Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Summary

Electrocardiogram (ECG) is the key variable to understanding cardiac electrophysiology. Physical exercise has beneficial effects but may also be harmful in the context of cardiovascular diseases. This manuscript provides a method of recording real-time ECG during exercise, which can serve to investigate its effects on cardiac electrophysiology in mice.

Abstract

Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. However, in certain cardiac diseases such as inherited arrhythmia syndromes, e.g., arrhythmogenic cardiomyopathy (ACM) or myocarditis, physical exercise may have negative effects on the heart leading to a proarrhythmogenic substrate production. Currently, the underlying molecular mechanisms of exercise-related proarrhythmogenic remodeling are largely unknown, thus it remains unclear which frequency, duration, and intensity of exercise can be considered safe in the context of disease(s).

The proposed method allows to study proarrhythmic/antiarrhythmic effects of physical exercise by combining treadmill training with real-time monitoring of the ECG. Implantable telemetry devices are used to continuously record the ECG of freely moving mice over a period of up to 3 months both at rest and during treadmill training. Data acquisition software with its analysis modules is used to analyze basic ECG parameters such as heart rate, P wave duration, PR interval, QRS interval, or QT duration at rest, during and after training. Furthermore, heart rate variability (HRV) parameters and occurrence of arrhythmias are evaluated. In brief, this manuscript describes a step-by-step approach to experimentally explore exercise induced effects on cardiac electrophysiology, including potential proarrhythmogenic remodeling in mouse models.

Introduction

Regular physical activity is important for a healthy life. Certain cardiovascular conditions, however, lead to situations where this common-sense agreement is at least questionable. In patients with myocarditis, current data even shows adverse effects of exercise and, thus, it is recommended to pause all exercise for a certain period in these patients^1,2,3. In other cardiovascular diseases (CVD) such as inherited arrhythmia syndromes comparatively less evidence on the appropriate level of exercise exists^4,5,6,7, making clinical counselling in these cases, mostly for young and physically active patients, very challenging.

Adverse remodeling leading to reduced contractility and heart failure and proarrhythmogenic remodeling leading to arrhythmias and sudden cardiac death have been suggested as hallmarks of exercise-associated harmful effects on the heart⁸. A large number of studies indicate beneficial effects of moderate exercise over a broad spectrum of different diseases^9,10. Extensive training, however, may have detrimental effects on the heart leading to arrhythmias especially in otherwise healthy athletes¹¹. Although structural remodeling processes leading to a vulnerable proarrhythmic substrate production may underlie this paradox situation as demonstrated in marathon runners¹², the specific mechanisms of exercise-related adverse remodeling both in healthy people and in patients with cardiovascular diseases remain largely unknown.

In animals, especially in mice, several suitable models have been developed to mimic a broad range of cardiovascular diseases^13,14. Also, various exercise models and training protocols have been established in mice^15,16,17, including motorized treadmill training, voluntary wheel running (VWR), and swimming^17,18. Evaluation of cardiac electrophysiology by ECG monitoring classically depends on a direct conducting connection between the animal and some sort of detection device. Thus, either animals need to be anesthetized, e.g., to obtain ECG recordings using sharp electrodes¹⁹, or animals need to be immobilized by a restrainer ²⁰, or data quality is reduced due to motion artifacts, e.g., when using paw-electrodes²¹ or conductive platforms²² allowing only basic analysis. Thus, none of the above-mentioned approaches are compatible with training protocols and consequently prevent studies on exercise-related mechanisms leading to adverse remodeling in mice. Implantable telemetry devices can overcome these hurdles and are nowadays the most powerful tool and gold standard to evaluate murine electrophysiology in vivo in conscious and moving animals^23,24. Current telemetry hardware solutions have been developed to monitor mice in their cages^25,26, and commonly require a receiver to be placed underneath the cage for data acquisition, thus making real-time monitoring outside these circumstances challenging. Here we provide an approach to investigate the effects of exercise on cardiac electrophysiology and arrhythmogenesis by real-time ECG recording during treadmill training in mice using implanted telemetry devices. All parameters obtained were analyzed as previously described by Tomsits et al.²³.

Protocol

All animal procedures were conducted in accordance with the guidelines of the Animal Care and Ethics committee of the University of Munich and all procedures were approved by the Government of Bavaria, Munich, Germany (ROB-55.2-2532.Vet_02-16-200). Four male wildtype in-house bred C57BL/6N mice were used in this study.

