We acknowledge the Vienna Drosophila Resource Center and the Bloomington Drosophila stock center (NIH P40OD018537)for providing Drosophila strains. We are grateful to the Kl&#228;mbt laboratory for introducing us to the island assay and to Martijn Eidhof for building the island assay setup. This study was in part supported by the E-RARE-3 Joint Transnational Call grant &#34;Preparing therapies for autosomal recessive ataxias&#34; PREPARE (NWO 9003037604), by a TOP grant (912-12-109) from the Netherlands Organization for Scientific Research (NWO), and by two DCN/Radboud University Medical Center PhD fellowships. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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	<li>Smeets, C. J., Verbeek, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cerebellar+ataxia+and+functional+genomics:+Identifying+the+routes+to+cerebellar+neurodegeneration.">Cerebellar ataxia and functional genomics: Identifying the routes to cerebellar neurodegeneration.</a> Biochim Biophys Acta. 1842, 2030-2038 (2014).</li><li>Liu, Y. T., Lee, Y. C., Soong, B. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+we+have+learned+from+the+next-generation+sequencing:+Contributions+to+the+genetic+diagnoses+and+understanding+of+pathomechanisms+of+neurodegenerative+diseases.">What we have learned from the next-generation sequencing: Contributions to the genetic diagnoses and understanding of pathomechanisms of neurodegenerative diseases.</a> J Neurogenet. 29, 103-112 (2015).</li><li>He, J., Mangelsdorf, M., Fan, D., Bartlett, P., Brown, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amyotrophic+Lateral+Sclerosis+Genetic+Studies:+From+Genome-wide+Association+Mapping+to+Genome+Sequencing.">Amyotrophic Lateral Sclerosis Genetic Studies: From Genome-wide Association Mapping to Genome Sequencing.</a> Neuroscientist. 21, 599-615 (2015).</li><li>Lill, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetics+of+Parkinson's+disease.">Genetics of Parkinson's disease.</a> Mol Cell Probes. 30, 386-396 (2016).</li><li>Bjedov, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanisms+of+life+span+extension+by+rapamycin+in+the+fruit+fly+Drosophila+melanogaster.">Mechanisms of life span extension by rapamycin in the fruit fly Drosophila melanogaster.</a> Cell Metab. 11, 35-46 (2010).</li><li>Hada, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=D-chiro-inositol+and+pinitol+extend+the+life+span+of+Drosophila+melanogaster.">D-chiro-inositol and pinitol extend the life span of Drosophila melanogaster.</a> J Gerontol A Biol Sci Med Sci. 68, 226-234 (2013).</li><li>Schmidt, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinesin+heavy+chain+function+in+Drosophila+glial+cells+controls+neuronal+activity.">Kinesin heavy chain function in Drosophila glial cells controls neuronal activity.</a> J Neurosci. 32, 7466-7476 (2012).</li><li>Kochinke, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+Phenomics+Analysis+Deconvolutes+Genes+Mutated+in+Intellectual+Disability+into+Biologically+Coherent+Modules.">Systematic Phenomics Analysis Deconvolutes Genes Mutated in Intellectual Disability into Biologically Coherent Modules.</a> Am J Hum Genet. 98, 149-164 (2016).</li><li>Volkenhoff, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glial+Glycolysis+Is+Essential+for+Neuronal+Survival+in+Drosophila.">Glial Glycolysis Is Essential for Neuronal Survival in Drosophila.</a> Cell Metab. 22, 437-447 (2015).</li><li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nat Methods. 9, 676-682 (2012).</li><li>R Development Core Team. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> R: A language and environment for statistical computing. , (2008).</li><li>Greenspan, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Fly pushing: the theory and practice of Drosophila genetics. , (2004).</li><li>Rothblum-Oviatt, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ataxia+telangiectasia:+a+review.">Ataxia telangiectasia: a review.</a> Orphanet J Rare Dis. 11, 159 (2016).</li><li>Petersen, A. J., Rimkus, S. A., Wassarman, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ATM+kinase+inhibition+in+glial+cells+activates+the+innate+immune+response+and+causes+neurodegeneration+in+Drosophila.">ATM kinase inhibition in glial cells activates the innate immune response and causes neurodegeneration in Drosophila.</a> Proc Natl Acad Sci U S A. 109, E656-E664 (2012).</li><li>Gargano, J. W., Martin, I., Bhandari, P., Grotewiel, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+iterative+negative+geotaxis+(RING):+a+new+method+for+assessing+age-related+locomotor+decline+in+Drosophila.">Rapid iterative negative geotaxis (RING): a new method for assessing age-related locomotor decline in Drosophila.</a> Exp Gerontol. 40, 386-395 (2005).</li><li>. ImageJ User Guide - IJ 1.46 Available from: <a target="_blank" href="https://imagej.nih.gov/ij/docs/guide/146-30.html">https://imagej.nih.gov/ij/docs/guide/146-30.html</a> (2012)</li></ol>

The authors have no conflicts of interest to disclose.

