 5 fruit flies of each type. Figure 3 In the figure, j is a bar chart of the daily social interactions of RNAi fruit flies with the mre11 gene and wild-type control fruit flies containing the mre11 gene. Five fruit flies of the above five species were used, with five of each species. Figure 3 In the figure, k is a bar chart of the total daily social time of RNAi fruit flies with the mre11 gene and wild-type control fruit flies containing the mre11 gene. The above five types of fruit flies were used, with two fruit flies of each type. Figure 3 The bar chart in 'l' represents the daily social interaction frequency of RNAi fruit flies containing the mre11 gene and wild-type control fruit flies containing the mre11 gene. Five fruit fly species were used, with two of each species. This chart, along with the i, j, and k charts, is used to investigate whether social behavior affects fruit fly sleep. Figure 3 In the test, m represents the performance index of RNAi fruit flies with the mre11 gene and wild-type control fruit flies containing the mre11 gene in the fruit fly stress odor (dSO) test. Five fruit flies of the above five species were used, with five of each species. Figure 3In the figure, 'n' represents the preference index of the RNAi fruit flies containing the mre11 gene and the control wild-type fruit flies containing the mre11 gene to the response to 3-octanol (OCT). The higher the preference index, the greater the avoidance of OCT. Five fruit flies of the above five species were used, with five flies of each species. This figure, along with figure m, is used to explore whether the possible behavioral mechanism by which the mre11 gene affects sleep is related to odor and the response to odor. The comparison of the behavioral characteristics of fruit flies in each figure in Figures a-n was performed using one-way ANOVA. * represents a statistically significant p-value < 0.05; ** represents a statistically significant p-value < 0.01; *** represents a statistically significant p-value < 0.001; **** represents a statistically significant p-value < 0.0001; ns represents a statistically significant p-value that is not significant; error bars refer to the standard error of the mean (SEM), i.e., the standard deviation of the estimated sample mean.

[0063] The mre11 (meiotic recombination 11) gene is known to be involved in DNA damage repair and telomere protection, but there is currently no literature reporting its relationship with sleep regulation. For example... Figure 3 As shown in Figures ab, it can be seen that the mre11 gene affects the sleep of fruit flies in a colony state, and that decreased expression of the mre11 gene reduces the sleep time of fruit flies. Figure 3 As shown in the diagram, it can be seen that whether using the method proposed in this invention or the traditional one-dimensional method, it can be demonstrated that the reduced expression level of the mre11 gene in isolated Drosophila treatment has no effect on Drosophila sleep. Figure 3 As shown in the example, in a colony setting, decreased expression of the mre11 gene leads to a reduction in both sleep duration and number of sleep cycles, while sleep latency is prolonged when lights are off. These results indicate that a lack of the mre11 gene impairs the initiation and maintenance of sleep in fruit flies. This example also explored the minimum number of individuals required to observe a reduction in induced sleep under colony conditions, finding that two fruit flies are sufficient. Figure 3 As shown in h. Figure 3 As shown in the diagram, social characteristic analysis in RNAi Drosophila with the mre11 gene and wild-type control flies containing the mre11 gene revealed that the reduced sleep in RNAi Drosophila with the mre11 gene was not caused by increased social activity, as both the duration and frequency of social interactions were reduced in RNAi Drosophila with the mre11 gene. However, due to impaired avoidance of stress odors, RNAi Drosophila with the mre11 gene exhibited a severe deficiency in stress odor responses, while the ability to release stress odors appeared intact, reflecting sensory deficits in the processing of olfactory social cues, such as... Figure 3As shown in m. Furthermore, wild-type fruit flies in the control group containing the mre11 gene exhibited an avoidance of unpleasant odors (3-octanol was used in this example), while in RNAi fruit flies containing the mre11 gene, this behavioral response was significantly weakened, such as... Figure 3 As shown in n.

[0064] In summary, these results indicate that the absence of the mre11 gene leads to olfactory defects. It is possible that, when different genotypes exist, this altered olfly sense enhances certain socially relevant signals that strongly suppress sleep in RNAi Drosophila of the mre11 gene.

[0065] Based on the two examples above, it is demonstrated that the method of the present invention can detect the behavioral characteristics of fruit flies at high throughput and large scale, and therefore has important application value in the field of biological research.

[0066] The above embodiments use Drosophila melanogaster as an example. In addition to Drosophila melanogaster, the method of the present invention is also applicable to other fruit flies. The fruit fly trajectory tracking device (including biochemical incubator and infrared camera) can also be flexibly adjusted and pre-selected according to the actual situation.

[0067] Implementation 2

[0068] An apparatus for detecting fruit fly behavior, used to perform a method for detecting fruit fly behavior as described in Embodiment 1 above, includes: a conventional biochemical incubator, an infrared camera, video capture software, and a processor; an infrared camera is installed on the upper part of each layer of multi-well culture plate in the conventional biochemical incubator for capturing video of fruit flies in each well of that layer of multi-well culture plate; the video capture software is used to control the infrared camera to perform video capture for a specific period based on the behavior information of the fruit flies to be tested, and to receive and store high-throughput fruit fly behavioral video files captured by the infrared camera; the processor is used to process the high-throughput fruit fly behavioral video files to obtain fruit fly behavior information.