1. Preparation and surgical implantation of the transmitter

NOTE: For a detailed protocol of transmitter preparation and implantation refer to McCauley et al.²⁶.

1. Preparation of the transmitter
   1. Use new transmitters directly as these are sterile. If transmitters are reused, clean the device by placing it in saline to get rid of bloodstains, remove any fragments of tissue adhering to the transmitter and the lead electrodes. Following the initial cleaning, if necessary, submerge the transmitter in a 1% cleaning solution (see Table of Materials) for 4 h to further clean the transmitter.
   2. Activate the transmitter by placing the supplied magnet in close proximity. Following activation, test the signal from the transmitter using a radio device at 530 Hz AM frequency. A sharp and clear beep indicates the transmitter is activated, whereas an inactivated transmitter does not give any signal.
2. Surgical preparation and implantation
   NOTE: All surgical procedures must be carried out under clean and sterile conditions.
   1. Disinfect all surfaces and reusable equipment prior to use and use sterile disposables, e.g., gauze, gloves, etc.
   2. Prepare the leads of the transmitter by shortening to optimal lengths, the negative (white) lead to approximately 3.5 cm and the positive (red) lead to 2.5 cm. Remove the red and white insulation sheath on the tip of the electrodes by making a small cut to expose 5-7 mm of the conducting wire.
      NOTE: These lengths are suggested for 9-12-week-old BALB/c or C57BL/6 mice, weighing ~25 g. Adjust if the animals used in the study are larger/heavier.
   3. Note the weight of the transmitter and the body weight of the mouse. Also, note the serial number and calibration values of the transmitter provided by DSI.
      NOTE: The body weight of the animal is used to calculate doses of anesthetics and analgesics. The initial body weight is also used as reference to evaluate animal recovery post-surgery.
   4. Anaesthetize the mouse in an induction chamber connected to an isoflurane vaporizer flushed with 2%-3% isoflurane (vol/vol) driven by 1 L/min of 100% oxygen. Wait for full narcosis onset and check toe-pinch and lid reflex to ensure proper depth of narcosis before proceeding.
   5. Next, place the anaesthetized animal in a supine position and use ointment (see Table of Materials) to prevent eye dryness during the procedure. Carry out the surgical procedure under clean conditions on a surgical suite to maintain the body temperature of the mouse at 37 °C. Insert a rectal probe as temperature sensor.
   6. Maintain the anesthesia by continuous isoflurane (1.5%-2%) application. Inject fentanyl (0.50 µg/g) intraperitoneally for analgesia. Connect an adsorber to the ventilation setup to avoid excess gas to escape into the operating room (recommended).
   7. Insert needle ECG electrodes in both arms and the grounding electrode into the left leg of the mouse to obtain a lead I ECG configuration to monitor the ECG during surgery and to obtain baseline ECG.
   8. Shave the abdomen and chest and disinfect the area of surgery using chlorhexidine/alcohol (see Table of Materials). Use tweezers to tighten the skin and perform a 1.5-2 cm ventral midline abdominal incision using scissors (laparotomy).
   9. Make a subcutaneous pocket (ca 1 mm) in the upper-right chest and lower-left chest below the heart for placing the electrode leads, as shown in Figure 1.
   10. Place the transmitter body gently in the peritoneum above the intestine. Insert a 14 G needle subcutaneously from both pockets in the upper-right chest and lower-left chest pocket made earlier to create a tunnel for electrode positioning.
   11. Guide the red and white electrodes through the needle to place them in a lead II configuration. Position and fix the electrode tips with 6.0 sutures, positive electrode (red) in the lower-left chest and the negative electrode (white) in the upper-right chest.
   12. Suture all the incisions using 6.0 sutures and apply disinfectant (see Table of Materials) on the wounds. Move the animal to a recovery cage (only one animal/cage) and place it under a heat source to maintain body temperature until full recovery of narcosis. Only after full recovery and the ability to maintain sternal recumbency the animal may be placed back into company if required.
   13. Provide the animal with a sufficient dose of analgesics and antibiotics post-surgery. Use carprofen (5 µg/g) as analgesic and enrofloxacin (5 µg/g) as antibiotic. Monitor the wound at regular intervals to ensure there is no inflammation or wound dehiscence occurrence.
   14. After 7-10 days of post-surgery recovery period, the animal is ready to undergo treadmill training. Ensure that wounds are properly healed, and the mouse is healthy before starting the training.
       ​NOTE: After finalization of the experimental period, the use of telemetry transmitters does not require a specific euthanasia method. The choice of method depends on subsequent analysis and its specific requirements for tissue condition as well as on local animal care rules and regulations and approval of the respective local ethics committee.