This protocol describes the &#34;Drosophila Island Assay&#34; macro that quantitatively assesses Drosophila motor behavior during the island assay. The macro accurately counts the flies on the platform over time, making the island assay highly sensitive and suitable for quantitative high-throughput evaluation of locomotor defects. The methodology allows for the comparison of any condition, with flies grown under different genetic and/or environmental conditions, including drug exposure. This readout is thus particularly useful as discovery tool when performing large genetic or pharmaceutical screens, when studying Drosophila models of movement disorders and other neurological diseases, or when examining locomotion or flight behavior.
The island assay protocol presented in this manuscript provides advantages over existing/alternative methods. For example, video-tracking locomotion is much more time-consuming and less suitable for testing large sample sizes. The island assay is a high-throughput screening tool, and, in this sense, is comparable to the rapid interactive negative geotaxis (RING) assay16. The difference between the two is that the island assays allows for the detection of a broader range of locomotor problems; the inability of flies to leave the platform can be caused by defects in flight, jumping, or walking behavior caused by wing (muscle/neuronal) and/or leg (muscle/neuronal) defects. On the other hand, the RING assay assesses defects in climbing/walking behavior caused by leg (muscle/neuronal) defects. In case users are interested in multiple behavioral readouts, the island assay can also easily be combined with other assays, such as the RING assay. In addition, lasers required for optogenetics can easily be installed in the island assay box, and the setup is so simple that it can easily be moved to a room where temperature and light can be controlled.
To ensure the success and reproducibility of the island assay described here, several recommendations should be followed. Aliquot and transfer the flies to the experimental test vials at least one day before the experiment to avoid the effects of CO2 or cold anesthesia. Do not overcrowd the experimental vials (use 10 - 15 flies per vial; it is best to always place the same number of flies per vial). Keep the flies on fresh food at all times. If not yet familiar with conducting the assay, practice throwing flies onto the platform to maximize the yield. Also practice quickly retracting the hand right afterwards, as it interferes with the data analysis (image analysis and fly counting start only after the hand is out of the picture). Keep the environmental and experimental conditions identical in experiments that need to be compared (e.g., controls versus mutants or a genotype tested at different ages). Always perform the experiments at the same time of the day and maintain the vials under controlled temperature and humidity conditions. For statistical power, test at least three technical replicates per biological replicate.
To ensure the successful performance of the macro described here, webcam and image settings must be adjusted to achieve maximal contrast: flies appearing as black objects on a white platform. When the number of flies is not properly counted by the macro, adjust the contrast settings, check whether the ROI is properly selected, and ensure that the size of the flies on the platform is above the specified minimum fly size setting (see step 8.3 of this protocol). The settings only need to be defined once. They are applicable to all experiments, as long as the distance between the webcam and the platform is not changed. The Circularity_min and max settings define the circularity of the particles (particles = counted flies) that will be taken into account for the analysis (flies = counted objects). 1 represents a perfect circle, and 0 represents a line17. Since the flies always present a certain degree of circularity (a fly cannot appear as a straight line), the &#34;Circularity_max&#34; setting is set at 1 and the &#34;Circularity_min&#34; setting is set at 0.4. It is unlikely that the user needs to adjust these settings.
The macro occasionally makes counting mistakes when a fly is located close to the border of the platform. This can occur if the flies cannot fly but walk in and out of the user-defined ROI. In most cases, reselecting the ROI (fitting it as much as possible to the platform) can easily solve this issue. However, the &#34;Island Assay Analysis&#34; script is able to detect and correct improper data counts caused by flies walking in and out of the ROI relatively well. Although the resolution of the webcam presented here is high enough to discriminate flies in close proximity fairly well, we have implemented additional algorithms in the image processing procedure of the &#34;Drosophila Island Assay&#34; macro, such as the watershed and erode function17. These facilitate the proper delineation of flies that are in close proximity on the platform. In addition, the macro is unable to distinguish between flies that jumped from the platform or flew away from it. Nonetheless, it is generally observed that healthy young fly strains fly away immediately when dropped onto the platform, whereas older flies and flies with locomotor deficits remain longer on the platform and will eventually jump or fall off the platform. Despite these limitations, the assay and analysis provide a very accurate measure of locomotor behavior.
To ensure the successful performance of the &#34;Island Assay Analysis&#34; script, the user has to make sure to enter the correct paths for the input and output files in the script rows indicated in the protocol and to provide the data in the correct folder format (as indicated in Figure 2). If the user finds the criteria used to filter out unreliable experimental data too stringent (row 68: the first value in the &#34;Count&#34; column is less than or equal to 5; row 71: the first value in the &#34;Count&#34; column is higher than the total number of flies thrown on the platform +3), turn off these filter settings by adding a # in front of the text in rows 68 and 71 in the &#34;Island Assay Analysis&#34; script. In this case, all datasets will be included in the analysis. Alternatively, filter settings can be changed by adjusting the values in rows 68 and 71 according to user needs. Possible artifacts in the count values in the &#34;results.txt&#34; generated by the &#34;Drosophila Island Assay&#34; macro can also be manually adjusted, and the script can be re-run on the adjusted data. When the user is interested in processing more than 10 fps, or more than 10 s of data, the number of frames processed by the &#34;Island Assay Analysis&#34; script should be adjusted. The statistical analysis can also be replaced by user-defined alternatives.
A folder called &#34;Examples Island Assay,&#34; containing examples with image time-series obtained using the island assay, can be found at the following website: https://doi.org/10.6084/m9.figshare.4309652.v1. Download the &#8220;Examples Island Assay&#8221; folder and follow the steps described in this protocol to quickly become familiarized with the structure of file storage, the processing of the images with the &#8220;Drosophila Island assay&#8221; macro, and the &#8220;Island Assay Analysis&#8221; script.
The island assay, in combination with the developed macro and analysis script, can be used to evaluate and quantify the aberrant movement behavior of a Drosophila model of Ataxia-Telangiectasia. Since the assay can be efficiently applied to various ages, it is well suited to analyzing the potentially progressive nature of phenotypes.
In summary, the island assay, in combination with the &#34;Drosophila Island Assay&#34; macro and the &#34;Island Assay Analysis&#34; script, is a cost-effective, reliable, and highly efficient assay to objectively analyze and quantify locomotor defects of Drosophila models of movement disorders in a high-throughput manner.

