Heat Tolerance Assays Using the Drosophila Activity Monitor System: A Guide to an Executable Application for Data Analysis

Summary

This report describes a method for measuring adult Drosophila melanogaster time to knockdown using a Drosophila Activity Monitor (DAM2) in response to an air conduction heat stressor within an incubator chamber. The DAM2 measures activity by recording individual fly movements as they cross an infrared beam. Data analysis is facilitated by a novel executable file created by the authors.

Abstract

The study of heat tolerance in Drosophila melanogaster has been of particular interest to researchers for decades, with a common approach to assessing heat tolerance being to monitor the time to knockdown (TKD) after exposure to an elevated temperature. Classically, flies are housed in individual vials and placed inside a heated water bath. TKD is then monitored manually by researchers. While very well-established, there remain problems of subjectivity and consistent application of a tangible definition of cessation of all movement, including muscular spasms, when implementing these manual assays. We have developed a high-throughput method for automating heat tolerance assays using the TriKinetics Drosophila Activity Monitors (DAM2). To accompany the DAM2 system, we have written a program and created an easy-use executable to automatically read the last time of movement from the activity data generated. This script then writes to a .csv file the time to heat paralysis (TKD) for each fly. Our data show that this automated DAM2 method is consistent and reliable. Meanwhile, activity profiles created from the activity count data are of interest. These activity profiles can be compiled and have the potential to expand heat tolerance assays to include the relatively unstudied behavioral components of heat tolerance. This protocol will describe in detail how to use the DAM2 system and the HoTDAM! software to estimate heat tolerance in D. melanogaster.

Introduction

Ectotherms typically respond to heat stress with increased locomotor activity. This phenomenon has been apparent to researchers for decades, with the characteristic behavioral response described by Cowles and Bogert in 1944¹. They described how an organism under heat stress will first show increased locomotor movement. As the heat stress builds, short bursts of activity are interspersed with periods of inactivity. The temperature at which the organism can no longer show coordinated movement is the critical thermal maximum (CTmax). Muscular spams follow, and ultimately the organism collapses^1,2. This collapse is difficult to define and represents something akin to "heat rigor, coma, or death"². Here we will use the term physiological collapse to theoretically refer to this blurry endpoint of heat stress.

Drosophila melanogaster and other small insects have been valuable models to study heat stress. To estimate at least a portion of the complex collection of traits that constitute heat tolerance, many researchers have manually observed the time and the temperature at which physiological collapse occurs, representing the time to knockdown (TKD) and CTmax, respectively. While very well established, these manual assay methods suffer some drawbacks. An operational definition of physiological collapse can be difficult to establish and apply appropriately to all cases, especially when observers are less experienced. For example, at what point does the organism go from muscular spasms to collapse? The pattern of muscular spasms and seizure activity prior to collapse can be unpredictable and can complicate accurate observation^2,3, threatening accuracy and precision. Meanwhile, the difficulty in observation also limits the number of organisms that can be assayed at one time, limiting scalability.

Since an increase in activity is a consistent response to heat and TKD and CTmax are ultimately the point at which activity ceases, we sought to employ the Drosophila Activity Monitors (DAM2) from TriKinetics to automate heat tolerance assays. We recently published a method for an automated assay, along with easy-to-use software, using the DAM2 system⁴. The assay was validated by comparing measures of heat tolerance in terms of TKD to a classic manual observation-based TKD assay across several factors. We also explored the locomotor activity component of TKD assays to further characterize the inducible thermotolerance phenotype. We named the assay and accompanying software HoTDAM! (Heat Tolerance assays using the Drosophila Activity Monitoring System). Here we provide a detailed description of the automated heat tolerance assay method using the DAM2 system and the HoTDAM! software. The assay is easy to use and is readily scalable to allow for the measurement of many organisms at one time.

In this manuscript, we performed TKD assays on thermosensory mutant flies (transient receptor potential ankyrin 1; TRPA1) and their genetic controls (white¹¹¹⁸; w¹¹¹⁸). These organisms were chosen to emphasize the importance of the characteristic increased activity seen during heat stress for the assay. Namely, TRPA1 organisms do not show this escape behavior, illustrating the intrinsic connection between conserved behavioral responses and estimates of heat tolerance, such as TKD. We performed the assay for both females and males, along with implementing a heat-hardening pretreatment. The representative results presented here are data from entirely new assays than those used in the original validation assays published previously.

