We are grateful to Stanislav Lazopulo for help with the video content.

1. Measuring Fly Locomotion Using Drosophila Activity Monitor (DAM)
NOTE: For more details see reference5.
<ol>
	<li>Prepare individual fly tubes with food on one end and cotton on the other. The end with food should be sealed to prevent the food from drying out.
	<ol>
		<li>Put 5-6 g of fly food in a 50 mL beaker. Cut the food into small pieces so that it is easier to melt it.</li>
		<li>Connect 32 individual glass tubes with a rubber band.</li>
		<li>Melt the food in the beaker by heating it in a microwave oven for 10-15 s. Stop the microwave every 5 s and carefully shake the beaker to ensure equal melting of food.</li>
		<li>While the food is still liquid, put the prepared individual tubes in the beaker with food. Move the tubes up and down so they are equally filled.</li>
		<li>Allow the food to cool down and solidify for approximately 1 h.</li>
		<li>After the food is solid, remove the tubes with the food from the beaker.</li>
		<li>Seal the end containing the food using wax. First, carefully clean a tube using a paper towel, then press the tube against the wax. Visually check the quality of the seal, and if necessary, repeat the sealing again.</li>
		<li>Close the other end of the tube with cotton; cotton will permit air to go through while keeping a fly locked in the tube.</li>
	</ol>
	</li>
	<li>Place a single fly in each individual tube and place the tubes in the DAM.</li>
	<li>Place monitors in an incubator that maintains constant temperature and humidity. Based on the experiment, set the proper light/dark conditions as follows.
	<ol>
		<li>For light/dark experiments keep the flies in light/dark cycle for the whole experiment. Do not use the first day of measurements in the analysis.</li>
		<li>For constant darkness experiments, first keep the flies for two days in light/dark conditions for entrainment and synchronization of the clocks and then switch to constant darkness. Do not use the measurements from the first day of constant darkness in the analysis.</li>
	</ol>
	</li>
	<li>Collect at least four days of data that can be used in the analysis. 
	NOTE: The DAM system will output a single file with a locomotion recording of all flies in the monitor.</li>
</ol>
2. Data Analysis
<ol>
	<li>Split the monitor output file into multiple single fly activity files; each file should be a single column &#39;.txt&#39; file with an individual fly locomotion measurement.</li>
	<li>Run &#39;ModelFitPS3.m&#39; function in a Matlab command window with the following input parameters:
	<ol>
		<li>For samplingrate, set the data sampling time interval in seconds. For example, if activity was measured every minute, enter 60 as the samplingrate.</li>
		<li>For bin_interval, set the time interval in minutes to which data will be binned for better visualization; the recommended bin interval is 20-30 min.</li>
		<li>For trend, enter &#34;1&#34; if the data show baseline trend and &#34;0&#34; otherwise; data with trend will be detrended first by fitting a second order polynomial and then subtracting it from the data.</li>
	</ol>
	</li>
	<li>In the popup window select a single fly activity file. 
	Note: The first plot is data power spectrum, and not the familiar activity plot. From the plotted power spectrum, determine the primary period T0: Either click with the left mouse button on the circadian peak, or with right mouse button on the second harmonic peak (around T0/2).</li>
	<li>On the opened data plot, check if the morning and evening peaks are well visualized. If not, change the bin_interval value by right clicking anywhere on the graph and inputting the new bin_interval value in the dialog box. The program will redraw the data with the new value of the interval. To accept the bin_interval value, left click anywhere on the graph.</li>
	<li>The program will redraw the data again and show the first five days of activity. On this plot, click on the first M peak that will be used in the analysis (sometimes it is necessary to skip one or two days). 
	NOTE: The program will redraw the graph starting from the picked morning peak. The blue and red lines will show the approximate position of the E peak and next day M peak, respectively, based on the period determined in step 2.4.</li>
	<li>On the same graph, choose data for a preliminary fit of the data with the model: Click on the following points (in this order; note that the click location will be indicated with a red star on the bottom): (i) Top of M peak; (ii) End of the M peak; (iii) Start of the E peak; (iv) Top of the E peak; (v) End of the E peak; (vi) Top of the M peak of the next day.</li>
	<li>Note that the program now presents the power spectrum. 
	NOTE: The x axis is now given in frequency.
	<ol>
		<li>In the opened window with the power spectrum, pick points that will be used for fitting the analytical expression for the model power spectrum. The period detected in step 2.4 is marked with a red line. To pick fitting points, first roughly determine the primary period, similar to step 2.4. Then using the slider, fine tune the primary period value so that the fitting points (shown with red circles, will appear after moving the slider) are closed to peak values.</li>
	</ol>
	</li>
	<li>After the visual peak selection, click &#34;Accept&#34; and the program will fit selected points with the analytical expression to calculate the model parameters.</li>
	<li>Note that the parameters and spectral fit error are saved to the file &#34;model_fit_parameters.txt&#34;; the program will additionally save 2 figures with fits of the locomotor data and its power spectrum.</li>
</ol>