In recent years, advances in next-generation sequencing technologies have greatly contributed to the identification of genes underlying degenerative movement disorders of the brain (e.g., cerebellar ataxia and Parkinson&#39;s), of peripheral neuronal origin (e.g., amyotrophic lateral sclerosis and hereditary spastic paraplegia), and of muscular origin (e.g., Duchenne muscular dystrophy and myotonic dystrophy)1,2,3,4. Despite this, little is known about the molecular mechanisms that underlie most of these disorders. A better understanding of these mechanisms is essential to developing therapies.
As in humans, movement in model organisms, such as flight and locomotion in Drosophila, is controlled by the central brain, peripheral nervous system, and muscles. In addition, the fast generation time and genetic toolbox of Drosophila make this model organism particularly suitable for the high-throughput screening of genes involved in movement disorders and for drug testing5,6. Due to the substantial number of conditions that need to be tested in such screens, reliable, cost-effective, and relative simple assays, as well as tools to quantify the output results in an automated manner, are highly desirable.
Schmidt et al. (2012)7 described a low-cost test called the &#34;island assay&#34; to evaluate Drosophila locomotor behavior. The island assay has been used successfully in large-scale screenings to identify genes with glia-specific functions7, in the evaluation of Drosophila models of intellectual disability8, and for the general evaluation of fly motor behavior9. The principle design of the island assay consists of an elevated platform onto which several flies are thrown. This induces an innate motor behavior that enables healthy flies to escape from the platform within seconds. The assay measures the number of flies remaining on the platform over time7,8,9. All these features indicate that the island assay may be a powerful screening tool for genes involved in movement disorders.
Currently, the quantitative analysis of filmed island assay data is done manually7,8,9. To improve the efficiency of the assay, a low-cost method was developed to semi-automatically quantify the escape of flies from the platform. The setup uses a simple webcam connected to a laptop to collect image time series of the platform, with frames acquired every 0.1 s. Frames are then processed with the &#34;Drosophila Island Assay&#34; macro that quantifies the number of flies on the platform over time. The &#34;Drosophila Island Assay&#34; macro is divided into three independent sub-macros: (I) The &#34;Create stack and projection,&#34; sub-macro identifies different island experiments stored in different subfolders and creates a stack and a projection of each time series. (II) The &#34;Define Platform&#34; sub-macro will consecutively open all &#34;Projection_image_name.tif&#34; files located in the individual experimental subfolders, at which point the user is requested to manually define the island platform as the region of interest (ROI). (III) &#34;Analysis&#34; automatically quantifies the number of flies remaining on the platform during the time series. The sub-macros can be run consecutively (in one run) or independently. For statistical data analysis, a script was developed to automatically process the obtained data and to apply a statistical test to determine whether treatments/genotypes behave significantly different&#160;from each other (Figure 1). Finally, it is demonstrated that this setup can be used to evaluate and quantify the aberrant locomotor ability of a Drosophila model for ataxia-telangiectasia (AT).

<table>
	<tbody>
		<tr>
			<td class="xl19">25 x 95 mm Drosophila vials</td>
			<td class="xl19">Flystuff</td>
			<td class="xl20">32-116SB</td>
			<td class="xl19">-</td>
		</tr>
		<tr>
			<td class="xl21">Logitech C525 HD Webcam</td>
			<td class="xl19">Logitech</td>
			<td class="xl19">-</td>
			<td class="xl19">Any webcam with USB connection is suitable.</td>
		</tr>
		<tr>
			<td class="xl19">Stand to hold webcam</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
		</tr>
		<tr>
			<td class="xl19">Lamp</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">12 V LED lights are appropriate</td>
		</tr>
		<tr>
			<td class="xl19">Pounding pad</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">Any mouse pad works</td>
		</tr>
		<tr>
			<td class="xl19">Island Assay box</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">Dimensions 40x35x2.5 cm. Hole 20x30 cm. Transparent.</td>
		</tr>
		<tr>
			<td class="xl19">Island Assay bath</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">Dimensions 42x38x25 cm. Non white.</td>
		</tr>
		<tr>
			<td class="xl19">Island/platform</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">Dimensions 42x38x25 cm. Uniform white.</td>
		</tr>
		<tr>
			<td class="xl19">Soap</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">Standard dishwashing detergent is suitable.</td>
		</tr>
		<tr>
			<td class="xl19">Computer</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">Scripts run both on Windows and Mac</td>
		</tr>
		<tr>
			<td class="xl22">Image-recording software: HandiAvi&#174;</td>
			<td class="xl23">AZcendant&#174;</td>
			<td class="xl23">-</td>
			<td class="xl19">HandyAvi is only compatible with Windows and has been described throughout the manuscript. It can be downloaded from: http://www.azcendant.com/DownloadHandyAvi.html (version 5.0)</td>
		</tr>
		<tr>
			<td class="xl22">Image-recording software: WebcamCapture</td>
			<td class="xl25">-</td>
			<td class="xl25">-</td>
			<td class="xl25">Fiji/ImageJ plugin that can be used on Mac alternative to HandyAvi for image-recordings and can be downloaded from: https://imagej.nih.gov/ij/plugins/webcam-capture/ When using this method, the user has to use the same folder setup and image-recording settings indicated in this manuscript, with the exception that for each experimental replicate, the captured image stack should be exported as Stack.tiff to the corresponding experimental replicate folder. Upon running the &#34;Drosophila Island Assay&#34; macro on this data, no text should be present in the &#34;First frame identifier&#34; setting.</td>
		</tr>
		<tr>
			<td class="xl19">Fiji</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl19">Version 1.4 or higher, can be downloaded from: https://figshare.com/s/def4197ee0010b21a76f</td>
		</tr>
		<tr>
			<td class="xl19">R studio</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl24">Can be downloaded from: https://www.rstudio.com/products/rstudio/download/</td>
		</tr>
		<tr>
			<td class="xl19">R</td>
			<td class="xl19">-</td>
			<td class="xl19">-</td>
			<td class="xl24">Version 3.3.2, can be downloaded from: https://cran.rstudio.com</td>
		</tr>
	</tbody>
</table>

high-throughput analysis of locomotor behavior in the drosophila island assay

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other neurological diseases at an increasing speed. However, little is known about the molecular mechanisms that underlie these disorders. The genetic, molecular, and behavioral toolbox of Drosophila melanogaster makes this model organism particularly useful to characterize new disease genes and mechanisms in a high-throughput manner. Nevertheless, high-throughput screens require efficient and reliable assays that, ideally, are cost-effective and allow for the automatized quantification of traits relevant to these disorders. The island assay is a cost-effective and easily set-up method to evaluate Drosophila locomotor behavior. In this assay, flies are thrown onto a platform from a fixed height. This induces an innate motor response that enables the flies to escape from the platform within seconds. At present, quantitative analyses of filmed island assays are done manually, which is a laborious undertaking, particularly when performing large screens.
This manuscript describes the &#34;Drosophila Island Assay&#34; and &#34;Island Assay Analysis&#34; algorithms for&#160;high-throughput, automated data processing and quantification of island assay data. In the setup, a simple webcam connected to a laptop collects an image series of the platform while the assay is performed. The &#34;Drosophila Island Assay&#34; algorithm developed for the open-source software Fiji processes these image series and quantifies, for each experimental condition, the number of flies on the platform over time. The &#34;Island Assay Analysis&#34; script, compatible with the free software R, was developed to automatically process the obtained data and to calculate whether treatments/genotypes are statistically different. This greatly improves the efficiency of the island assay and makes it a powerful readout for basic locomotion and flight behavior. It can thus be applied to large screens investigating fly locomotor ability, Drosophila models of movement disorders, and drug efficacy.

