KairoSight: Open-Source Software for the Analysis of Cardiac Optical Data Collected From Multiple Species

Cardiac optical mapping, also known as optocardiography, employs parameter-sensitive fluorescence dye(s) to image cardiac tissue and resolve the electrical and calcium oscillations that underly cardiac function. This technique is increasingly being used in conjunction with, or even as a replacement for, traditional electrocardiography. Over the last several decades, optical mapping has matured into a “gold standard” for cardiac research applications, yet the analysis of optical signals can be challenging. Despite the refinement of software tools and algorithms, significant programming expertise is often required to analyze large optical data sets, and data analysis can be laborious and time-consuming. To address this challenge, we developed an accessible, open-source software script that is untethered from any subscription-based programming language. The described software, written in python, is aptly named “KairoSight” in reference to the Greek word for “opportune time” (Kairos) and the ability to “see” voltage and calcium signals acquired from cardiac tissue. To demonstrate analysis features and highlight species differences, we employed experimental datasets collected from mammalian hearts (Langendorff-perfused rat, guinea pig, and swine) dyed with RH237 (transmembrane voltage) and Rhod-2, AM (intracellular calcium), as well as human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) dyed with FluoVolt (membrane potential), and Fluo-4, AM (calcium indicator). We also demonstrate cardiac responsiveness to ryanodine (ryanodine receptor modulator) and isoproterenol (beta-adrenergic agonist) and highlight regional differences after an ablation injury. KairoSight can be employed by both basic and clinical scientists to analyze complex cardiac optical mapping datasets without requiring dedicated computer science expertise or proprietary software.

To increase accessibility to optical mapping approaches, a few software applications have been released as open-source packages with freely available code, installation instructions, and user manuals [e.g., Rhythm (Laughner et al., 2012;Gloschat et al., 2018), Orca (Doshi et al., 2015), ElectroMap (O'Shea et al., 2019), and Cosmas (Tomek et al., 2021)]. However, utilization of these analysis tools can be hindered by necessary software expertise, dependence on a programming language that is cost prohibitive, or significant investment in commercial software (e.g., Optiq-Cairn Research). To address these hurdles, we developed a new software solution with a condensed workflow (Figure 1) to analyze cardiac optical mapping data quickly and accurately. Notably, the presented software package is controlled by a graphical user interface, which can facilitate its use by individuals with limited data analysis expertise. The described software is aptly named "KairoSight" in reference to the Greek word for "opportune time" (Kairos) and the ability to "see" voltage or calcium signals acquired from cardiac tissue. Since this software package uses an open-source platform (Python), it does not require commercial or licensed components. In the described studies, we demonstrate the utility of our software application using experimental datasets collected from mammalian hearts (rat, guinea pig, swine) stained with RH237 (transmembrane voltage) and Rhod-2, AM (intracellular calcium), as well as hiPSC-CM stained with FluoVolt (membrane potential) and Fluo-4, AM (calcium indicator). We also demonstrate cardiac responsiveness to ryanodine (ryanodine receptor modulator) and isoproterenol (beta-adrenergic agonist)-two agents commonly used to monitor perturbations in calcium cycling-and highlight regional differences after an ablation injury. By reducing complexities associated with data analysis, KairoSight equips investigators with the necessary tools to evaluate cardiac physiology and pathophysiology without requiring dedicated computer science expertise or proprietary software.