2. Data acquisition

1. Prearrangements
   1. To start data acquisition, place the animal cage on the signal receiver. Connect the signal receiver to the data acquisition system consisting of a data exchange matrix and a signal interface. Connect the data acquisition system to a computer with the acquisition software for data visualization (see setup details in Figure 2A).
   2. Start the software and confirm username and license on the following screen, and then click Continue. Click on Hardware to set up the transmitter and signal receiver device. Select Edit Physio Tel/HD (MX2) Configuration to open a configuration window.
   3. Select MX2 Configuration in the list view of the configuration tab to see all the available transmitters and their serial numbers in the available column. Click-and-drag the implanted transmitter from the available column to the selected column.
      NOTE: If a transmitter is listed in the selected column, it is also added to the MX2 configuration in the configuration tab on the far left.
   4. Colored icons next to the transmitter's serial number indicate the status. Check status for all transmitters: green with checkmark = transmitter is synchronized and ready; red with exclamation mark = transmitter currently not available, e.g., is currently configured in an experiment on another system; yellow = transmitter is synchronizing or does not have any receivers connected. Make sure there is green light indicating nominal data transfer.
   5. To configure the transmitter, select the serial number of the added transmitter and click on Create New Implant. Select ETA-F10 from the dropdown menu of the implant model to see implant details.
   6. Select the model and serial number of the receiver from a far-left menu of the receiver(s) associated with the implant. A list of plugged and connected receivers appears under this menu with a checkbox.
   7. Click on Search for ETA Implant to assign a signal receiver to the implanted transmitter. Open the signal type menu and select ECG with a sample rate of 1,000 Hz. Enter the calibration values on the back of the implant's packaging. Select Save & Exit.
   8. Click on Setup in the menu bar and select Subject Setup. A dialog box with subject details will appear. Enter the desired file name, which will be saved in subject setup.
   9. Select the gender of the animal and select Mouse from the drop-down menu of species. Open the analysis drop-down menu and select ECG (module). Change default labeling to ECG and units to mV if desired. Select the Trigger adjacent to ECG.
   10. Click on ECG under the subject name within the far-right menu to open the channel details menu. Select the desired ECG Parameters such as Num (Cycle number), HR (Heart rate), or intervals such as PR-I, QT-I, RR-I, QRS, etc. from the parameter list.
   11. To set up the display, click on Setup in the menu bar and select Experiment Setup. A setup dialog box appears. Select the Graph Setup from the far-right menu to define up to 16 graphical windows providing both raw data, e.g., ECG signals and derived parameters, e.g., XY loop, HR trend. To display the ECG, select the Enable Page checkbox for page 1.
2. Treadmill training with simultaneous real-time ECG recording
   1. Prepare an experimental setup as shown in Figure 2B for a 2-lane treadmill with real-time ECG monitoring during training.
      NOTE: A 5-lane rodent treadmill (see Table of Materials) for training is recommended. The setup consists of a conveyor belt divided into five running compartments and a control unit with touchscreen. Each running compartment is formed by a transparent Plexiglas box with a lid, mounted on the conveyor belt. Each compartment has an electric shock grid where short electrical pulses act as stimulus to keep the animal running. Each compartment is individually connected to the control unit to enable compartment specific adjustment of shock intensity. The control unit can display the distance run, the number of shocks and the total duration of shocks. Since all compartments share the same conveyor belt, the speed and inclination can only be adjusted for all compartments at the same time.
   2. To enable good signal transduction during training, place the signal receiver on top of the box establishing the running lane with the animal as shown in Figure 2B. The exact position of the signal receiver on the running lane differs between individual animals due to different signal/noise ratios.
      1. Move the signal receiver until the optimal position on the running lane is found. Do so by running a test experiment with an animal under training and note the position with best signal/noise ratio. Use this optimal position for the actual experiment.