<ol>
	<li>Pittendrigh, C. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Circadian+systems:+general+perspective.">Circadian systems: general perspective.</a> Biological Rhythms. II, 57-80 (1981).</li><li>Zhang, E. E., Kay, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clocks+not+winding+down:+unravelling+circadian+networks.">Clocks not winding down: unravelling circadian networks.</a> Nat Rev Mol Cell Biol. 11 (11), 764-776 (2010).</li><li>Tataroglu, O., Emery, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+molecular+ticks+of+the+Drosophila+circadian+clock.">The molecular ticks of the Drosophila circadian clock.</a> Curr Opin Insect Sci. 7, 51-57 (2015).</li><li>Plautz, J. D., 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=Quantitative+analysis+of+Drosophila+period+gene+transcription+in+living+animals.">Quantitative analysis of Drosophila period gene transcription in living animals.</a> J Biol Rhythms. 12 (3), 204-217 (1997).</li><li>Chiu, J. C., Low, K. H., Pike, D. H., Yildirim, E., Edery, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assaying+locomotor+activity+to+study+circadian+rhythms+and+sleep+parameters+in+Drosophila.">Assaying locomotor activity to study circadian rhythms and sleep parameters in Drosophila.</a> J Vis Exp. (43), e2157 (2010).</li><li>Helfrich-F&#246;rster, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+control+of+morning+and+evening+components+in+the+activity+rhythm+of+Drosophila+melanogaster--sex-specific+differences+suggest+a+different+quality+of+activity.">Differential control of morning and evening components in the activity rhythm of Drosophila melanogaster--sex-specific differences suggest a different quality of activity.</a> J Biol Rhythms. 15 (2), 135-154 (2000).</li><li>Dautzenberg, F. M., Neysari, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Irreversible+binding+kinetics+of+neuropeptide+Y+ligands+to+Y2+but+not+to+Y1+and+Y5+receptors.">Irreversible binding kinetics of neuropeptide Y ligands to Y2 but not to Y1 and Y5 receptors.</a> Pharmacology. 75 (1), 21-29 (2005).</li><li>Lazopulo, A., Syed, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+mathematical+model+provides+mechanistic+links+to+temporal+patterns+in+Drosophila+daily+activity.">A mathematical model provides mechanistic links to temporal patterns in Drosophila daily activity.</a> BMC Neuroscience. 17 (1), 14 (2016).</li><li>Donelson, N., Kim, E. Z., Slawson, J. B., Vecsey, C. G., Huber, R., Griffith, L. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+positional+tracking+for+long-term+analysis+of+Drosophila+sleep+and+locomotion+using+the+&amp;#34;tracker&amp;#34;+program.">High-resolution positional tracking for long-term analysis of Drosophila sleep and locomotion using the &amp;#34;tracker&amp;#34; program.</a> PloS ONE. 7 (5), e37250 (2012).</li><li>Schlichting, M., 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=A+Neural+Network+Underlying+Circadian+Entrainment+and+Photoperiodic+Adjustment+of+Sleep+and+Activity+in+Drosophila.">A Neural Network Underlying Circadian Entrainment and Photoperiodic Adjustment of Sleep and Activity in Drosophila.</a> J Neurosci. 36 (35), 9084-9096 (2016).</li><li>Guo, F., 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=Circadian+neuron+feedback+controls+the+Drosophila+sleep-activity+profile.">Circadian neuron feedback controls the Drosophila sleep-activity profile.</a> Nature. 536 (7616), 292-297 (2016).</li></ol>

The authors have nothing to disclose.