Acquisition and Generation of Optical Datasets
Animal procedures were approved by the Institutional Animal Care and Use Committee of the Children's Research Institute and FIGURE 1 | Simplified Workflow. (A) Representative raw image file from a Langendorff-perfused rat heart study (*.tif stack). Image properties (e.g., frame rate) are defined by the user. In our experimental setup, voltage and calcium signals can be analyzed from the same heart preparation. (B) Activation maps generated from raw image file demonstrate propagation away from the pacing electrode at the apex (left-voltage, right-calcium). (C) Generated action potential duration (left) and calcium transient duration (right) maps. (D) Representative traces from the raw image file.
followed the National Institutes of Health's Guide for the Care and Use of Laboratory Animals. All animals were euthanized by exsanguination under anesthesia following heart excision, as previously described in detail (Jaimes et al., 2019a;Swift et al., 2019). Experimental data were acquired from isolated, intact hearts that were maintained on a temperature-controlled (37 • C), constant-pressure (70 mmHg) Langendorff-perfusion system. To reduce motion artifact, Krebs-Henseleit perfusion buffer was supplemented with 10-15 µM (−/−) blebbistatin (Cayman Chemical) (Fedorov et al., 2007;Swift et al., 2012), or 10 mM 2, 3-butanedione monoxime (BDM; Sigma-Aldrich). Intact heart preparations were loaded with a calcium indicator dye (Rhod-2, AM: 100 µg rat, 100 µg guinea pig, 400 µg swine), which was added and allowed to stabilize for 10 min, followed by a potentiometric dye (RH237: 62 µg rat, 62 µg guinea pig, 248 µg swine). The epicardial surface was illuminated with filtered LED light (535 ± 25 nm), and emitted fluorescent light was split through a dichroic mirror (660 + nm) and directed through high-transmission filters to separate the V m (710 nm long pass) and Ca 2+ (585 ± 40 nm) signals. These simultaneous recordings were projected onto a single sensor with 2,016 × 2,016 pixel resolution and a pixel size of 11 × 11 µm (pco.dimax cs4), using an optosplit (Cairn Research) (Jaimes et al., 2019a;Swift et al., 2019). Adequate dye loading and signal detection was verified during the experiment using Fiji, an open-source image analysis software tool (Schindelin et al., 2012). Image stacks acquired from experimental data were saved in TIFF format ( * .tif) for subsequent analysis.

Software Development, Installation, and Image Analysis
KairoSight source code was written in the Python programming language (Python 3.8). Files related to the user interface elements and layout were generated in Qt Designer and converted to * .py python script files, suitable for the PyQt library used to give interface elements their functionality. When available and appropriate, methods from the standard Python libraries were used for data handling and analysis. Steps were taken to minimize user input and error by taking advantage of existing optimized numerical libraries like NumPy, scikit-image, and SciPy, Python's core scientific computing library. The software is freely available to download as source code from https://github.com/kairosight/ kairosight-2.0, which can be run and edited in either the Spyder IDE or from the command prompt using the Anaconda distribution of Python. For convenience, an installation video, graphical user interface usage video, README file, and sample image stacks (spatially binned due to file size limitations) are provided on GitHub.
Step 1-Image Properties In the user interface, an image stack was imported, and key properties of the dataset were provided by the user [frame rate (fps), image scale (px/cm), and image type (V m or Ca 2+ )]. When necessary, signal intensities were inverted along a central value to assure the analysis of relatively positive deflections from baseline intensities, based on the dye and filter combination. For our experimental dataset, upright signals were analyzed for calcium, while inverted signals were analyzed for voltage. An area of interest was isolated by cropping and rotating-which reduced the analysis of irrelevant pixels.
Step 2-Processing Background regions or non-fluorescent tissue areas were masked to isolate pixels of interest. The default algorithm, Otsu, generates a threshold value based on Otsu's Method (Otsu, 1979;Lee et al., 2017) and classifies each pixel as "foreground" or "background." Thresholding uses an operator defined percentage value (between 0.0 and 1.0) and includes that percentage of pixels as sorted by their intensity in the mask, and uses an operator defined kernel size to perform morphological closing, opening, and dilation to smooth the mask. Random Walk (Grady, 2006) uses an Otsu approach to label pixels according to user-defined values. An algorithm solves diffusion equations initiated at defined pixels to label pixels of similar value, wherein unknown pixels are assigned the label they have the highest probability to reach during diffusion. Mean calculates the mean pixel intensity and labels all pixels below that threshold as background, and all pixels above the threshold as foreground (Glasbey, 1993). Using the first frame, background pixels were masked and assigned an intensity value of zero for the entire stack, and the dimensions of the stack were preserved throughout. Whether automated or user-guided, masking effectively isolated the brightest continuous region of a cardiac preparation independent of dynamic changes in the fluorescent signal from action potentials or calcium transients. Image stacks can also be convolved with a kernel, to blur or smooth the image by placing greater weight on pixels closer to the center pixel as described by a Gaussian curve, while minimizing spatial broadening of the optical signal (Laughner et al., 2012).
Voltage and calcium dyes often exhibit a small fractional change in fluorescence, which can result in a modest signalto-noise ratio that impedes signal analysis without additional processing steps. Fluorescence signals regularly contain spatial noise, temporal noise, and baseline drift due to photobleaching. High-frequency temporal noise can be minimized, when needed, using a bidirectionally applied Butterworth filter with a user defined cutoff (e.g., 100 Hz). Spatial "salt-and-pepper" noise can be reduced by convolution with an image kernel filter (e.g., built-in box blur with adjustable kernel size). Signal drift can be removed by calculating and subtracting a least square fit polynomial curve of user-specified order for each pixel. Pixel intensity can be converted from arbitrary units of fluorescence (AUF) to normalized intensity (0 for minimum to 1 for maximum). Normalization from 0 to 1 scales all pixels to the same dynamic range, which facilitates subsequent workflow steps.
Step 3-Analysis Analysis steps were performed to produce imaging results and maps from either individual or ensembled signals from suitable pixel(s). Specific timepoints and durations within the action potential or calcium transient signals were detected and calculated. The "start time" marker was placed prior to the upstroke of the signal and the "end time" marker was placed following the peak of the transient (activation time measurement) or following the return of the transient to baseline (duration time measurement; see Supplementary Figure 1). To identify inflections of interest, 1st and 2nd derivatives of each fluorescent signal were generated and smoothed. Maxima and minima of those derivations were used to identify the inflection points (timepoints) of interest within each action potential or calcium transient. For duration maps, the maximum expected time interval and the desired duration percentage (e.g., APD 80 ) was defined by the user.
Between the baseline period and the peak time, the activation time (dFmax) of the action potential or calcium transient were identified. Downstroke (dFmin) and end time (2nd dF2max) were identified after each peak time, but before the subsequent baseline period began (Laughner et al., 2012). Identification of fundamental time points enabled further computation of optical signal features (e.g., action potential or calcium transient durations (APD or CaD: time from activation to any% of Fmax), diastolic interval (DI: time from APD 80 or CaD 80 to the subsequent activation), the time for an action potential or calcium transient to reach 1-1/e amplitude (τ: mono-exponential decay time (Jaimes et al., 2016b)).
In addition to the temporal results derived from optical signals, spatial analysis was important for identifying tissue heterogeneities, voltage and calcium concordance, and the directionality of electrical and/or calcium wavefront propagation. Functional activation patterns were displayed using isochronal maps, in which an activation time was assigned to each pixel and presented simultaneously. Similarly, spatial distributions of durations (APD, CaD) were displayed in scaled maps.
Exporting Data FIGURE 2 | Voltage Signal-to-Noise Ratio (SNR). (A) Representative rat heart loaded with voltage dye, RH237. Action potentials were analyzed from the same region of interest (ROI) on the epicardial surface (n = 6 action potentials, same heart with diminished light illumination). (B) No measurable change in activation time was noted when the excitation light intensity and SNR was reduced (n = 6 action potentials, same heart). (C) Calculated APD 80 values also remained consistent (n = 6 action potentials, same heart). (D) Relationship between SNR and action potential measurements (n = 4 optical signals collected from n = 4 independent heart studies). Epicardial dynamic pacing (160 ms PCL) from the apex of the left ventricle. over time, including intensity signals, analysis results (all values or mean ± SD) or calculated/mapped values as * .csv.

