Cardio PyMEA: A user-friendly, open-source Python application for cardiomyocyte microelectrode array analysis

Figures

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  Fig 1. Main window of Cardio PyMEA graphical user interface. All of the user’s interactions with Cardio PyMEA stem from this window. The user can utilize the various drop-down menu options to import data, perform calculations, and perform statistical analysis.

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  Fig 2. Operational flowchart for using Cardio PyMEA. This flowchart illustrates how Cardio PyMEA can be operated in order to analyze cardiomyocyte MEA data.

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  Fig 3. Representative schematic of the geometric configuration used for a 120 electrode MEA. This coordinate system shown here was devised using an interelectrode spacing of 200 μm and electrode diameter of 30 μm.

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  Fig 4. ‘Find Beats Results’ window. The left plot shows an individual beat for the electrode specified by the window slider. The sliders below the plot control which beat and/or electrode is plotted. The right plot shows the full MEA for a specified beat and is controlled by the same ‘Beat’ slider as the left plot.

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  Fig 5. Diagram of cardiomyocyte field potential property calculations. A) Time lag (tlag, pacemaker) calculation. The field potential peak for each electrode is calculated, then the peak with the minimum time is normalized to 0 ms. This process is applied to all electrodes to yield normalized time lag values for the given beat. B) Amplitude (gray vertical line), dV/dtmax, and LAT can be calculated from the field potential signal. Black arrows indicate derivative calculations along points prior to or after the field potential peak (red x). The maximum positive (dV/dtmax, magenta circle) and negative (LAT, orange circle) derivatives are identified and used in calculating dV/dtmax and LAT, respectively. C) Beat intervals are calculated by determining the time between field potential peaks.

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  Fig 6. Beat detection results for an MEA-plated cardiomyocyte culture. An individual 120 electrode MEA (A) was plated with cardiomyocytes (B). Field potentials were recorded (C) and analyzed to determine the R-wave-like peak, beat amplitude, T-wave endpoint, and other features. These field potentials showed varying levels of noise, ranging from clean (D) to moderately (E) or significantly (F) noisy, as indicated by the decreasing signal-to-noise (S/N) ratios. Beat detection was performed for all field potentials across all MEAs.

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  Fig 7. Pacemaker analysis output. A-C: representative pacemaker heatmap, spatially defined by the MEA coordinate system. D-F: estimated pacemaker location for the given data based on the time lag propagation wavefront. The estimated origin is indicated by an orange dot.

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  Fig 8. ‘Property vs Distance with R2’ window. Representative plots of each property versus distance from the pacemaker. Statistical quantities, such as average time lag and conduction velocity, as well as top R2 values, are presented in the ‘Statistics Readout’ text box on the right side of the window.

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  Fig 9. ‘Powerlaw Distribution Comparison’ window. The results of power law analysis, both qualitative (plots) and quantitative (numerical output, text box below plots) are presented in this window of the application. The user can adjust a variety of parameters, as necessary, and execute their analysis by clicking ‘Plot’. The power law plot was adapted from [25].