Protocol

1. Fly husbandry

1. Choose the appropriate fly stock/line for the investigation.
   NOTE: To illustrate the assay, we used the transient receptor potential TRPA1 knockout stock and its genetic control, the w¹¹¹⁸ stock.
2. Maintain the fly stocks under consistent conditions as appropriate. To follow this protocol, house stocks at 25 ˚C under a 12:12 diurnal cycle on standard food (cornmeal, molasses, and torula yeast).
3. Separate males and females with light anesthesia.
   NOTE: Depending on the experiment, virgin or mated flies might be used.
   1. Allow adult males and females (separately for both stocks) to mate and oviposit for 5 days. Clear the adults from the bottles.
   2. As adults begin to eclose several days later, clear the bottles again. After 2 days, separate the males and females using light ether anesthesia and allow them to mature separately at a density of 25 flies per vial.

2. Pretreatment

1. Apply the pretreatment or independent variable that will define the experimental groups (genetic, environmental, pharmacologic, or otherwise).
   1. To follow this protocol, separate the adults by sex, and after 5 days, pretreat half of the adults by immersing a sealed vial containing the organisms in a water bath at 37 ˚C for 1 h. Keep the controls in the incubator at 25 ˚C. Allow the flies to recover for 24 h after pretreatment, prior to the heat tolerance assay.

3. DAM2 system setup

1. Load the flies into the DAM2 monitor tubes and cap the tubes at both ends with cotton. If the flies are to be assessed for heat tolerance immediately, do not use any anesthesia while loading the organisms into the monitor tubes to avoid any potential confounding effects associated with anesthesia exposure. Instead, aspirate individual flies from the holding vials into the monitor tubes.
2. Put the assay tubes into the activity monitors, taking note as to which slot numbers the different groups are loaded.
   NOTE: These may be randomized depending on the experiment if location within the monitor might be considered a confounding variable.
3. DAM2 data acquisition software
   NOTE: The DAM2 system and software are described thoroughly in the DAMSystem3 Software Data Sheet available on the company's website (see the Table of Materials). Refer to the DAM2 instructions for specific troubleshooting and general functionality. Here we provide guidance for using the system in the context of our heat tolerance assay.
   1. The DAM2 system monitors the number of times an individual fly within each tube breaks an infrared beam. The data acquisition software then indexes and resets this count every defined time interval. Under preferences, select the reading interval to be used in the assay. We have found a 15 s reading interval to achieve a good balance between resolution and reading errors.
      NOTE: It is recommended that the reading interval be no shorter in seconds than the number of monitors being used. A shorter reading interval will give better temporal resolution to the assay but will also potentially increase the instances of reading errors. The software can fall behind while trying to index the readings. In addition, a newer, faster computer goes a long way in preventing reading errors.
   2. While considering reading errors, ensure that the computer being used to run the DAM2 data acquisition software is set to never sleep or hibernate to not interrupt the assay. Further, ensure that auto-updates (including any institution-controlled network updates) are set to off while collecting data.
   3. Check that all monitors are connected and communicating with the software. Confirm that each monitor's status is green under the current data tab.
      NOTE: See the DAMSystem3 Software Datasheet for details on what problems different color codes refer to.
   4. The data acquisition software will automatically write the activity counts to text files in the Data folder within the system files. To make data analysis easier, delete any text files within this Data folder prior to starting the data acquisition software for any assay.
      NOTE: The text files will automatically populate, so it is permissible to remove them from this folder to clear out old data without impeding the program.