Statistical Analysis
Results were reported as mean ± standard deviation. Differences between group means were determined using two-tailed Student's t-test or analysis of variance (GraphPad Prism). Significance was defined as a p-value < 0.05, or an adjusted p-value (q < 0.1) after correction for multiple comparisons using two stage linear step-up procedure to control the false discovery rate (0.1), as described by Benjamini et al. (2006). Significance was denoted in the figures with an asterisk ( * ). Replicates were defined in each figure legend; independent optical signals were used from the same heart preparation (Figures 2A-C, 3A-C signal-to-noise data), and independent heart preparations were used for other measurements (Figures 4-8).

KairoSight Workflow and User Interface
To provide a more accessible tool for optical mapping data analysis, we developed a novel, open-source software package with an intuitive user interface (Supplementary Figure 1) that directs users through a linear workflow (example shown in Figure 1). Image processing was accomplished by importing an image stack and applying the following steps: (1) Image Properties, (2) Processing, and (3) Analysis. The KairoSight FIGURE 3 | Calcium Signal-to-Noise Ratio (SNR). (A) Representative rat heart loaded with calcium dye, Rhod-2, AM. Calcium transients were analyzed from the same region of interest (ROI) on the epicardial surface (n = 6 calcium transients, same heart with diminished light intensity). (B) No measurable change in activation time was noted when the excitation light intensity and SNR was reduced (n = 6 calcium transients, same heart). (C) Calculated CaD 80 values also remain unchanged (n = 6 calcium transients, same heart). (D) Relationship between SNR and calcium transient measurements (n = 4 optical signals collected from n = 4 independent heart studies). Epicardial dynamic pacing (160 ms PCL) from the apex of the left ventricle.
interface used a workflow with very few input options, which minimized user error, maximized computational resources, and enabled new users to easily understand the methods to implement data analysis steps with accuracy. To illustrate the Image Properties step, cropping and masking were applied to an experimental dataset in Figure 1.