In the described protocol, Drosophila island assay data are acquired and processed in three steps. First, the flight escape response of Drosophila thrown onto the island platform is recorded with a webcam and stored as individual .bmp images (protocol sections 1 - 7). Second, the &#34;Drosophila Island Assay&#34; macro (step 8) processes the frames, generating a &#34;result.txt&#34; text file (Table 1), in which the number of objects detected in each frame is summarized, and an image stack &#34;Result_stack.tiff,&#34; which shows the objects detected within the area of the platform in each frame. Third, the &#34;Island Assay Analysis&#34; script (protocol section 9) processes the macro data stored in the &#34;results.txt&#34; files of individual experiments. Several steps are incorporated in the script to filter and combine the data for statistical analysis. The first frame in which the flies are thrown on the platform is detected and considered as time point 1. Earlier frames are eliminated from the dataset. 100 frames following time point 1 (corresponding to 10 s) are selected for analysis. Experiments in which the initial number of flies detected on the platform is smaller than 5 are automatically excluded from the analysis, eliminating unreliable results from underpowered experiments. Experiments in which the initial number of flies detected on the platform exceeds the &#34;Number of flies per vial&#34; setting plus a tolerance of 3 are also excluded. This eliminates datasets in which noise particles were erroneously detected as flies. The script then calculates the percentage of flies detected for each time point compared to the highest number of flies detected in the series. Errors in datasets caused by flies walking in and out of the ROI (detected as a decrease followed by an increase in the percentage of flies on the platform over time) are automatically corrected by regarding them as consistently present on the platform at earlier stages. All replicate datasets for a specific experimental condition that were present in the main directory are combined and exported to the &#34;data_all_conditions.csv&#34; file. Its columns represent the variables described in Table 2. The script will also export a line graph for each experimental condition, named according to the folder containing the data. This graph shows the percent of flies remaining on the platform over time (flight escape response) for the experimental replicates present in the respective folder (Figure 3A-B). The mean, SD, and SEM for each experimental condition are calculated and summarized in the &#34;Statistics summary.csv&#34; file. A line graph called &#34;Escape_response_all_conditions.tiff&#34; shows the average flight escape response for up to 12 experimental conditions present in the main folder (Figure 3C). Finally, the area under the curve for all experimental conditions present in the main folder is calculated and summarized in the &#34;AUC.csv&#34; file. Depending on the number of conditions present in the main folder, the script will either perform a two-tailed unpaired Welch t-test (2 conditions) or an ANOVA with Tukey correction for multiple testing (more than 2 conditions) to determine whether the experimental conditions differ significantly from each other. These results are summarized in &#34;Welch_t-test_results.txt&#34; or &#34;AUC_anova_results.txt.&#34; When performing the ANOVA, the script will also export an &#34;AUC_anova.tiff&#34; file that displays the difference in the mean AUC and the 95% confidence intervals of the experimental conditions that are compared. The values of the absolute area under the curve of the experimental replicates for all experimental conditions are displayed as individual data points with medians in &#34;AUC.tiff&#34; (Figure 3D).
Ataxia Telangiectasia (AT) is an autosomal recessive movement disorder characterized by early onset of cerebellar ataxia due to mutations in the Ataxia Telangiectasia Mutated (ATM) gene14. Mutants of the Drosophila orthologue of ATM, tefu, display defects in mobility and longevity15. To evaluate the &#34;Drosophila Island Assay&#34; macro, a Drosophila model of AT was tested in the island assay, and the data output of the macro was compared to manual data counts. The results show that ubiquitous tefu knockdown (w-; Actin-Gal4/GD11950) significantly decreases the ability of these flies to leave the platform compared to their genetic background controls (w-; Actin-Gal4/+) (Figure 4). After 1 s, 50% of the control flies had escaped the platform, in contrast to the &#60;1% of tefu-RNAi flies. Importantly, the data obtained with the macro faithfully reproduced the data obtained by manual counting, indicating that the macro is a reliable tool that can be used for the quantification of island assay data and the evaluation of movement defects (Figure 4 A-B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55892/55892fig1.jpg" /> 
Figure 1: Flowchart outlining the requirements, experimental procedure, and analysis of the island assay. (A) Island assay equipment. (B) Experimental setup for the island assay. (C) Island assay. (D) Processing of island assay data with the &#34;Drosophila Island Assay&#34; macro. The &#34;Drosophila Island Assay&#34; macro is composed of 3 sub-macros: 1) make stack and projection, 2) define platform, and 3) analysis. (E) Processing and statistical evaluation of data using the &#34;Island Assay Analysis&#34; script. <a href="https://cloudflare.jove.com/files/ftp_upload/55892/55892fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55892/55892fig2.jpg" /> 
Figure 2: Examples of different adjustments required during the protocol. (A) The required directory structure in which island assay experiments must be stored for data processing and analysis. (B) When adjusting the video settings, flies must appear black on a white background. (C) Image frame output files as saved by the image-recording software described in this manuscript. (D) The yellow outline shows the platform selection. The stored platform selection in the &#34;ROI Manager&#34; is highlighted in blue. (E) Flies are represented as white dots during the adjustment of the &#34;Minimum fly size setting.&#34; The results window shows the area of the flies in pixels. (F) Example of a single recorded image frame (on the left) and the corresponding frame in the resulting image stack, obtained with the &#34;Drosophila Island Assay&#34; macro (on the right). <a href="https://cloudflare.jove.com/files/ftp_upload/55892/55892fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55892/55892fig3.jpg" /> 