         NOTE: Due to the size of the signal receiver and placement of the receiver normal to the axis of the running lanes (as shown in Figure 2B), only two animals can train at the same time with ECG monitoring in this configuration.
   3. Divide the treadmill training into the following two phases.
      1. Acclimatization phase: time during which the animal is adapted to the training conditions. Perform a 1-week acclimatization protocol as shown in Table 1 with the speed of running and the time of training for each day as described.
      2. Training phase: Post acclimatization train the animal at a fixed speed for a fixed time per day for a total of X days. For this protocol, perform a 5-day training regime over 3 weeks with constant speed of 25 cm/s and a duration of 60 min/day (Table 2). After 5 days of training, provide a 2-day break before the next week of training.
         NOTE: X defines the total number of days of training and is defined based on the experimental objective.
   4. Switch on the treadmill. Set the treadmill slope, speed, and shock intensity according to the training protocol. Use an upward slope of 5°, which leads to a moderate level of stress (recommended). Use the same inclination for the acclimatization phase and training phase.
      NOTE: The inclination of the treadmill defines training intensity; choose the desired inclination. The training protocol can vary based on the experimental objective.
   5. Press on Settings in the control unit and select Grid Test. This opens a grid size selection screen. Select Mice. A Grid test screen will appear with two sub-tests: shock test and cleaning test. Press on Start to begin the shock test. A message warning the user of test shocks will appear. To begin the test, confirm the warning by touching the screen.
   6. Place the conductive part of the sponge accessory provided with the treadmill on the grid of the treadmill. Place it until the word Pass appears on screen. Test all grids like this. The test will end automatically after all lanes successfully pass it but can be stopped anytime by the user by pressing the Stop button.
   7. To continue with the cleaning test, press the >> button and Start and wait for the test to run. This test will also stop automatically as soon as all lanes have passed it. If the test fails, a warning message will appear on screen. Touch the message to see the result.
      NOTE: These tests are done to check cleanliness and function of the grid. Grids must be clean to ensure good animal detection and subsequently correct delivery of the electrical stimulus if needed. If the test fails, clean the grids, check whether all the cables are connected properly and repeat the test.
   8. Transfer the animal to the running compartment. Place the signal receiver on the transparent box and connect the signal receiver via the connecting cable to the data acquisition system, which consists of a data exchange matrix and a signal interface, which in turn connects to a computer with the acquisition software running to view the ECG signal during the experiment.
   9. Press Start to enter the running mode. Animals will receive a short electrical impulse when in contact with the electric grid, which will forward the animal toward the running lane. Use minimal shock intensity of 0.1 mA. This is sufficient to motivate the animals but is not visible in the ECG recording. Try placing food pellets outside the running lines within the view of the animal to keep it motivated.
      NOTE: The range given by the manufacturer for electrical shocks is 0.1 mA-2 mA. Increase in shock intensity may be necessary in different mouse strains or under different experimental conditions, nevertheless, we do recommend using the lowest shock intensity possible. Alternatively, to reduce overall electric shocks, try to keep the animal on the running lane by gently pushing it, e.g., with cotton ear buds or by stimulating it with a gentle puff of compressed air. If animals are trained well, the electric grid and the running lane can be separated by a piece of Styrofoam to avoid unwanted shocks.
   10. If an animal does not train and cannot be motivated even with electric shock, remove it from the training protocol for that day if there is no amelioration within the first 15 min of the experiment.
   11. Upon completion, allow the animal to rest for 5 min post-training before transferring it back to the cage. Remove the signal receiver from the transparent box and place it back underneath the cage as shown in Figure 2A. Turn off the treadmill to avoid any unwanted shocks.
   12. Clean the treadmill belt, the running compartments, and the electric grid with non-alcoholic cleaning agent. Clean lanes lead to better training results.
       ​NOTE: During training, it is important to constantly clean the lanes, as animals stop running on dirty lanes. We use cotton ear buds to get rid of animal feces while training.