[0069] The relevant technical solutions are the same as in Embodiment 1, and will not be repeated here.

[0070] Example 3

[0071] A method for screening genes that regulate the behavior of fruit flies involves selecting genes with a relatively large degree of variation relative to the overall population from all genes to be screened based on fruit fly behavioral information, thus completing the screening process; wherein the aforementioned fruit fly behavioral information is obtained through a method for detecting fruit fly behavior as described above.

[0072] The relevant technical solutions are the same as in Embodiment 1, and will not be repeated here.

[0073] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting fruit fly behavior, characterized in that, include: The fruit flies to be tested were placed in a multi-well culture plate. After the fruit flies adapted to the culture plate environment, the fruit flies in each well of the multi-well culture plate were filmed from above to obtain a high-throughput fruit fly behavioral video file. The time period of video filming was determined based on the behavioral information of the fruit flies to be tested. The high-throughput Drosophila behavioral video file was processed into frame-by-frame images, and the coordinates of each Drosophila in each well of the multi-well culture plate at each frame were extracted from them. Based on the preset behavioral features corresponding to the behavioral information of the fruit fly to be tested, and based on the coordinates of each fruit fly in each well of the multi-well culture plate at each frame, the behavioral information of the fruit fly to be tested is determined, thereby realizing the detection of fruit fly behavior. After obtaining the behavioral video file, image processing technology is used to process the video into frame-by-frame image data. Based on the center coordinates of the single wells in the multi-well culture plate, the complete image is segmented into multiple single-well culture dish images. After grayscale conversion, binarization, and noise reduction, the contours in the images are extracted. The center point of the fruit fly contour is defined as the fruit fly position, thereby obtaining the fruit fly's coordinate information. The distance between each coordinate in adjacent frames is calculated, and the closest coordinate is considered to be the position of the same fruit fly in the adjacent frame. The missing or falsely detected coordinates are added, deleted, or corrected based on the number of coordinates in the previous frame. When the behavioral information of the fruit flies to be tested includes fruit fly sleep-related information, the preset behavioral features corresponding to the fruit fly sleep-related information are: If the distance between two consecutive frames is less than a preset pixel, the fly is considered to be in a stationary state; if the stationary state of a fly lasts for more than a preset duration, it is considered to be in a sleeping state; wherein, the preset pixel and the preset duration are determined in advance by observing the flies in the multi-well culture plate. When the behavioral information of the fruit fly to be tested includes social information related to the fruit fly, the preset behavioral features corresponding to the social information related to the fruit fly are: When the distance between two fruit flies is less than a preset number of pixels, it is considered that social behavior has occurred between the two fruit flies; each social behavior is recorded starting from when the distance between the two fruit flies is less than the preset number of pixels, until the distance between the two fruit flies exceeds the preset number of pixels; wherein, the preset number of pixels is determined in advance by observing the fruit flies in the multi-well culture plate; When the fruit fly behavior information to be tested includes fruit fly movement-related information, the preset behavioral feature corresponding to the fruit fly movement-related information is: the pixel distance the fruit fly moves per unit time.

2. The method according to claim 1, characterized in that, When the behavior of the fruit flies being tested includes sleep, the culture conditions of the multi-well culture plate include 12 hours of light and 12 hours of darkness.

3. The method according to claim 1, characterized in that, After extracting the coordinates of each fruit fly in each well of the multi-well culture plate at each frame, the method also includes: Each fruit fly is numbered, and its coordinates in each frame are arranged and organized in chronological order to form a coordinate specification file; The determination of the behavior information of the fruit fly to be tested is based on the coordinate specification file.

4. The method according to claim 1, characterized in that, The preset number of pixels is 40.

5. A device for detecting fruit fly behavior, characterized in that, A method for performing a method for detecting fruit fly behavior as described in any one of claims 1 to 4 includes: a conventional biochemical incubator, an infrared camera, video capture software, and a processor; An infrared camera is installed on the upper part of each multi-well culture plate in the conventional biochemical incubator to capture video of fruit flies in each well of the multi-well culture plate. The video capture software is used to control the infrared camera to perform video capture according to the behavior information of the fruit flies to be tested, and to receive and store the high-throughput fruit fly behavioral video files captured by the infrared camera. The processor is used to process the high-throughput fruit fly behavioral video files to obtain fruit fly behavior information.

6. A method for screening genes regulating Drosophila behavior, characterized in that, Based on fruit fly behavioral information, genes with a relatively large degree of variation relative to the overall variation are selected from all genes to be screened as potential behavioral regulatory genes, thus completing the screening; wherein, the fruit fly behavioral information is obtained by a method for detecting fruit fly behavior as described in any one of claims 1 to 4.

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