Figure 3: Result images obtained after data processing with the &#34;Island Assay Analysis&#34; script. (A) Line graph showing the flight escape response for each control experimental replicate. (B) Line graph showing the flight escape response for each tefu RNAi experimental replicate. (C) Average flight escape response for indicated experimental conditions; the error bars represent the SEM. (D) Dot plots representing the area under the curve distribution for control and mutant conditions. Experimental replicates for both tefu RNAi and control conditions are displayed as individual data points (in black) with medians (red line). <a href="https://cloudflare.jove.com/files/ftp_upload/55892/55892fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55892/55892fig4.jpg" /> 
Figure 4: Ubiquitous tefu knockdown flies show a significantly decreased ability to leave the platform. The data represent the percent of flies on the platform over time (s) for control (w-; Actin-Gal4/+) and tefu RNAi (w-; Actin-Gal4/GD11950) flies. (number of replicates = 5; the error bars represent the SEM). (A) Raw data obtained with the &#34;Drosophila Island Assay&#34; macro. (B) Dot plots representing the area under the curve distribution for control and tefu RNAi conditions obtained with the &#34;Drosophila Island Assay&#34; macro (Welch unpaired t-test, **p &#60;0.01). (C) Raw data obtained by manually counting the number of flies present on the island per second. (D) Dot plots representing the area under the curve distribution for control and Tefu RNAi conditions obtained by hand-counting (Welch unpaired t-test, **p &#60;0.01). The error bars represent the SEM. <a href="https://cloudflare.jove.com/files/ftp_upload/55892/55892fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Column name</td>
			<td colspan="3">Description</td>
		</tr>
		<tr>
			<td>Slice</td>
			<td colspan="3">Frame name.</td>
		</tr>
		<tr>
			<td>Count</td>
			<td colspan="3">The number of objects detected in the frame within the limits of the platform (ROI).</td>
		</tr>
		<tr>
			<td>Total Area</td>
			<td colspan="3">Total area of the objects detected in the frame within the limits of the platform (ROI) in pixels.</td>
		</tr>
		<tr>
			<td>Average Size</td>
			<td colspan="3">Total area of the objects detected in the frame divided by the number of objects within the limits of the platform (ROI).</td>
		</tr>
		<tr>
			<td>% Area</td>
			<td colspan="3">The percentage of area occupied by the objects with respect to the total area of the platform (ROI).</td>
		</tr>
		<tr>
			<td>Perim.</td>
			<td colspan="3">Total perimeter of the objects detected in the frame within the limits of the platform (ROI) in pixels.</td>
		</tr>
		<tr>
			<td>Min. Fly Size</td>
			<td colspan="3">The minimum fly size setting defined by the user in the graphical interface of the &#34;Drosophila Island Assay&#34; macro (in pixels).</td>
		</tr>
		<tr>
			<td>Area ROI</td>
			<td colspan="3">The area of the platform (ROI) defined by the user during the run of the sub-macro define platform (in pixels).</td>
		</tr>
		<tr>
			<td>Number of Flies</td>
			<td colspan="3">The number of flies used per experiment defined by the user in the graphical interface of the &#34;Drosophila Island Assay&#34; macro.</td>
		</tr>
	</tbody>
</table>
Table 1: Parameters measured by the &#34;Drosophila Island Assay&#34; macro. The parameters described in this table will appear in the &#34;results.txt&#34; file upon running the &#34;Drosophila Island Assay&#34; macro.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Column name</td>
			<td colspan="3">Description</td>
		</tr>
		<tr>
			<td>Slice</td>
			<td colspan="3">Frame number.</td>
		</tr>
		<tr>
			<td>Count</td>
			<td colspan="3">The number of flies detected in the frame within the limits of the platform (ROI).</td>
		</tr>
		<tr>
			<td>X.Area</td>
			<td colspan="3">Total area of the flies detected in the frame within the limits of the platform (ROI) in pixels.</td>
		</tr>
		<tr>
			<td>Min. Fly Size</td>
			<td colspan="3">The minimum fly size entry setting defined by the user in the graphical interface of the &#34;Drosophila Island Assay&#34; macro (in pixels).</td>
		</tr>
		<tr>
			<td>Area ROI</td>
			<td colspan="3">The area of the platform (ROI, in pixels) defined by the user when running the sub-macro &#34;define platform&#34;.</td>
		</tr>
		<tr>
			<td>Number of Flies</td>
			<td colspan="3">The number of flies used per experiment defined by the user in the graphical interface of the &#34;Drosophila Island Assay&#34; macro.</td>
		</tr>
		<tr>
			<td>X.Count</td>
			<td colspan="3">% flies present on the platform in the respective slice/frame relative to the highest number of flies detected on the platform during experiment.</td>
		</tr>
		<tr>
			<td>Timepoint</td>
			<td colspan="3">Time point 1 represents the first frame to be analysed and corresponds to the frame where the flies first appear on the platform. There are a total of 100 frames per replicate analyzed (corresponding to 10 s, when using the described settings.</td>
		</tr>
		<tr>
			<td>Experiment</td>
			<td colspan="3">Number of replicates per condition.</td>
		</tr>
		<tr>
			<td>Condition</td>
			<td colspan="3">Indicates the name of the experimental condition (according to the user-defined name of the folder containing the data).</td>
		</tr>
	</tbody>
</table>
Table 2: Description of the variables obtained after processing the data with the &#34;Island Assay Analysis&#34; script. The parameters described in this table appear in the &#34;data_all_conditions.csv&#34; file upon processing the data with the &#34;Island Assay Analysis&#34; script.