3. Data analysis

NOTE: Depending on the individual research aims, various parameters can be obtained and analyzed. This protocol focusses on two aspects: analysis of quantitative ECG traits and the occurrence of arrhythmias before, during, and after training using an approach previously described by Tomsits et^al.23; and analysis of heart rate variability (HRV)²⁷.

1. ECG analysis
   1. For a detailed description, refer to Tomsits et al.²³. In brief, start the software, confirm the username and serial number of the software license, and click on Continue.
   2. To open a file with the extension. PnmExp, click on Load Experiment. The browse for folder dialog opens, select the file, and click on Open.
   3. Go to Actions/ Start review in the toolbar and select the Load Review Data dialog box, which provides an overview of all the subjects and their recorded signals within the previously selected experiment.
   4. Select the file to analyze by clicking on the checkbox next to its name in the Subjects panel on the left side of the screen. To analyze the ECG, select the checkbox next to ECG in the signal types panel.
   5. Select either the entire recording or define a range or duration using the time range option. Click on OK to load the selected data set into review and windows for events and parameters open automatically.
   6. To display the ECG, click on Graph Setup in the menu toolbar to open a new window. Select Primary in signal type, enter Time 0:00:00:01, and then select the desired Labelling, Unit of display, and Low and High axis limits by entering the respective text boxes. Confirm by clicking on the Enable Page checkbox and the defined ECG tracing window appears.
   7. Adjust X-axis and Y-axis dimensions of the ECG by double clicking. Left click into the trace to show wave annotation and recognize and annotate each segment of the trace, P, Q, R, T wave, correctly.
      NOTE: If annotations are not correct, several options, QRS, PT, Advanced, Noise, Marks, Notes, Precision, can be used to optimize, e.g., the Analyze/ Attributes option using the right click. For a detailed description refer to Tomsits et al.²³.
   8. Select the required ECG parameters from the parameter window and copy to a spreadsheet or a statistics software for further analysis.
2. Arrhythmia detection
   1. For arrhythmia detection, click on Experiment/Data Insights to open a new data insight window.
   2. Define customized search rules to screen the recording in the search panel. Create a new search by selecting Create New Search after a right click within the search list.
   3. In the dropdown menu of the entry dialog, define the respective search rule and click on OK to add this search rule to the list. To apply search rules, click and drag them to the channel of interest on the left.
   4. In the results panel, each section within the ECG recording to which the rule applies is displayed. For a detailed overview on different search rules, refer to Tomsits et al.²³. For two exemplary rules, bradycardia and tachycardia, see the definition and description below.
      ​NOTE: For these search rules, murine physiologic heart rate is defined according to Kaese et al.²⁸ as 500-724 beats/min, corresponding to a cycle length of 82-110 ms.
      1. Bradycardia: In a two-step approach, identify every individual RR interval longer than 120 ms. Since bradycardia requires more than a single elongated RR interval, define an additional search rule to only identify 20 consecutive RR intervals longer than 120 ms as bradycardia as follows: Bradycardia-single as Value (HR_cyc0) <500, and Bradycardia as Series (Bradycardia-single, 1) >=20. Click on OK to add this search rule to the list.
      2. Following the same approach for tachycardia, define Tachycardia-single as Value (HR_cyc0) >724, identifying every individual RR interval that is shorter than 82 ms, and then add the additional search rule Tachycardia as Series (Tachycardia-single, 1) >=20. Click on OK to add this search rule to the list.
3. Heart rate variability analysis
   NOTE: The heart rate variability (HRV) analysis is not done in the acquisition software and requires exporting data from the acquisition software in a readable format. Here, we provide a short step-by step guide for data export in the widely used European data format (EDF).
   1. Start the software, confirm username and serial number, and click on Continue.
   2. To export the ECG trace for e.g., HRV analysis, click on Experiment and select Export to EDF. In the Export to EDF window, select the animal number, check ECG, select a time range for which data will be exported and click on Export.
      NOTE: There is no limit to the exported time range set by the software, more data will just take longer to process. It is also possible to split up exports into sections, e.g., 24 h and reintegrate them at a later timepoint if needed.
   3. Start the analysis software used for HRV analysis (see Table of Materials), click on File and select Open to load the desired EDF file.
   4. Click on HRV and select Settings. This will open a window to set various parameters. Under beat detection, select the species for which HRV analysis is done. Selection of the species will set the values for histogram bin width, pRR threshold and SDARR averaging value within the Analysis panel to a predefined standard.
   5. Select HRV and choose Report View. Copy the results to a statistics software for further statistical analysis.
   6. Signal quality can be significantly lower during training phases. If so, manually select cycles with visible P and QRS for subsequent analysis. Exclude bad data marks and data marks without clear P waves from the analysis. Do this under the careful consideration of an experienced ECG analyst to avoid eliminating good data points.

Representative Results

Depending on individual research objectives, subsequent analysis of obtained telemetry data will differ widely. Here, we demonstrate feasibility of the method by obtaining good quality data recorded during training periods and provide exemplary results of ECGs and heart rate variability analyses before, during, and after training. Data are presented as mean ± standard error of mean (SEM), all statistical analyses were conducted with a suitable statistical software (see Table of Materials). Statistical significance was assessed by the student's t test. The QT interval is corrected as previously discussed by Roussel et al. using the formula QTc = QT / (√(RR / 100))²⁹.