This work presents instructions for using a computational tool that provides a quantitative description of fly locomotion pattern. The tool fits locomotion data with a mathematical model consisting of four exponential terms that together describe the shape and size of the M and E peaks. The final values for the model parameters are obtained from fitting the power spectra of the data, where the use of the raw data can avoid artefactual effects that data binning or filtering can impose on parameter values. The model parame...

The circadian clock is an endogenous biochemical oscillator with a period of approximately 24 hours and is almost ubiquitous in animals and plants1,2. The clock helps synchronize an organism&#39;s internal processes and behavior to the external light dark cycle. The genetic structure of the circadian clock has been widely studied since the 1960s using the fruit fly, D. melanogaster. In this insect, the core of the circadian clock consists of four proteins: PERIOD, TIMELESS, CLOCK, and CYCLE. These core components together with other molecules form a feedback loop that produces nearly sinusoidal oscillations of clock genes3,4. The circadian clock in flies is widely studied using multiday locomotor recordings where fly activity is detected with a single infrared beam crossing the middle of an individual tube5. A typical fly recording has a complex bimodal pattern with two well distinguishable peaks: Morning peak (M) that starts at the end of the night and has a maximum when lights turn on; and Evening peak (E) that starts at the end of the day and has a maximum when lights turn off6. Interestingly, the shape of such behavioral recording is very different from the simple sinusoidal oscillations observed at the molecular level, suggesting the action of additional mechanisms contributing to the observed temporal patterns. To better understand these hidden mechanisms, we have developed a computational tool that provides a quantitative description of the temporal patterns.
In our work, locomotor rhythms are defined in terms of a waveform that mimics fly activity pattern. Since simple sine waves cannot be used to model the observed rhythmic changes in activity, we tested various signal shapes to select the simplest one that captures all the salient features seen in the recordings. Fruit fly circadian behavior is controlled by the activity of clock neurons that often have exponential patterns of activation and deactivation7. The exponential dynamics and visual analysis of the data motivated us to build a model with exponential terms consisting of four exponents with nine independent parameters and closely resembling the fly activity pattern8. In addition to the locomotor data, we also analyze its power spectrum. Typical fly activity spectrum shows multiple peaks at harmonics T0/2, T0/3, etc., in addition to the expected fundamental peak at the circadian period T0. According to the Fourier theorem, only a pure sine wave produces a single peak in power spectra, while more complex waveforms show multiple spectral peaks at harmonics of the primary period (Figure 1). Therefore, given the non-sinusoidal temporal pattern in fly activity8, a multi-peak power spectrum of the data is mathematically expected and does not necessarily imply the presence of multiple periods of oscillation. Importantly, the power spectrum of the proposed model waveform also shows peaks at all harmonics of the primary period, similar to the fly locomotor recordings, thus underscoring the high fidelity with which our model describes fly data both in time and in frequency.
At time resolutions of a few minutes or less, fly activity data appears noisy, making it difficult to extract parameters directly from the raw data. Binning data into longer time intervals can decrease noise level, but, can alter the data in ways that can affect the estimation of model parameters. We therefore obtain the parameters from power spectra of the recordings, using an analytical expression for the expected power spectra calculated from the Fourier transform of the model function8 (see Additional File 1 of reference8). This approach of obtaining parameters from the power spectra yields accurate parameter values without any additional manipulations, such as binning or filtering, of the raw activity data. Mathematical details of the model and applications to wild-type and mutant data are described in reference8. The protocol presented here focuses on the step-by-step instructions to use the computational tool.