Watch this Scientific Journal Video about High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay at JoVE.com

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

The island assay is a relatively new, cost-effective assay that can be used to evaluate the basic locomotor behavior of Drosophila melanogaster. This manuscript describes algorithms for automatic data processing and objective quantification of island assay data, making this assay a sensitive, high-throughput readout for large genetic or pharmacological screens.

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other ...

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8.8K Views.  Radboud University Medical Center. The island assay is a relatively new, cost-effective assay that can be used to evaluate the basic locomotor behavior of Drosophila melanogaster. This manuscript describes algorithms for automatic data processing and objective quantification of island assay data, making this assay a sensitive, high-throughput readout for large genetic or pharmacological screens.

Video: High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

Single Sensillum Recordings in the Insects Drosophila melanogaster and Anopheles gambiae

The sense of smell is essential for insects to find foods, mates, predators, and oviposition sites3. Insect olfactory sensory neurons (OSNs) are enclosed in sensory hairs called sensilla, which cover the surface of olfactory organs. The surface of each sensillum is covered with tiny pores, through which odorants pass and dissolve in a fluid called sensillum lymph, which bathes the sensory dendrites of the OSNs housed in a given sensillum. The OSN dendrites express odorant receptor (OR) proteins, which in insects function as odor-gated ion channels4, 5. The interaction of odorants with ORs either increases or decreases the basal firing rate of the OSN. This neuronal activity in the form of action potentials embodies the first representation of the quality, intensity, and temporal characteristics of the odorant6, 7.
Given the easy access to these sensory hairs, it is possible to perform extracellular recordings from single OSNs by introducing a recording electrode into the sensillum lymph, while the reference electrode is placed in the lymph of the eye or body of the insect. In Drosophila, sensilla house between one and four OSNs, but each OSN typically displays a characteristic spike amplitude. Spike sorting techniques make it possible to assign spiking responses to individual OSNs. This single sensillum recording (SSR) technique monitors the difference in potential between the sensillum lymph and the reference electrode as electrical spikes that are generated by the receptor activity on OSNs1, 2, 8. Changes in the number of spikes in response to the odorant represent the cellular basis of odor coding in insects. Here, we describe the preparation method currently used in our lab to perform SSR on Drosophila melanogaster and Anopheles gambiae, and show representative traces induced by the odorants in a sensillum-specific manner.

Single Sensillum Recordings in the Insects Drosophila melanogaster and Anopheles gambiae

Electrophysiological responses of olfactory sensory neurons to odorants can be measured in insects using single sensillum recordings. In this video article we will demonstrate how to perform single sensillum recordings in the antennae of the vinegar fly (Drosophila melanogaster) and the maxillary palps of the malaria mosquito (Anopheles gambiae).

The sense of smell is essential for insects to find foods, mates, predators, and oviposition sites3. Insect olfactory sensory neurons (OSNs) are enclosed ...

Assaying Locomotor, Learning, and Memory Deficits in Drosophila Models of Neurodegeneration

Advances in genetic methods have enabled the study of genes involved in human neurodegenerative diseases using Drosophila as a model system1. Most of these diseases, including Alzheimer's, Parkinson's and Huntington's disease are characterized by age-dependent deterioration in learning and memory functions and movement coordination2. Here we use behavioral assays, including the negative geotaxis assay3 and the aversive phototaxic suppression assay (APS assay)4,5, to show that some of the behavior characteristics associated with human neurodegeneration can be recapitulated in flies. In the negative geotaxis assay, the natural tendency of flies to move against gravity when agitated is utilized to study genes or conditions that may hinder locomotor capacities. In the APS assay, the learning and memory functions are tested in positively-phototactic flies trained to associate light with aversive bitter taste and hence avoid this otherwise natural tendency to move toward light. Testing these trained flies 6 hours post-training is used to assess memory functions. Using these assays, the contribution of any genetic or environmental factors toward developing neurodegeneration can be easily studied in flies.

Assaying Locomotor, Learning, and Memory Deficits in Drosophila Models of Neurodegeneration

Behavior assays for measuring locomotor functions, learning, and memory abilities in Drosophila.

Advances in genetic methods have enabled the study of genes involved in human neurodegenerative diseases using Drosophila as a model system1. Most of ...

Studying the Neural Basis of Adaptive Locomotor Behavior in Insects

Studying the neural basis of walking behavior, one often faces the problem that it is hard to separate the neuronally produced stepping output from those leg movements that result from passive forces and interactions with other legs through the common contact with the substrate. If we want to understand, which part of a given movement is produced by nervous system motor output, kinematic analysis of stepping movements, therefore, needs to be complemented with electrophysiological recordings of motor activity. The recording of neuronal or muscular activity in a behaving animal is often limited by the electrophysiological equipment which can constrain the animal in its ability to move with as many degrees of freedom as possible. This can either be avoided by using implantable electrodes and then having the animal move on a long tether (i.e. Clarac et al., 1987; Duch &amp; Pfl&uuml;ger, 1995; B&ouml;hm et al., 1997; Gruhn &amp; Rathmayer, 2002) or by transmitting the data using telemetric devices (Kutsch et al, 1993; Fischer et al., 1996; Tsuchida et al. 2004; Hama et al., 2007; Wang et al., 2008). Both of these elegant methods, which are successfully used in larger arthropods, often prove difficult to apply in smaller walking insects which either easily get entangled in the long tether or are hindered by the weight of the telemetric device and its batteries. In addition, in all these cases, it is still impossible to distinguish between the purely neuronal basis of locomotion and the effects exerted by mechanical coupling between the walking legs through the substrate. One solution for this problem is to conduct the experiments in a tethered animal that is free to walk in place and that is locally suspended, for example over a slippery surface, which effectively removes most ground contact mechanics. This has been used to study escape responses (Camhi and Nolen, 1981; Camhi and Levy, 1988), turning (Tryba and Ritzman, 2000a,b; Gruhn et al., 2009a), backward walking (Graham and Epstein, 1985) or changes in velocity (Gruhn et al., 2009b) and it allows the experimenter easily to combine intra- and extracellular physiology with kinematic analyses (Gruhn et al., 2006).
We use a slippery surface setup to investigate the timing of leg muscles in the behaving stick insect with respect to touch-down and lift-off under different behavioral paradigms such as straight forward and curved walking in intact and reduced preparations.