Successful telemetric ECG recording during training
With this protocol, it is possible to obtain ECG data with clear P, Q, R, S, and T waves in animals during training as demonstrated in Figure 3.

All the measurements from one animal were taken from the same day. Baseline measurements were taken at 10 am ± 10 min before training when animals were still in their permanent housing. Measurements during training were taken from the middle of the 60 min training session ± 10 min on day 3 in the third week of training, post training measurements were taken from the 5 min resting period after training and before retransfer to the permanent housing and recovered measurements were taken 1 h after training ± 10 min. Suitable sections of the ECG tracing for analysis were chosen manually from these defined sections with regard to the readout, e.g., 40 consecutive cycles for data presented in Figure 4.

Evaluation of ECG-derived parameters
Data are used to analyze physiologic changes before, during, and after exercise as shown for one example animal in Figure 4. Heart rate (Figure 4A), PR interval (Figure 4B), QRS duration (Figure 4C), and QTc interval (Figure 4D) are evaluated by averaging 40 consecutive ECG cycles. Heart rate increases to around 800 bpm when the animal is exercising and gradually recovers toward baseline after training. PR interval, QRS duration, and QTc intervals shorten under stress and once stress is over, return to baseline. Exemplary data from one animal are shown.

Detection of tachycardia
Search definitions were used as described in step 3.2.4 for detection of tachycardia and bradycardia episodes. Figure 5A shows sinus rhythm at baseline. A representative trace of sinus tachycardia during training is shown in Figure 5B. Exemplary data from one animal is shown here.

Data quality assessment by evaluation of heart rate variability parameters
HRV analysis is done as described in step 3.3. 5 min sections for HRV analysis is presented in Figure 6. Figure 6A shows the heart rate of a single animal over the course of an experiment. Heart rate increases during training and gradually returns to baseline post training, this trend can also be visualized by the median RR interval as shown in Figure 6B. Figure 6C shows comparable standard deviation of RR intervals (SDRR) obtained at baseline and during training by automated RR annotation, demonstrating data quality. Data obtained is from three mice. The SDRR is the standard deviation of all interbeat intervals (IBI) and is calculated automatically by the software as positive square root of the IBI variance around the mean IBI using the formula:

σ_x =

Figure 1: Schematic illustration of the telemetry transmitter and lead positioning. Mouse is in supine position; the transmitter is placed intraperitoneally, and leads are fixed subcutaneously in a lead II configuration. Created with Biorender. Please click here to view a larger version of this figure.

Figure 2: Experimental setup. (A) Setup for ECG recording using implantable telemetry before and after training with the signal receiver being kept underneath the animal cage. (B) Setup for real-time ECG monitoring during treadmill training. For optimal signal quality, the signal receiver is placed on the transparent box. Created with Biorender. Please click here to view a larger version of this figure.

Figure 3: Representative ECG during training. Normal sinus rhythm, P-wave, QRS, and T-wave are indicated using capital letters, RR-interval is marked with a bar. Please click here to view a larger version of this figure.

Figure 4: Telemetry over time. Trend graphs show representative results for (A) heart rate (BPM). (B) PR interval (ms). (C) QRS duration (ms). (D) QTc Interval (ms) before (baseline), during (training), immediately after training (post training), and after full recovery (recovered). Data is obtained from one animal by averaging 40 consecutive ECG cycles. Please click here to view a larger version of this figure.

Figure 5: Representative ECGs before and during training. (A) Sinus rhythm before training. (B) Sinus tachycardia during training. Data is from one animal. Please click here to view a larger version of this figure.

Figure 6: Data quality assessment by HRV analysis. (A) Representative heart rate trend of a single animal before (baseline), during (training), and after (post training) exercise. (B) Median RR interval before (baseline) and during training (training) and after full recovery (recovered), shown as mean ± SEM, unpaired Student's t-test, ***p < 0.001. (C) SDRR before (baseline) and during training (training) and after full recovery (recovered), n = 3, shown as mean ± SEM. Please click here to view a larger version of this figure.

5-day acclimatization phase
Day	Speed (cm/sec)	Time(min)
1	16.7	10
2	18.3	20
3	20	30
4	21.7	40
5	23.3	50
Remark: 2 min rest intervals after every 15 min

Table 1: Training regime during acclimatization phase.