4. Heat tolerance assay

1. Load the monitors into the assay incubator.
2. Start the acquisition software and let the software index for a defined length of time (e.g., 40 indexes or 10 min if the reading interval is set to 15 s) before applying the heat stress.
   NOTE: This allows for some acclimation to the tubes and recovery from the movement of the monitors while setting them up. Especially if activity during the assay is being analyzed, this acclimation time will establish a baseline activity prior to the induction of heat stress. Depending on the specific nature of the assay being performed, this acclimation phase could be skipped, with the monitors placed directly into the incubator already at the stress temperature.
3. If performing a static (TKD) assay, set the incubator to ramp as quickly as possible to the set noxious temperature after the period of acclimation. The response variable will be operationalized as the last recorded time of movement (i.e., last non-zero index) to estimate TKD.
   1. If performing a dynamic assay, determine the rate at which the temperature will increase after the acclimation period. Here the response variable measured is technically time again. The time at which the last movement is recorded will coincide with a temperature (CTmax), depending on the rate of ramping.
   2. Monitor activity counts in real time on the DAM system display in the acquisition software or directly examine the text files in the data folder in the DAMSystem3 program files. Copy the text files and open the copy as opposed to the original file within the data folder to avoid causing any problems with live data recording.
   3. After no movement is seen in any of the flies for several minutes, stop the acquisition software.
      NOTE: With our assays, we have found that a few to several minutes of inactivity is indicative of physiological collapse due to heat stress similar to that seen in the classic manual TKD assays. This is, however, dependent on the spontaneous escape behavior. As such, consider possible confounding variables if the specific treatment in the investigation might alter this behavioral response. Further, depending on the specific parameters of the assay (e.g., temperature, treatment), the length of the assay will obviously vary.

5. Data organization and analysis

NOTE: See Supplemental Video S1 for a walkthrough of how to download the executable application from the GitHub, as well basic functionality of the software.

1. Once the data has been acquired, scan the text files for errors using the referenced software (see the Table of Materials) and select specific start and stop points for binning activity data. If a 10 min acclimation interval was implemented, start the binning 10 min into the activity recording. See the discussion for further details about binning.
2. Open the HoTDAM! analysis software and import the scanned monitor data files by clicking File | load monitor data.
   NOTE: The software and executable application are available from GitHub (https://github.com/MatthewR47/HoTDAM) or from the company’s website within Analysis Software section. Note that the executable application is only compatible with Windows operating systems at this time.
3. Add group designations to indicate which treatment groups correspond to which cell within the DAM2 monitors.
   NOTE: The layout of the software interface corresponds to the layout of the monitors.
4. Click Start Multi-Group Definition to pull up a dialog box to add a group designation that will be applied to multiple cells. Once the group designation is accepted, click on the cells to apply the group designation, then click on Stop Multi-Group Designation.
   NOTE: The software organizes and exports DAMSystem3 data to .csv files to be analyzed in statistics software.
5. Export the TKD (i.e., the last non-zero index) for each fly within the monitors to a .csv file by clicking File | Export Knockdown Data | Export All Monitors or Export Selected Monitors. TKD for each fly will be organized in the output by group designation.
6. Export activity data for each fly by clicking on File | Export Activity Data | Export All Monitors or Export Selected Monitors to a .csv file, keeping only the timestamp and count data, making the data file easier to work with, while also assigning designated group labels for each fly.
   NOTE: The first several columns in the DAMSystem3 data files correspond to internal data for the monitors (e.g., light monitoring or error messages) during acquisition. Count data are in columns 11-42 (see the DamSystem3 Data Sheet for details). Exporting the activity data with the analysis software will remove all columns but the timestamp and the count data columns.
7. If performing a CTmax experiment, use the TKD to determine the CTmax by using the rate of temperature ramping.
   
   NOTE: The analysis software was written in an object-oriented manner in C# (source code available on GitHub; https://github.com/MatthewR47/HoTDAM) such that aspects of the program can be easily modified to allow for customization to meet specific purposes.

6. Statistics

NOTE: Many different tests can be used to analyze the TKD data, depending on the specifics of the experimental setup.