We describe a method to record motor activity, timed to the electrically recorded tarsal contact signal in a tethered insect, walking on a slippery surface. This is used to study the neural basis of adaptive behavior under reduced influence of mechanical interaction between legs through the substrate.

Studying the neural basis of walking behavior, one often faces the problem that it is hard to separate the neuronally produced stepping output from those ...

In vivo Electroporation of Developing Mouse Retina

The functional characterization of genes expressed during mammalian retinal development remains a significant challenge. Gene targeting to generate constitutive or conditional loss of function knockouts remains cost and labor intensive, as well as time consuming. Adding to these challenges, retina expressed genes may have essential roles outside the retina leading to unintended confounds when using a knockout approach. Furthermore, the ability to ectopically express a gene in a gain of function experiment can be extremely valuable when attempting to identify a role in cell fate specification and/or terminal differentiation.
We present a method for the rapid and efficient incorporation of DNA plasmids into the neonatal mouse retina by electroporation. The application of short electrical impulses above a certain field strength results in a transient increase in plasma membrane permeability, facilitating the transfer of material across the membrane 1,2,3,4. Groundbreaking work demonstrated that electroporation could be utilized as a method of gene transfer into mammalian cells by inducing the formation of hydrophilic plasma membrane pores allowing the passage of highly charged DNA through the lipid bilayer 5. Continuous technical development has resulted in the viability of electroporation as a method for in vivo gene transfer in multiple mouse tissues including the retina, the method for which is described herein 6, 7, 8, 9, 10. 
DNA solution is injected into the subretinal space so that DNA is placed between the retinal pigmented epithelium and retina of the neonatal (P0) mouse and electrical pulses are applied using a tweezer electrode. The lateral placement of the eyes in the mouse allows for the easy orientation of the tweezer electrode to the necessary negative pole-DNA-retina-positive pole alignment. Extensive incorporation and expression of transferred genes can be identified by postnatal day 2 (P2). Due to the lack of significant lateral migration of cells in the retina, electroporated and non-electroporated regions are generated. Non-electroporated regions may serve as internal histological controls where appropriate. 
Retinal electroporation can be used to express a gene under a ubiquitous promoter, such as CAG, or to disrupt gene function using shRNA constructs or Cre-recombinase. More targeted expression can be achieved by designing constructs with cell specific gene promoters. Visualization of electroporated cells is achieved using bicistronic constructs expressing GFP or by co-electroporating a GFP expression construct. Furthermore, multiple constructs may be electroporated for the study of combinatorial gene effects or simultaneous gain and loss of function of different genes. Retinal electroporation may also be utilized for the analysis of genomic cis-regulatory elements by generating appropriate expression constructs and deletion mutants. Such experiments can be used to identify cis-regulatory regions sufficient or required for cell specific gene expression 11. Potential experiments are limited only by construct availability.

In vivo Electroporation of Developing Mouse Retina

A method for the incorporation of plasmid DNA into murine retinal cells for the purpose of performing either gain- or loss of function studies in vivo is presented. This method capitalizes on the transient increase in permeability of cell plasma membranes induced by the application of an external electrical field.

The functional characterization of genes expressed during mammalian retinal development remains a significant challenge.  Gene targeting to generate ...

Methods to Assay Drosophila Behavior

Drosophila melanogaster, the fruit fly, has been used to study molecular mechanisms of a wide range of human diseases such as cancer, cardiovascular disease and various neurological diseases1. We have optimized simple and robust behavioral assays for determining larval locomotion, adult climbing ability (RING assay), and courtship behaviors of Drosophila. These behavioral assays are widely applicable for studying the role of genetic and environmental factors on fly behavior. Larval crawling ability can be reliably used for determining early stage changes in the crawling abilities of Drosophila larvae and also for examining effect of drugs or human disease genes (in transgenic flies) on their locomotion. The larval crawling assay becomes more applicable if expression or abolition of a gene causes lethality in pupal or adult stages, as these flies do not survive to adulthood where they otherwise could be assessed. This basic assay can also be used in conjunction with bright light or stress to examine additional behavioral responses in Drosophila larvae. Courtship behavior has been widely used to investigate genetic basis of sexual behavior, and can also be used to examine activity and coordination, as well as learning and memory. Drosophila courtship behavior involves the exchange of various sensory stimuli including visual, auditory, and chemosensory signals between males and females that lead to a complex series of well characterized motor behaviors culminating in successful copulation. Traditional adult climbing assays (negative geotaxis) are tedious, labor intensive, and time consuming, with significant variation between different trials2-4. The rapid iterative negative geotaxis (RING) assay5 has many advantages over more widely employed protocols, providing a reproducible, sensitive, and high throughput approach to quantify adult locomotor and negative geotaxis behaviors. In the RING assay, several genotypes or drug treatments can be tested simultaneously using large number of animals, with the high-throughput approach making it more amenable for screening experiments.

Methods to Assay Drosophila Behavior

Drosophila melanogaster is a genetically and behaviorally tractable model system that has been used to understand the molecular and cellular basis of many important biological processes for over a century 1. Drosophila has been well exploited to gain insights into the genetic basis of fly behavior.

Drosophila melanogaster, the fruit fly, has been used to study molecular mechanisms of a wide range of human diseases such as cancer, cardiovascular ...

Optogenetic Stimulation of Escape Behavior in Drosophila melanogaster

A growing number of genetically encoded tools are becoming available that allow non-invasive manipulation of the neural activity of specific neurons in Drosophila melanogaster1. Chief among these are optogenetic tools, which enable the activation or silencing of specific neurons in the intact and freely moving animal using bright light. Channelrhodopsin (ChR2) is a light-activated cation channel that, when activated by blue light, causes depolarization of neurons that express it. ChR2 has been effective for identifying neurons critical for specific behaviors, such as CO2 avoidance, proboscis extension and giant-fiber mediated startle response2-4. However, as the intense light sources used to stimulate ChR2 also stimulate photoreceptors, these optogenetic techniques have not previously been used in the visual system. Here, we combine an optogenetic approach with a mutation that impairs phototransduction to demonstrate that activation of a cluster of loom-sensitive neurons in the fly's optic lobe, Foma-1 neurons, can drive an escape behavior used to avoid collision. We used a null allele of a critical component of the phototransduction cascade, phospholipase C-&beta;, encoded by the norpA gene, to render the flies blind and also use the Gal4-UAS transcriptional activator system to drive expression of ChR2 in the Foma-1 neurons. Individual flies are placed on a small platform surrounded by blue LEDs. When the LEDs are illuminated, the flies quickly take-off into flight, in a manner similar to visually driven loom-escape behavior. We believe that this technique can be easily adapted to examine other behaviors in freely moving flies.