5-day training phase
Day	Speed (cm/sec)	Time(min)
1	25.0	60
2	25.0	60
3	25.0	60
4	25.0	60
5	25.0	60
Remark: 2 min rest intervals after every 15 min

Table 2: Training regime during training phase.

Discussion

Current guidelines recommend regular physical activity as it has been demonstrated to be an important modifier of cardiovascular risk factors³⁰. There is also a growing body of evidence that moderate physical activity may protect against atrial fibrillation (AF) both in primary and secondary prevention^31,32,33. On the contrary, endurance athletes such as marathon runners have a higher risk to develop AF indicating that endurance training may also have negative effects^34,35. Such a U-shaped relationship between arrhythmia risk and training intensity has been clearly shown for AF in otherwise healthy athletes^9,36,37,38 and in patients with underlying heart disease, however, only little is known regarding training intensity and arrhythmogenesis^4,5,6,7.

To overcome this limitation and to improve patient care, further research on exercise-related effects on cardiac electrophysiology is warranted. To investigate fundamental mechanisms and molecular/cellular adaptations in response to training different models in a number of animal species have been developed¹⁵. Given the immanent benefits but also limitations of every model/species, researchers have to choose the most suitable one for each individual research question; regarding electrophysiology and arrhythmia research mouse^13,14,39,40 and pig models are widely used^{13,14,41,42,43}. Although training protocols using a motorized treadmill have been developed in pigs, there are a number of significant challenges including (i) the pigs' sedentary behavior, which requires a time- and labor-intense conditioning prior to the experiment as well as stimuli to keep pigs compliant during experiment and (ii) the body size and weight, which may prevent training in older pigs or training over long periods of time^15,44. In mice, several exercise protocols have been developed including motorized treadmill training, VWR, or swimming^17,18. Although VWR mimics the natural running pattern in rodents and is less stressful compared to forced exercise methods such as swimming and treadmill training, it also has certain disadvantages⁴⁵. The spontaneous nature of VWR does not allow controlling intensity, duration, or frequency of exercise thus preventing well-controlled experiments. In swimming models, the duration and intensity of training can be easily regulated, the necessary equipment is simple and available at low costs, and the method can be established in most research labs⁴⁶. Despite these advantages, studying electrophysiology in a swimming model is difficult as there is currently no option to monitor the ECG during swimming. The approach described in this protocol combines an implantable telemetry system with a treadmill exercise model and thus, overcomes the limitations of other training models in the context of electrophysiology research^47,48. Using a treadmill allows to control various exercise conditions such as intensity (slope inclination and running speed) or duration. In addition, different training protocols can be studied including endurance exercise training, interval training, and acute exercises. Following this protocol, it is now also possible to record and monitor the ECG using implantable telemetry transmitters while the mouse is running on the treadmill.

Given that mice usually run willingly for only a few minutes, stimuli such as tapping their back with little sticks, blowing puffs of compressed air or electrical stimuli are necessary. These stimuli, however, can induce psychological stress, which can affect the quality of experimental data significantly. Hence, we tried to minimize these stress factors by letting the mouse adapt to the treadmill during an acclimatization phase, with a steady increment of speed and using minimum-to-zero shock intensity as previously described^15,17,45.

In general, when recording ECGs, motion artefacts are a major issue, especially during physical activity. Following our proposed protocol, researchers will be able to acquire ECG signals in good quality allowing to clearly distinguish and annotate P, Q, R, S, T (Figure 3). Thus, various ECG parameters such as heart rate, heart rate variability, PR interval, QRS duration, or QT duration can reliably be assessed before, during, and after the training using automated software algorithms. Also, arrhythmias such as tachyarrhythmia, bradyarrhythmia, or pauses can be detected. As heart rate variability analyses-usually performed to investigate the effects of the autonomic nervous system on the heart^27,28-depend on sufficient R-wave annotation, data quality can be verified by similarly low SDRR values obtained at rest and during training by automated annotation as shown in Figure 6.