Analysis of Transmembrane Voltage and Intracellular Calcium
Multiparametric optical signals were acquired from Langendorffperfused rat, guinea pig, and swine hearts, and hiPSC-CM, which were stained with fluorescent dyes. Using the cropping feature, the user isolated areas of interest and specified the type of signal (voltage-inverted signal, or calcium-upright signal) from the images loaded onto KairoSight. Activation maps, action potential, or calcium transient duration maps (Figures 2-9), as well as statistical results (e.g., action potential and calcium transient duration, amplitude, activation time, transient decay tau) were generated. The software identified and analyzed optical voltage and calcium signals from multiple species (Figures 4, 5, 7), with variations in signal shape and pacing frequency.

Suboptimal Signal-to-Noise Ratio
To confirm the consistency of the signal analysis software in the event of imperfect signals, experimental data was analyzed with varying degrees of noise. Optical signals were collected from hearts loaded with a voltage sensitive dye (RH237) and a calcium dye (Rhod-2, AM). The signal-to-noise ratio (SNR; calculated as the signal amplitude divided by the standard deviation of the  Values reported as mean ± SD. *p < 0.05, as determined by an unpaired Student's t-test (two-tailed).

Multi-Species Analysis
To test the flexibility of the software in analyzing various signal morphologies, tissues from four source species were analyzed. Transmembrane voltage signals were collected from the hearts of rats, guinea pigs, and swine, as well as cultured hiPSC-CM layers.
In comparing the morphology of the electrical and calcium traces between species, varying differences in the optical signals were detected (Figures 4, 5). Rat action potential and calcium transient optical signals aligned with examples in the literature (Walton et al., 2013;Jaimes et al., 2016b;Zasadny et al., 2020). The corresponding electrical restitution curve showed little change when the dynamic pacing cycle length (PCL) was decremented from 200 to 100 ms ( Figure 4A), however, the CaD 80 shortened by 18.8% at the faster pacing rate ( Figure 5A). Guinea pig signals and restitution values mirrored those reported in the literature (Guo et al., 2008;Lee et al., 2012), as well as those modeled by Edwards and Louch (2017). As expected, duration times were longer in the guinea pig heart, compared to the rat. Specifically, the APD 80 and CaD 80 shortened by approximately 15.2 and 17.4%, respectively, as the PCL was decremented from 200 to 140 ms (Figures 4B, 5B). Likewise, swine APD 80 and CaD 80 exhibited even slower dynamics, in agreement with previous studies (Bourgeois et al., 2011;Walton et al., 2013;Lee et al., 2017). Decrementing the PCL from 350 to 200 ms shortened the APD 80 by 25.5% ( Figure 4C) and the CaD 80 by 30.3% ( Figure 5C). Although not directly comparable to an intact heart preparation, hiPSC-CM were also imaged and analyzed as proof of concept. Decrementing the PCL from 3,330 to 1,430 ms shortened the APD 80 by 16.1% ( Figure 4D) and CaD 80 by 27.6% (Figure 5D).

Activation Time Measurements
Action potential and calcium transient activation maps were generated for rat, guinea pig, and swine hearts during both sinus FIGURE 7 | Cardiac alternans occur at faster pacing cycle lengths. (A) Representative optical action potentials, and (B) calcium transients from a Langendorff-perfused guinea pig heart. Slower PCL (200 ms) produces uniform action potentials and calcium transients (left), while a faster PCL (140 ms) results in cardiac alternans (right). Traces were acquired from two different regions of interest (solid line-left ventricle, dotted line-right ventricle) and discordant alternans were readily observable in the calcium signal. Four sequential beats show regional differences in alternans in calcium duration maps. (C) Relative occurrence of cardiac alternans in different species (mouse, rat, guinea pig). Epicardial dynamic pacing applied to the left ventricle for all studies (n = 5-18 independent heart studies per species). rhythm and in response to dynamic epicardial pacing (Figure 6). As expected, activation time measurements were faster during sinus rhythm (often occurring in the right ventricle before the left ventricle) and slower in response to external pacing. In the rat heart, activation time was 8.9 ± 1.9 ms (V m ) and 10.2 ± 2.1 ms (Ca 2+ ) during sinus rhythm (Figure 6A), which increased to 27.7 ± 3.5 ms (V m ) and 30.4 ± 5.2 ms (Ca 2+ ) with pacing (200 ms PCL; Figures 6B,C). Similar observations were noted in larger species; in the guinea pig heart activation time was 18.8 ± 5 ms (V m ) and 19.9 ± 6.3 ms (Ca 2+ ) during sinus rhythm (Figure 6A), which increased to 29.4 ± 3.4 ms (V m ) and 31.9 ± 4.8 ms (Ca 2+ ) with pacing (200 ms PCL, Figures 6B,C). Piglet heart activation time was 32.7 ± 7.3 ms (V m ) and 37.8 ± 10.2 ms (Ca 2+ ) during sinus rhythm (Figure 6A), which increased to 67.0 ± 10.5 (V m ) and 77.4 ± 12.2 ms (Ca 2+ ) with pacing (350 ms PCL, Figures 6B,C).