1. Use survival analysis (e.g., Cox regression, Kaplan-Meier) to conceptualize knockdown as an event that each fly within the assay will experience.
   NOTE: For a detailed review, see Bradburn's and Clark's 4-paper series discussing survival analysis and its implementation^5,6,7,8.
2. Use ANOVA to assess group differences and compare modalities, especially when validating the automated assay for specific conditions.
3. Analyze the TKD data using Kaplan-Meier survival analysis. Split the file by stock such that the analysis is performed separately (e.g., for TRPA1 and w¹¹¹⁸ stocks lines). Choose the time variable as TKD (in min), knockdown as the event, pretreatment as the factor, and sex as the strata.
4. Perform the log-rank, Breslow, and Tarone-Ware tests to compare pretreatment for each stratum (i.e., sex), for illustrative purposes showing several methods.
5. Construct survival plots.

Results

The analyses for TRPA1 and w¹¹¹⁸ stocks were performed separately. Percentile TKD times and other descriptives can be found in Table 1.

			Percentilesa
Sex	Treatment		25.00%		50.00%		75.00%
		N	Estimate	Std. Error	Estimate	Std. Error	Estimate	Std. Error
W1118
Female	Control	16	42.50	0.43	40.75	2.00	38.75	0.43
	Hardened	16	48.75	4.55	43.25	2.50	38.50	1.95
Male	Control	16	43.25	1.88	39.75	0.25	37.00	2.38
	Hardened	16	46.50	5.63	35.50	2.00	30.75	3.03
TRPA1
Female	Control	16	34.25	2.17	29.25	7.50	21.50	0.72
	Hardened	15	37.25	4.84	28.75	1.77	25.75	0.86
Male	Control	15	38.25	3.29	32.00	1.29	24.50	7.92
	Hardened	16	27.50	0.72	25.75	1.00	22.75	6.06

Table 1: Descriptive statistics for Kaplan-Meier survival analyses of w¹¹¹⁸ and TRPA1 flies. Percentile TKD values are given in minutes. Hardened refers to 37 ˚C pretreatment followed by 24 hours recovery prior to heat shock at 39 ˚C. Controls were kept at 25 ˚C for the duration of the pretreatment. NOTE: In survival analyses, the 75^th percentile is the latest time at which 75% of the sample continues to display activity.

For the w¹¹¹⁸ flies, log-rank testing found a significant difference in the probability of knockdown between hardened and control females (p = 0.026), such that hardened females had a lower probability of being knocked down by the heat stressor over the course of the assay. Meanwhile, log-rank testing found no significant difference in the probability of knockdown between hardened and control w¹¹¹⁸ males (p = 0.798). For the TRPA1 females, no significant difference in the probability of knockdown was found between hardened and control organisms (p = 0.547) using log rank testing. Finally, for the TRPA1 males, log-rank testing found a significant difference in the probability of knockdown between hardened and control organisms (p = 0.014), such that control organisms had a lower probability of being knocked down by the heat stressor. See Table 2 for all results of the survival analyses (including generalized Wilcoxon and Tarone-Ware tests) and Figure 1 for survival curves.

Sex		χ2	df	p-value
W1118
Female	Log Rank (Mantel-Cox)	4.96	1	0.026
	Breslow (Generalized Wilcoxon)	1.72	1	0.190
	Tarone-Ware	3.07	1	0.080
Male	Log Rank (Mantel-Cox)	0.07	1	0.798
	Breslow (Generalized Wilcoxon)	0.86	1	0.353
	Tarone-Ware	0.32	1	0.572
TRPA1
Female	Log Rank (Mantel-Cox)	0.36	1	0.547
	Breslow (Generalized Wilcoxon)	0.45	1	0.503
	Tarone-Ware	0.36	1	0.549
Male	Log Rank (Mantel-Cox)	6.01	1	0.014
	Breslow (Generalized Wilcoxon)	4.19	1	0.041
	Tarone-Ware	5.20	1	0.023

Table 2: Significance testing comparing the probability of remaining active between pretreated and control w¹¹¹⁸ and TRPA1 female and male flies. Hardened refers to 37 ˚C pretreatment followed by 24 h recovery prior to heat shock at 39 ˚C. Control organisms were kept at 25 ˚C for the duration of the pretreatment.