<table><tbody><tr><td>Drosophila Activity Monitor</td><td>TriKinetics</td><td>DAM2, DAM5</td><td>Measures fly locootion using single infrared beam</td></tr><tr><td>MatLab</td><td>Mathworks</td><td></td><td>Computing environment and programming language, MatLab should include Optimization and Symbolic Math toolboxes</td></tr><tr><td>Drosophila melanogaster&amp;nbsp;</td><td></td><td>per[S], per[L], iso31(wild type)</td><td>Our analysis can be performed with fly mutants of any circadian period</td></tr></tbody></table>

a computational method to quantify fly circadian activity

In most animals and plants, circadian clocks orchestrate behavioral and molecular processes and synchronize them to the daily light-dark cycle. Fundamental mechanisms that underlie this temporal control are widely studied using the fruit fly Drosophila melanogaster as a model organism. In flies, the clock is typically studied by analyzing multiday locomotor recording. Such a recording shows a complex bimodal pattern with two peaks of activity: a morning peak that happens around dawn, and an evening peak that happens around dusk. These two peaks together form a waveform that is very different from sinusoidal oscillations observed in clock genes, suggesting that mechanisms in addition to the clock have profound effects in producing the observed patterns in behavioral data. Here we provide instructions on using a recently developed computational method that mathematically describes temporal patterns in fly activity. The method fits activity data with a model waveform that consists of four exponential terms and nine independent parameters that fully describe the shape and size of the morning and evening peaks of activity. The extracted parameters can help elucidate the kinetic mechanisms of substrates that underlie the commonly observed bimodal activity patterns in fly locomotor rhythms.

The method presented here allows quantification of the main features in fly locomotion pattern. The quantification is achieved by fitting the activity data with a model that consists of four exponential terms:
<img alt="Equation 1" src="/files/ftp_upload/55977/55977eq1.jpg" />
The model has nine independent parameters that describe activity pattern. The parameters b<su...

Watch this Scientific Journal Video about A Computational Method to Quantify Fly Circadian Activity at JoVE.com

A Computational Method to Quantify Fly Circadian Activity

A method to quantify the main temporal features seen in fly circadian locomotor rhythms is presented. The quantification is achieved by fitting fly activity with a multi-parametric model waveform. The model parameters describe the shape and size of the morning and evening peaks of daily activity.

In most animals and plants, circadian clocks orchestrate behavioral and molecular processes and synchronize them to the daily light-dark cycle. ...

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Behavior

5.9K Views.  University of Miami. Een methode om te kwantificeren van de belangrijkste tijdelijke functies gezien in vliegen circadiane ritmes van locomotor wordt gepresenteerd. De kwantificering wordt bereikt door het aanbrengen van vliegen activiteit met de golfvorm van een meerdere parametrische model. De model-parameters beschrijven de vorm en de grootte van de ochtend en avond toppen van dagelijkse activiteit.

Video: A Computational Method to Quantify Fly Circadian Activity

Assaying Locomotor Activity to Study Circadian Rhythms and Sleep Parameters in Drosophila

Most life forms exhibit daily rhythms in cellular, physiological and behavioral phenomena that are driven by endogenous circadian (&equiv;24 hr) pacemakers or clocks. Malfunctions in the human circadian system are associated with numerous diseases or disorders. Much progress towards our understanding of the mechanisms underlying circadian rhythms has emerged from genetic screens whereby an easily measured behavioral rhythm is used as a read-out of clock function. Studies using Drosophila have made seminal contributions to our understanding of the cellular and biochemical bases underlying circadian rhythms. The standard circadian behavioral read-out measured in Drosophila is locomotor activity. In general, the monitoring system involves specially designed devices that can measure the locomotor movement of Drosophila. These devices are housed in environmentally controlled incubators located in a darkroom and are based on using the interruption of a beam of infrared light to record the locomotor activity of individual flies contained inside small tubes. When measured over many days, Drosophila exhibit daily cycles of activity and inactivity, a behavioral rhythm that is governed by the animal's endogenous circadian system. The overall procedure has been simplified with the advent of commercially available locomotor activity monitoring devices and the development of software programs for data analysis. We use the system from Trikinetics Inc., which is the procedure described here and is currently the most popular system used worldwide. More recently, the same monitoring devices have been used to study sleep behavior in Drosophila. Because the daily wake-sleep cycles of many flies can be measured simultaneously and only 1 to 2 weeks worth of continuous locomotor activity data is usually sufficient, this system is ideal for large-scale screens to identify Drosophila manifesting altered circadian or sleep properties.