Cardiac Alternans and Demonstration of Regional Heterogeneity
Calcium flux plays a central role in excitation-contraction coupling, wherein perturbations in calcium handling (e.g., alternans, calcium leak from the sarcoplasmic reticulum) are associated with an array of cardiac dysfunctions, both electrical and mechanical (Weiss et al., 2011;Wang et al., 2014;Kanaporis and Blatter, 2015;Johnson et al., 2019;Sutanto et al., 2020). Calcium alternans are a potentially deleterious physiological oscillation, which are observable as alternating amplitude fluctuations in optical signals. The latter has been mechanistically linked to T-wave alternans that can segue into lethal ventricular arrhythmias (Weiss et al., 2011;Ng, 2017). Experimentally, cardiac alternans have been more commonly observed at faster heart rates and/or cooler temperatures (Egorov et al., 2012;Millet et al., 2021); accordingly, we utilized burst pacing to induce alternans in isolated, intact hearts (Figures 7A-C). Although not observed at slower pacing rates (200 ms PCL), intact guinea pig hearts displayed both APD alternans and calcium transient amplitude alternans at faster rates (120 ms PCL, Figures 7A,B). Further, calcium transient duration maps revealed spatially discordant alternans at faster rates, in which two regions of the tissue (right vs. left ventricle) were out of phase. When considering the occurrence of alternans between species, we observed variable susceptibility with increasing pacing frequency (relative occurrence at 100 ms PCL: 14% in mice, 42% in rats, and 57% in guinea pigs; Figure 7C).

Experimental Techniques to Demonstrate Responses to Pharmacological Agents and Myocardial Injury
Pharmacological agents are frequently employed to identify the mechanisms responsible for cardiac responses. As one example, isoproterenol is commonly used to induce heart failure through its positive chronotropic effects (Balakumar et al., 2007;Greer-Short and Poelzing, 2015;Chang et al., 2018). Using Langendorff-perfused rat hearts, we demonstrated that acute isoproterenol exposure (30 nM) results in a 40% increase in sinus rate (280.3 ± 32.4 baseline vs. 392.0 ± 37.7 BPM with isoproterenol), and 17.7% shortening of CaD 80 with dynamic pacing at 120 ms PCL (89.9 ± 5.9 baseline vs. 74 ± 14.4 ms with isoproterenol; Figures 8A,B). Further, ryanodine (5 µM) modulates calcium release from the sarcoplasmic reticulum, and is often used to demonstrate signal specificity in dual optical mapping studies (Choi and Salama, 2000;Jaimes et al., 2019a). Using KairoSight, we demonstrated a marked 92% reduction in the calcium transient amplitude with minimal effects on optical action potentials collected from intact rat hearts ( Figure 8C). Optical mapping studies are often used to monitor spiral wave FIGURE 8 | Impact of pharmacological agents on calcium handling. (A) Representative calcium transients acquired from a Langendorff-perfused rat heart at baseline and after acute treatment with isoproterenol (30 nM). Isoproterenol increased sinus rate (right, n = 4 independent heart studies, *p < 0.05 as determined by two-tailed Student's t-test). (B) Shorter calcium transient duration time in response to isoproterenol application was readily observable in calcium signals from a Langendorff-perfused rat heart during sinus rhythm and with external pacing for rate correction (120 ms PCL, n = 3-4 independent heart studies, *p < 0.05 as determined by two-way ANOVA with multiple comparisons testing). (C) Representative calcium transients from a Langendorff-perfused rat heart at baseline and after acute treatment with ryanodine (5 µM), during external pacing (160 ms PCL). Ryanodine diminished calcium transient amplitude (right; n = 4 independent heart studies, *p < 0.05 as determined by two-tailed Student's t-test). Values reported as mean + SD. BPM = beats per minute, dF/F0 = change in fluorescence over baseline.
formation and re-entry arrhythmias (Moe and Jalife, 1977;Pumir et al., 2005), which may occur due to tissue heterogeneities from a myocardial infarction or incomplete cardiac ablation. To demonstrate the utility of our optical mapping approach and data analysis, an epicardial radiofrequency ablation was applied to mimic the physiological environment that can promote such arrhythmias (Mercader et al., 2012). Optical signals were collected before and after radiofrequency ablation, and the analyzed signals show a typical pattern of electrical activity and calcium fluctuations outside the ablated area (Figure 9). Within the ablated area, we observed irregular and diminished signals from both the voltage and calcium signals. Accordingly, optical signal analysis via KairoSight can be applied to other applications focused on pharmacological treatment, tissue injury, or pathophysiology.