Figure 1: Survival curves depicting the proportion of organisms with remaining activity over the course of the TKD assay for w¹¹¹⁸ and TRPA1 flies. Hardened refers to 37 ˚C pretreatment followed by 24 h recovery prior to heat shock at 39 ˚C. Control organisms were kept at 25 ˚C for the duration of the pretreatment. TKD is in minutes since the introduction of the 39 ˚C heat stressor. Abbreviations: TKD = time to knockdown; TRPA1 = transient receptor potential ankyrin 1. Please click here to view a larger version of this figure.

Average activity plotted against time can be seen in Figure 2 for hardened and control TRPA1 and w¹¹¹⁸ males and females.

Figure 2: Average activity versus time for w¹¹¹⁸ and TRPA1 flies for the duration of the TKD assay. Abbreviations: TKD = time to knockdown; TRPA1 = transient receptor potential ankyrin 1. Please click here to view a larger version of this figure.

There are likely numerable and novel ways to analyze the activity data, especially when considering group effects and incorporating TKD. Since activity analysis during heat tolerance is relatively unstudied, we did not explore potentially novel statistical approaches to analyze activity data. Such statistical testing would warrant a much more extensive literature review and discussion than is appropriate here. Instead, we simply graphed the average activity for each group (pretreatment or control) versus time, because we have previously published this method to illustrate activity with an appropriate literature review.

Supplemental Video S1: Walkthrough for downloading the executable application from GitHub and basic functionality of the software. The most recent release (only compatible with Windows at this time) can be found on the referenced GitHub (https://github.com/MatthewR47/HoTDAM) or through a indexed link on the Analysis Software section of the company's website. Once the release is downloaded, the files will need to be extracted and the HoTDAM! application can be opened. Antivirus software may prevent the application from running; permissions may need to be given to the application. Once interface is running, the scanned DAM2 (File Scan Software) can be uploaded (File | Load Monitor Data). The Group Designation interface represents the activity monitor layout and allows the user to designate treatment groups to tubes individually one at a time or multiple at once with the Multi-Select Group Definition feature. All monitors are mutually exclusive with regard to the Group Designation functionality and must be defined separately. TKD and activity data can then be exported to .csv files (File | Export Knockdown Data or File | Export Activity Data) for a selected monitor individually or all monitors at once. If all monitors are exported at once, the TKD or activity data will be exported to a single .csv file, but data for each monitor will be sesparated within the file. The exported TKD files will group flies into groups displaying the last activity non-zero index for each tube. Activity files will keep all tubes separate but will associate group designations and will remove all information from the files but timestamp and count data for each tube. Please click here to download this File.

Discussion

The heat tolerance assay method we describe here is versatile and scalable. We have previously published a validation study where we compared the HoTDAM! method to a classic, observation-based TKD assay and found the automated assay to give show the same general trend across several factors⁴ (Figure 3). In other words, in the same way as the classic manual TKD assay, the DAM2 automated assay was able to differentiate organisms by sex, assay temperature, hardening pretreatment, and recovery time following hardening pretreatment prior to the assay. While the automated assay is presumed to be more objective in that TKD is not reliant on researcher observation, our data would not suggest that this makes the DAM2 assay appreciably superior to the manual assay in terms of discovering an effect. We found the assays to be quite similar in terms of precision, and if anything, the effect sizes within the automated assay are slightly smaller (see Rokusek et al.⁴ supplemental material for detailed descriptive statistical and ANOVA data for both the automated DAM2 and the manual observation-based assays from our validation studies). The reason for this likely has to do with the inherent difference in what the assays are measuring in terms of TKD, which is discussed in detail below.

Figure 3: Comparison of the automated DAM2 assay to a classic observation-based TKD assay. Plots show mean TKD in minutes and error bars are the standard error of the mean. Independent factors compared are (A) sex by assay temperature, (B) hardening by assay temperature, (C) sex by recovery time, (D) hardening by sex, (E) assay temperature by recovery time, and (F) hardening by recovery time. Absolute TKD tends to be longer in the automated assay, but the general trends across factors are consistent between assays. This figure was taken from Rokusek et al. ⁴. Abbreviation: TKD = time to knockdown. Please click here to view a larger version of this figure.