Assaying Locomotor Activity to Study Circadian Rhythms and Sleep Parameters in Drosophila

We describe procedures for recording daily locomotor activity rhythms of Drosophila and subsequent data analysis. Locomotor activity rhythms are a reliable behavioral output of animal circadian clocks and are used as the standard readout of clock function when studying circadian mutants or examining how the environment regulates the circadian system.

Most life forms exhibit daily rhythms in cellular, physiological and behavioral phenomena that are driven by endogenous circadian (&equiv;24 hr) ...

Neuroscience

Measuring Circadian and Acute Light Responses in Mice using Wheel Running Activity

Circadian rhythms are physiological functions that cycle over a period of approximately 24 hours (circadian- circa: approximate and diem: day)1, 2. They are responsible for timing our sleep/wake cycles and hormone secretion. Since this timing is not precisely 24-hours, it is synchronized to the solar day by light input. This is accomplished via photic input from the retina to the suprachiasmatic nucleus (SCN) which serves as the master pacemaker synchronizing peripheral clocks in other regions of the brain and peripheral tissues to the environmental light dark cycle3-7. The alignment of rhythms to this environmental light dark cycle organizes particular physiological events to the correct temporal niche, which is crucial for survival8. For example, mice sleep during the day and are active at night. This ability to consolidate activity to either the light or dark portion of the day is referred to as circadian photoentrainment and requires light input to the circadian clock9. Activity of mice at night is robust particularly in the presence of a running wheel. Measuring this behavior is a minimally invasive method that can be used to evaluate the functionality of the circadian system as well as light input to this system. Methods that will covered here are used to examine the circadian clock, light input to this system, as well as the direct influence of light on wheel running behavior.

This article will review methods that can be used to determine circadian function and light responsiveness in mice.

Circadian rhythms are physiological functions that cycle over a period of approximately 24 hours (circadian- circa: approximate and diem: day)1, 2.  They ...

Quantitative Measurement of the Immune Response and Sleep in Drosophila

A complex interaction between the immune response and host behavior has been described in a wide range of species. Excess sleep, in particular, is known to occur as a response to infection in mammals 1 and has also recently been described in Drosophila melanogaster2. It is generally accepted that sleep is beneficial to the host during an infection and that it is important for the maintenance of a robust immune system3,4. However, experimental evidence that supports this hypothesis is limited4, and the function of excess sleep during an immune response remains unclear. We have used a multidisciplinary approach to address this complex problem, and have conducted studies in the simple genetic model system, the fruitfly Drosophila melanogaster. We use a standard assay for measuring locomotor behavior and sleep in flies, and demonstrate how this assay is used to measure behavior in flies infected with a pathogenic strain of bacteria. This assay is also useful for monitoring the duration of survival in individual flies during an infection. Additional measures of immune function include the ability of flies to clear an infection and the activation of NF&kappa;B, a key transcription factor that is central to the innate immune response in Drosophila. Both survival outcome and bacterial clearance during infection together are indicators of resistance and tolerance to infection. Resistance refers to the ability of flies to clear an infection, while tolerance is defined as the ability of the host to limit damage from an infection and thereby survive despite high levels of pathogen within the system5. Real-time monitoring of NF&kappa;B activity during infection provides insight into a molecular mechanism of survival during infection. The use of Drosophila in these straightforward assays facilitates the genetic and molecular analyses of sleep and the immune response and how these two complex systems are reciprocally influenced.

Quantitative Measurement of the Immune Response and Sleep in Drosophila

To understand a link between the immune response and behavior, we describe a method to measure locomotor behavior in Drosophila during bacterial infection as well as the ability of flies to mount an immune response by monitoring survival, bacterial load, and real-time activity of a key regulator of innate immunity, NF&kappa;B.

A complex interaction between the immune response and  host behavior has been described in a wide range of species. Excess sleep, in  particular, is known ...

Immunology and Infection

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 ...