DISCUSSION
Optical mapping is commonly used in cardiac research applications, as it allows one to resolve the electrical and calcium oscillations that underly cardiac function and dysfunction (Jaimes et al., 2016b;George and Efimov, 2019). Nevertheless, there are important technical limitations and constraints that should be considered. Although optical mapping can be employed in situ (Lee et al., 2019;Martišienė et al., 2020)-this technique is more commonly applied ex vivo, using Langendorffperfused heart preparations that are maintained with a crystalloid buffer. To avoid signal distortion, the motion of the heart is often suppressed using mechanical constraint or excitationcontraction uncoupling agents (Fedorov et al., 2007;Swift et al., 2012). In the latter, the secondary effects of uncoupling agents should be considered (e.g., reduced metabolic demand, altered electrical activity). Moreover, in Langendorff-perfused heart preparations, optical mapping is limited to the surface of the tissue and endocardial activity can only be surmised by what is "seen" on the epicardium. Similarly, measurements of activation time and conduction velocity are monitored in response to external pacing-rather than under sinus rhythm. Despite these limitations, optical mapping remains a powerful tool for studying the spatiotemporal dynamics of cardiac electrophysiology and excitation-contraction coupling.
Optical mapping techniques have evolved over the last few decades, with continuous advancements in voltage and calciumsensitive dyes, light source and photodetector technology. As just one example, in 1975 the first fully digital camera took 23 s to record a 100 × 100 pixel image, while newly developed CMOS sensors have offered improved spatiotemporal resolution with acquisition speeds that can exceed 2,000 frames per second. Despite these immense technical improvements, data analysis remains an obstacle for new investigators entering the field of optical mapping, as many groups still develop their own custom software. To help address this barrier to entry, we have developed an accessible and functional software platform to prepare, process, and analyze optical action potentials and calcium signals collected from cardiac preparations. In the presented article, we document the utility of KairoSight in analyzing optical data sets collected under conditions of variable SNR, demonstrate speciesspecific differences in action potentials and calcium transients, showcase cardiac responses to varying pacing frequencies and/or pharmacological agents, and highlight tissue heterogeneities in the context of discordant alternans and cardiac injury. Our opensource software was developed using Python, as it has consistently been ranked as a "top 10" programming language by industry analysts (Stephens, 2020). Due to its flexibility and widespread adoption, we fully anticipate the research community to utilize KairoSight for image analysis applications-and also modify and tailor this tool for their own research aims. For example, future updates to the software could include measurements of conduction velocity, automated detection of electrical or calcium alternans, and/or the generation of signal:noise ratio maps.

DATA AVAILABILITY STATEMENT
Datasets are available from the corresponding author on reasonable request.

ETHICS STATEMENT
The animal study was reviewed and approved by Institutional Animal Care and Use Committee of the Children's Research Institute.

AUTHOR CONTRIBUTIONS
BC, CG, LS, TP, DM, RJ, and NP performed the experiments and approved the manuscript. BC, CG, LS, and NP analyzed the data. BC, LS, and NP prepared the figures. BC, LS, DM, and NP drafted the manuscript. BC, LS, CG, RJ, DM, and NP conceived and designed the experiments. All authors contributed to the article and approved the submitted version.

FUNDING
This work was supported by the National Institutes of Health (R01HL139472 to NP), Children's Research Institute, and Children's National Heart Institute.

ACKNOWLEDGMENTS
We gratefully acknowledge Ritvik Muralidharan, Marissa Reilly, and Nina Ciccarelli for helpful discussions and technical input on this project.