The illustrative data presented here represents entirely separate experiments from our original verification trials, which also used different fly lines. The assay (TKD and activity) was sensitive to the effects of hardening pretreatment in the w¹¹¹⁸ stock (we used a Canton S wild type strain in the original experiments) providing support that basic functionality of the assay is stable under variable conditions, though only a few lines have been tested thus far. The assay is versatile in that it is not limited to TKD measurement. Since TKD in our assay is defined as the cessation of locomotor activity, the CTmax can be determined if the temperature ramping rate is known. Finally, the assay is entirely automated and can easily be scaled up by adding more activity monitors, allowing for much larger sampling sizes than would be feasible using a manual observation method.

Some key points to consider while performing the assay and analysis are as follows. Use an incubator that can rapidly reach a set temperature, such that the monitors can be loaded into the incubator at the rearing temperature and allowed to sit quietly to acclimate to the new environment prior to applying the heat stressor. If the temperature ramps too slowly, then the assay starts to resemble more of a dynamic CTmax assay as opposed to a static TKD assay. For example, if the software was allowed to index 40x prior to the induction of the heat stress to establish a baseline, the software can output the data file starting at index 40 for determination of TKD and data analysis. The starting point is in terms of the timestamp recorded by the software, so be aware of the index interval (e.g., 40 indexes is 10 min if the index interval is 15 s).

The File Scan Software can also be used to "bin" indexes and can either sum or average the counts. For example, when we graphed activity data (see below) we binned every four indexes together summing counts (i.e., we exported activity every minute as opposed to every 15 s). Note that the File Scan Software will not allow for partial minute start times when binning, in terms of the timestamp. For example, if the first index was at 12:10:15, with index interval set to 15 s, then binning at 1 min will start at the next nearest minute (e.g., 12:11:00). As such, if activity in minute increments is going to be used for analysis but the resolution of 15 s intervals for TKD is desired, start the acquisition software within 15 s of the nearest whole minute (e.g., sometime after 12:09:45 and before 12:10:00). This complication is due to the fact that the data acquisition software uses the computer system clock as the timestamp, as the DAMSystem3 was likely built with sleep studies in mind.

In terms of statistical approaches to TKD comparison, we argue that survival analysis offers more information than simply comparing mean or median knockdown times between groups. Survival curves can offer visual insight into the time course of knockdown for the populations during the assay. Similarly, statistical testing can be tailored to the specific question asked within the study. For example, generalized Wilcoxon tests place more weight on early events, while log-rank tests give equal weight to all time points and are more sensitive to differences at the later time points. If hazard ratios are not proportional (i.e., survival curves cross) Tarone-Ware is more rigorous. Since there should be no censoring of data as all organisms will have experienced the event (knockdown), an argument could be made for the use of non-parametric, rank ordering tests like Mann-Whitney U and Kruskal-Wallis along with the Kapan-Meier survival curves⁹. If an investigation is interested in knockdown after a defined length of time rather than TKD after all organisms have been knocked down, the use of survival analysis and the log-rank or generalized Wilcoxon tests would be robust to censored data (i.e., a scenario where not all organisms would have ceased moving or collapsed by the end of the assay). We provided the results from log-rank, generalized Wilcoxon, and Tarone-Ware in Table 2 to illustrate.

During heat stress and prior to CTmax, behavioral alterations (e.g., escape behavior) serve as coping mechanisms. These innate behavioral strategies are conserved across the animal kingdom but are especially important for ectotherms given their lower capacity for metabolic thermoregulation^10,11. Clearly, behavior is an important aspect of the ectotherm heat stress phenotype, but relatively sparse research has examined these behavioral aspects of heat tolerance. Given that the assay we describe relies on the characteristic increase in locomotor activity in response to heat stress, it is well suited to explore the locomotor aspects of heat stress responses. At the same time, this reliance on spontaneous activity carries with it some limitations. It is important to remember, especially when exploring components of locomotor responses to heat stress, that both TKD and activity are intrinsically related. To demonstrate a situation where this would be limiting for our assay, we provided data here from TRPA1 organisms that are deficient in thermosensation. When the organisms are deficient for the TRPA1 receptor, flies do not exhibit the characteristic locomotor response to heat¹². In the representative data shown here, the activity response in the TRPA1 organisms is blunted. The TKD measurements would not be easily comparable to wild type organisms since the measure represents a different locomotor response.