A Low-cost Method for Analyzing Seizure-like Activity and Movement in Drosophila

Video tracking systems have been used widely to analyze Drosophila melanogaster movement and detect various abnormalities in locomotive behavior. While these systems can provide a wealth of behavioral information, the cost and complexity of these systems can be prohibitive for many labs. We have developed a low-cost assay for measuring locomotive behavior and seizure movement in D. melanogaster. The system uses a web-cam to capture images that can be processed using a combination of inexpensive and free software to track the distance moved, the average velocity of movement and the duration of movement during a specified time-span. To demonstrate the utility of this system, we examined a group of D. melanogaster mutants, the Bang-sensitive (BS) paralytics, which are 3-10 times more susceptible to seizure-like activity (SLA) than wild type flies. Using this novel system, we were able to detect that the BS mutant bang senseless (bss) exhibits lower levels of exploratory locomotion in a novel environment than wild type flies. In addition, the system was used to identify that the drug metformin, which is commonly used to treat type II diabetes, reduces the intensity of SLA in the BS mutants.

A Low-cost Method for Analyzing Seizure-like Activity and Movement in Drosophila

Using a webcam and a combination of free and inexpensive software programs, locomotive patterns in Drosophila melanogaster can be analyzed to detect differences in the speed, distance, and time of various motor activities.

Video tracking systems have been used widely to analyze Drosophila melanogaster movement and detect various abnormalities in locomotive behavior. While ...

Single-cell Resolution Fluorescence Live Imaging of Drosophila Circadian Clocks in Larval Brain Culture

The circadian pacemaker circuit orchestrates rhythmic behavioral and physiological outputs coordinated with environmental cues, such as day/night cycles. The molecular clock within each pacemaker neuron generates circadian rhythms in gene expression, which underlie the rhythmic neuronal functions essential to the operation of the circuit. Investigation of the properties of the individual molecular oscillators in different subclasses of pacemaker neurons and their interaction with neuronal signaling yields a better understanding of the circadian pacemaker circuit. Here, we present a time-lapse fluorescent microscopy approach developed to monitor the molecular clockwork in clock neurons of cultured Drosophila larval brain. This method allows the multi-day recording of the rhythms of genetically encoded fluorescent circadian reporters at single-cell resolution. This setup can be combined with pharmacological manipulations to closely analyze real-time response of the molecular clock to various compounds. Beyond circadian rhythms, this multipurpose method in combination with powerful Drosophila genetic techniques offers the possibility to study diverse neuronal or molecular processes in live brain tissue.

Single-cell Resolution Fluorescence Live Imaging of Drosophila Circadian Clocks in Larval Brain Culture

The goal of this protocol is to establish ex vivo Drosophila larval brain culture optimized to monitor circadian molecular rhythms with long-term fluorescence time-lapse imaging. The application of this method to pharmacological assays is also discussed.

The circadian pacemaker circuit orchestrates rhythmic behavioral and physiological outputs coordinated with environmental cues, such as day/night cycles. ...

The Sleep Nullifying Apparatus: A Highly Efficient Method of Sleep Depriving Drosophila

Sleep homeostasis, the increase in sleep observed following sleep loss, is one of the defining criteria used to identify sleep throughout the animal kingdom. As a consequence, sleep deprivation and sleep restriction are powerful tools that are commonly used to provide insight into sleep function. Nonetheless, sleep deprivation experiments are inherently problematic in that the deprivation stimulus itself may be the cause of observed changes in physiology and behavior. Accordingly, successful sleep deprivation techniques should keep animals awake and, ideally, result in a robust sleep rebound without also inducing a large number of unintended consequences. Here, we describe a sleep deprivation technique for Drosophila melanogaster. The Sleep Nullifying Apparatus (SNAP) administers a stimulus every 10s to induce negative geotaxis. Although the stimulus is predictable, the SNAP effectively prevents &#62;95% of nighttime sleep even in flies with high sleep drive. Importantly, the subsequent homeostatic response is very similar to that achieved using hand-deprivation. The timing and spacing of the stimuli can be modified to minimize sleep loss and thus examine non-specific effects of the stimulus on physiology and behavior. The SNAP can also be used for sleep restriction and to assess arousal thresholds. The SNAP is a powerful sleep disruption technique that can be used to better understand sleep function.