As every experimental technique, this method does not come without pitfalls and contains several critical steps. Sterile conditions and a short operation time are requirements for successful transmitter implantation, proper wound healing, and fast animal recovery post-surgery. Sutures must not be too tight, or they will cause skin necrosis. In general, the surgical procedure requires practical experience, and results will improve over time. Lead positioning influences the recorded main vector, best results are obtained with a steep lead two position, as it results in higher P- and R-wave amplitudes, which in turn are critical requirements for later ECG analysis. Training mice can be challenging as not all animals train willingly. A well-designed acclimatization protocol, including introduction to the treadmill environment, slow increments in conveyor belt speed and positive enhancement of good training behavior, e.g., with food pellets, can help to condition the animals to train better and to reduce the need for potentially interfering stimuli during the experiments. It is important to reduce all stimuli to the absolute minimum as they may affect data quality. However, the most critical step is the optimal positioning of the telemetry receiver during the treadmill training as it directly determines the quality of the data obtained. The receiver position must be determined for each pair of animals training at the same time, as it varies depending on the exact position of telemetry device and leads as well as on the individual animals running pattern. The position is found by trial and error, visually judging the signal quality in real time. All ECG traits to be analyzed must be clearly visible before experiments can start. Given the high murine heart rate, many data points accumulate even with short recording periods. This and the overall low signal amplitude, naturally leading to a lower signal-to-noise ratio in rodents than in humans or large animals, make data analysis extremely challenging, as we have previously discussed²³. A major limitation of this protocol besides the costly equipment needed to perform telemetry and treadmill training is the high technical demand on the surgical procedure and on data analysis, limiting accessibility to beginners in the field.

In sum, the ECG is a brilliant tool to study cardiac electrophysiology and arrhythmogenesis. In humans, stress tests to record ECGs during exercise are routinely performed and allow to assess training-associated effects on cardiac electrophysiology. Mice are the most commonly used species in research, several exercise protocols have been developed, but monitoring the real-time ECG during training was not possible so far. Our proposed protocol allows to obtain ECG recordings during periods of exercise in mice for the first time. This will enable researchers to study both exercise-related mechanisms leading to beneficial cardiac adaptations and maladaptive, proarrhythmic remodeling and will, thus, eventually result in improved patient care in the future.

Materials

Name	Company	Catalog Number	Comments
14-gauge needle	Sterican	584125
Any mouse	e.g. Jackson Laboratories
Bepanthen	Bayer	1578675
Carprofen 0.005 mg/µL	Zoetis	53716-49-7
Data Exchange Matrix 2.0 (MX2)	Data Science International		Manages communication between PhysioTel and PhysioTel HD telemetry implants and the acquisition computer.
Enrofloxacin 25 mg/ml	Baytril	400614.00.00
Fentanyl 0.5 mg/10 mL	Braun Melsungen
Fine forceps	Fine Science Tools	11295-51
Five Lane Treadmill for Mouse	Panlab - Harvard Apparatus	76-0896	Includes treadmill unit, touchscreen control unit, a sponge , and cables
Iris scissors	Fine Science Tools	14084-08
Isoflurane 1 mL/mL	Cp-Pharma	31303
Isoflurane vaporizer system	Hugo Sachs Elektronik	34-0458, 34-1030, 73-4911, 34-0415, 73-4910	Includes an induction chamber, a gas evacuation unit and charcoal filters
LabChart Pro 8.1.16	ADInstruments
Magnet	Data Science International
Modified Bain circuit	Hugo Sachs Elektronik	73-4860	Includes an anesthesia mask for mice
Modular connectors	Data Science International		Connecting cables between Reciever, Signal Interface and Matrix 2.0 (MX2)
Novafil s 5-0	Medtrocin/Covidien	88864555-23
Octal BioAmp	ADInstruments	FE238-0239	Amplifier for recording Surface ECG
Octenisept	Schülke	121418
Oxygen 5 L	Linde	2020175	Includes a pressure regulator
PhysioTel ETA-F10 transmitter	Data Science International
PhysioTel receiver RPC-1	Data Science International		Signal reciever
Ponemah 6.42	Data Science International		ECG Analysis Software
Powerlab	ADInstruments	3516-1277	Suface ECG Acquisition hardware device. Includes ECG electrode leads
Prism 8.0.1	Graph Pad
Radio Device (Sony AF/AM)	Sony
Signal Interface	Data Science International		Acquires and synchronizes digital signals with telemetry data in Ponemah v6.x.
Spring scissors	Fine Science Tools	91500-09
Surgical platform	Kent Scientific	SURGI-M
Tergazyme 1%	Alconox	13051.0	Commercial cleaning solution
Tweezers	Kent Scientific	INS600098-2