Generalizability and direct comparison of results, especially to the classic heat tolerance assays is also a potential limitation (Figure 3). Often, observation-based assays will involve a mechanical or other stimulus to ensure that an immobile organism is indeed experiencing physiological collapse and not simply showing a lack of spontaneous activity^3,13. Since our assay relies on spontaneous activity, there is an inherent difference in the operational definition of TKD that could complicate the comparison. In other words, we have defined TKD as the cessation of locomotor activity, while classical assays generally define TKD as physiological collapse. Further, automated assays like the one we describe rely on heated air, while manual assays generally involve a water bath and, thus, more efficient heat transfer. All these limitations to the comparison between assay modalities represent downsides to the generalizability of the automated method. At the same time, observation-based methods have been reported to suffer from complications with direct comparisons between investigations as well^2,14,15. We argue that despite the limitations, automated assays are a viable means to estimate heat tolerance in terms of TKD or CTmax.

Other automated heat tolerance assays for TKD and CTmax have been implemented and described previously within the literature^16,17,18,19, with some utilizing the DAM2 activity monitors^18,19. Further, at least a few investigations have utilized video-based methods^16,20,21, as well as the DAM2 activity monitors²⁰, to examine fruit fly activity profiles as they relate to heat tolerance within the context of acute heat stress. As such, automating heat tolerance assays, including with the DAM2 system, is not a new idea. What is novel and of value in our assay is the ease of data management as facilitated by the HoTDAM! (analysis) software. Activity data are difficult to work with, and it can be time-consuming and cumbersome to pull even the simplest of measures, (e.g., TKD) from the large datasets without the use of software or scripts that need to be custom-made for each scenario. HoTDAM! is free and available as a fully functional executable application with a simple and easy-to-use interface. No prior experience in coding is necessary. Further, the software was written in an object-oriented manner with documentation to facilitate modification. As such, HoTDAM! can be easily customized to fit specific needs. The source code is available alongside the executable application. It is our hope that HoTDAM! can serve laboratories new to heat tolerance assays by offering an easy means to measure TKD and CTmax, as well as serve laboratories experienced with heat tolerance assays by offering customizable software to facilitate data management.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
14 mL polystyrene test tubes	Falcon	352057
30 gallon fish tank	Wal-mart
8 oz bottles	Genesee	32-129F
Constant Climate Chamber	Memmert	HPP750eco
cornmeal	Lab Scientific	FLY801010
DAM2  Drosophila Activity Monitor	TriKinetics	DAM2	(DAMSystem3 Data Sheet) https://www.trikinetics.com/Downloads/DAMSystem%20Price%20List%202024.7.pdf
DAMSystem data acquisition software	TriKinetics		free download
Drosophila agar	Lab Scientific	FLY80201
ethanol	Fisher Scientific	BP82011
Ether	Fisher Scientific	E134-4
FileScan software	TriKinetics		for scanning for text errors, binning data, and output
FlyStuff Flugs for bottles	Genesee	49-100
FlyStuff Flugs for vials	Genesee	49-102
FlyStuff vials	Genesee	32-113RL
HoTDAM software	Github or Trikinetics		https://github.com/MatthewR47/HoTDAM
Immersion circulating heater	PolyScience	MX-CA11B
molasses	Lab Scientific	FLY80084
propionic acid	Fisher Scientific	A258-500
Pyrex Glass tubes 5 x 65 mm for DAM2	TriKinetics	PGT 5x65	https://www.trikinetics.com/Downloads/DAMSystem%20Price%20List%202024.7.pdf
small paint brush	Wal-mart
SPSS Statistics	IBM
tegosept	Lab Scientific	FLY55015
torula yeast	MP Biomedicals	290308505
TRPA1 mutant stock	Bloomington Stock center	26504	w[1118]; TI{w[+mW.hs]=TI}TrpA1[1]
w1118 stock	Bloomington Stock center	3605