Optogenetic Stimulation of Escape Behavior in Drosophila melanogaster

Genetically encoded optogenetic tools enable noninvasive manipulation of specific neurons in the Drosophila brain. Such tools can identify neurons whose activation is sufficient to elicit or suppress particular behaviors. Here we present a method for activating Channelrhodopsin2 that is expressed in targeted neurons in freely walking flies.

A  growing number of genetically encoded tools are becoming available that allow non-invasive  manipulation of the neural activity of specific neurons in ...

A Single-fly Assay for Foraging Behavior in Drosophila

For many animals, hunger promotes changes in the olfactory system in a manner that facilitates the search for appropriate food sources. In this video article, we describe an automated assay to measure the effect of hunger or satiety on olfactory dependent food search behavior in the adult fruit fly Drosophila melanogaster. In a light-tight box illuminated by red light that is invisible to fruit flies, a camera linked to custom data acquisition software monitors the position of six flies simultaneously. Each fly is confined to walk in individual arenas containing a food odor at the center. The testing arenas rest on a porous floor that functions to prevent odor accumulation. Latency to locate the odor source, a metric that reflects olfactory sensitivity under different physiological states, is determined by software analysis. Here, we discuss the critical mechanics of running this behavioral paradigm and cover specific issues regarding fly loading, odor contamination, assay temperature, data quality, and statistical analysis.

A Single-fly Assay for Foraging Behavior in Drosophila

In this video article, we describe an automated assay to measure the effect of hunger or satiety on olfactory dependent food search behavior in the adult fruit fly Drosophila melanogaster.

For many animals, hunger promotes changes in the olfactory system in a manner that facilitates the search for appropriate food sources. In this video ...

Determination of the Spontaneous Locomotor Activity in Drosophila melanogaster

Drosophila melanogaster has been used as an excellent model organism to study environmental and genetic manipulations that affect behavior. One such behavior is spontaneous locomotor activity. Here we describe our protocol that utilizes Drosophila population monitors and a tracking system that allows continuous monitoring of the spontaneous locomotor activity of flies for several days at a time. This method is simple, reliable, and objective and can be used to examine the effects of aging, sex, changes in caloric content of food, addition of drugs, or genetic manipulations that mimic human diseases.

Determination of the Spontaneous Locomotor Activity in Drosophila melanogaster

Drosophila melanogaster are useful in studying genetic or environmental manipulations that affect behaviors such as spontaneous locomotor activity.&#160; Here we describe a protocol that utilizes monitors with infrared beams and data analysis software to quantify spontaneous locomotor activity.&#160;

Drosophila melanogaster has been used as an excellent model organism to study environmental and genetic manipulations that affect behavior. One such ...

High Throughput Assay to Examine Egg-Laying Preferences of Individual Drosophila melanogaster

Recently, egg-laying preference of Drosophila has emerged as a genetically tractable model to study the neural basis of simple decision-making processes. When selecting sites to deposit their eggs, female flies are capable of ranking the relative attractiveness of their options and choosing the &#34;greater of two goods.&#34; However, most egg-laying preference assays are not practical if one wants to take a systematic genetic screening approach to search for the circuit basis underlying this simple decision-making process, as they are population-based and laborious to set up. To increase the throughput of studying of egg-laying preferences of single females, we developed custom chambers that each can simultaneously assay egg-laying preferences of up to thirty individual flies as well as a protocol that ensures each female has a high egg-laying rate (so that their preference is readily discernable and more convincing). Our approach is simple to execute and produces very consistent results. Additionally, these chambers can be equipped with different attachments to allow video recording the egg-laying animals and to deliver light for optogenetics studies. This article provides the blueprints for fabricating these chambers and the procedure for preparing the flies to be assayed in these chambers.

High Throughput Assay to Examine Egg-Laying Preferences of Individual Drosophila melanogaster

This protocol describes a high throughput assay for testing egg-laying preferences of Drosophila melanogaster at single-animal resolution. This assay provides a simple, efficient, and scalable platform to identify genes and circuit components that control a simple decision-making process.

Recently, egg-laying preference of Drosophila has emerged as a genetically tractable model to study the neural basis of simple decision-making processes. ...

A Simple Way to Measure Alterations in Reward-seeking Behavior Using Drosophila melanogaster

We describe a protocol for measuring ethanol self-administration in fruit flies (Drosophila melanogaster) as a proxy for changes in reward states. We demonstrate a simple way to tap into the fly reward system, modify experiences related to natural reward, and use voluntary ethanol consumption as a measure for changes in reward states. The approach serves as a relevant tool to study the neurons and genes that play a role in experience-mediated changes of internal state. The protocol is composed of two discrete parts: exposing the flies to rewarding and nonrewarding experiences, and assaying voluntary ethanol consumption as a measure of the motivation to obtain a drug reward. The two parts can be used independently to induce the modulation of experience as an initial step for further downstream assays or as an independent two-choice feeding assay, respectively. The protocol does not require a complicated setup and can therefore be applied in any laboratory with basic fly culture tools.

A Simple Way to Measure Alterations in Reward-seeking Behavior Using Drosophila melanogaster

We describe a protocol for inducing rewarding and nonrewarding experiences in fruit flies (Drosophila melanogaster) using voluntary ethanol consumption as a measure for changes in reward states.

We describe a protocol for measuring ethanol self-administration in fruit flies (Drosophila melanogaster) as a proxy for changes in reward states. We ...