Name: a computational method to quantify fly circadian activity
Uploaded: 2017-10-28T14:00:00
Description: Watch this Scientific Journal Video about A Computational Method to Quantify Fly Circadian Activity at JoVE.com

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.
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	<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 i...

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	<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>

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 border="0" cellpadding="0" cellspacing="0" width="1389">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:296px;">Drosophila Activity Monitor</td>
			<td style="width:81px;">TriKinetics</td>
			<td style="width:201px;">DAM2, DAM5</td>
			<td style="width:811px;">Measures fly locootion using single infrared beam</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:296px;">MatLab</td>
			<td style="width:81px;">Mathworks</td>
			<td style="width:201px;"></td>
			<td>Computing environment and programming language, MatLab should include Optimization and Symbolic Math toolboxes</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:21px;width:296px;">Drosophila melanogaster&#160;</td>
			<td style="width:81px;"></td>
			<td style="width:201px;">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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