The Sleep Nullifying Apparatus: A Highly Efficient Method of Sleep Depriving Drosophila

Sleep deprivation is a powerful tool to investigate sleep function and regulation. We describe a protocol to sleep deprive Drosophila using the Sleep Nullifying Apparatus, and to determine the extent of rebound sleep induced by deprivation.

Sleep homeostasis, the increase in sleep observed following sleep loss, is one of the defining criteria used to identify sleep throughout the animal ...

Measurement of Larval Activity in the Drosophila Activity Monitor

Drosophila larvae are used in many behavioral studies, yet a simple device for measuring basic parameters of larval activity has not been available. This protocol repurposes an instrument often used to measure adult activity, the TriKinetics Drosophila activity monitor (MB5 Multi-Beam Activity Monitor) to study larval activity. The instrument can monitor the movements of animals in 16 individual 8 cm glass assay tubes, using 17 infrared detection beams per tube. Logging software automatically saves data to a computer, recording parameters such as number of moves, times sensors were triggered, and animals&#8217; positions within the tubes. The data can then be analyzed to represent overall locomotion and/or position preference as well as other measurements. All data are easily accessible and compatible with basic graphing and data manipulation software. This protocol will discuss how to use the apparatus, how to operate the software and how to run a larval activity assay from start to finish.

Measurement of Larval Activity in the Drosophila Activity Monitor

This report describes a method for measuring Drosophila larval activity using the TriKinetics Drosophila Activity Monitor. The device employs infrared beams to detect movements of up to 16 individual animals. Data can be analyzed to represent motion parameters including rates and the positions of the animals within the assay chambers.

Drosophila larvae are used in many behavioral studies, yet a simple device for measuring basic parameters of larval activity has not been available. This ...

Biology

Analysis of Circadian Photoresponses in Drosophila Using Locomotor Activity

Circadian rhythms are only beneficial to animals if they can be synchronized by changes of ambient conditions. Light and temperature are two dominant environmental parameters that synchronize animal circadian clocks. In Drosophila circadian photo-responses are mediated by a solely blue light photoreceptor named CRYPTOCHROME (CRY). Upon photoreception, CRY changes its conformation and initiates the proteosomal dependent degradation of TIMELESS (TIM). TIM is an important pacemaker protein, thus degradation of TIM will reset the circadian clock. Under constant light conditions (LL), wild type flies quickly become arrhythmic because of the constant degradation of TIM, while flies bearing defects with circadian photo-responses will still be rhythmic. Thus LL triggered arrhythmicity has been used for screening of components in circadian light input pathways. A brief short light pulse in the night can also dramatically shift phases of circadian rhythms. As expected, this phase shift response is reduced in flied with defects in circadian photoresponse. Thus analyzing locomotion behavior rhythmicity under LL or phase changes after short light pulse in constant darkness (DD) are two major methods to study circadian photoresponse. Here we describe how to design and analyze LL and phase response experiments. LL arrhythmicity is suitable for screening light input pathways mutants, whereas phase response validates the results and provide further information for light sensitivity.

Drosophila locomotor activity is a robust and quantitative measurement of circadian photo-responses. We describe protocols for designing behavior experiments for circadian photo-responses and analyzing the data. Studying the circadian photo-responses is important for dissecting the neuronal and molecular mechanisms of light entrainment.

Circadian rhythms are only beneficial to animals if they can be synchronized by changes of ambient conditions. Light and temperature are two dominant ...

Circadian Entrainment of Drosophila Melanogaster

Nearly universal among organisms, circadian rhythms coordinate biological activity to earth&#39;s orbit around the sun. To identify factors creating this rhythm and to understand the resulting outputs, entrainment of model organisms to defined circadian time-points is required. Here we detail a procedure to entrain many Drosophila to a defined circadian rhythm. Furthermore, we detail post-entrainment steps to prepare samples for immunofluorescence, nucleic acid, or protein extraction-based analysis.

Here, we detail how to synchronize Drosophila to a circadian day. This is the first, and most important step necessary for studying biological rhythms and chronobiology.

Nearly universal among organisms, circadian rhythms coordinate biological activity to earth&#39;s orbit around the sun. To identify factors creating this ...

A Computational Method to Quantify Fly Circadian Activity

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