High-Throughput Analysis of Optical Mapping Data Using ElectroMap

The use of multi-array and optical mapping technologies in cardiac research is expanding. However, what is currently lacking is robust, high-throughput opensource software for analysis of electrophysiology data. This is why we have developed novel software called ElectroMap to help researchers with their analysis.

ElectroMap incorporates new and established methods to analyze cardiac mapping data which can be acquired from any species using multiple different cameras, multiple different fluorescent dyes, and different acquisition modalities including optical mapping and multi-electrode array systems. In addition to measuring standard AP parameters such as action potential duration, conductive assay, and morphology, ElectroMap enables analysis and quantify activity. Begin by downloading all files from the latest source code release of ElectroMap from the GitHub repository.

Unzip the downloaded contents to a desired location. Open MATLAB and navigate to the folder location hosting the ElectroMap source code. Then open the file electromap.

m and press run in the editor to start the ElectroMap user interface. Next, to load the image, press select folder and navigate to the location of the data file or files to be analyzed. Select the file to be loaded from the within the interface and press load images.

Once loaded, the first frame will appear and a red outline will indicate automatic thresholding of the image. If desired, modify this to a threshold based on the signal time course amplitude by changing the option in the image for threshold dropdown menu. Note that once the thresholding is selected, it is then applied for the whole image stack.

If desired, change the threshold option to manual which will activate the slider to manually adjust the image threshold. Additionally, select the custom region of interest for analysis by selecting the appropriate tick boxes below the threshold options. Note that advanced options for region of interest selection such as number of areas are available from ROI selection from the top menu.

Once an appropriate threshold has been applied, press process images to apply processing. At this point, ensure that the correct camera settings have been entered for frame rate and kilohertz and pixel size in micrometers and signal processing options such as window timeframe, spatial, and temporal filtering. After the file has been processed, inspect the peaks in the tissue average signal labeled by red circles.

Segment the signal based on detected peaks. Press segment signal. If desired, apply custom segmentation of the signal by zooming in on the time of interest and selecting segment signal.

After the images have been processed, the produce maps button will become active. Press produce maps to apply action potential duration, activation time, conduction velocity, and signal to noise ratio analysis. Select the desired segments from the list box to apply the analysis to each.

Next, select get pixel info to see a detailed display of the signal from any pixel within the image and compare pixels to simultaneously plot signals from up to six locations. To access more detailed analysis of conduction velocity, press conduction. Then press single vector to analyze conduction using the single vector method where the gradient vector is calculated from the delay inactivation time between two points.

Finally, press local vector to apply the multi-vector method with the settings matching those from the main interface. Press activation curve to plot the percentage of tissue activated as a function of time. To quantify other parameters using ElectroMap, select one of the other desired analyses from the dropdown menu above the display map.

Press single file analysis to open a dedicated module for high-throughput duration and conduction analysis of each identified segment in a file. Perform the analysis on either the whole image or on selected regions or points of interest. To export data from ElectroMap, press export values to save the values of the currently displayed map in the main user interface.

Then press export map to bring up a popup containing the currently displayed map which can then be saved in a variety of image formats. If desired, add a color bar by selecting the color bar icon. Set the scale by selecting edit then color map.

Finally, press activation video to render an animation of the activation sequence which can be saved as an animated GIF file. This shows the action potential morphology of the murine atria when compared to the guinea pig ventricle recorded using voltage sensitive dyes as previously reported. Despite the distinct shape of the action potential and the use of two separate optical mapping cameras with different frame rates and pixel sizes, ElectroMap can be utilized to successfully analyze both data sets by selecting the appropriate parameters.

The application of ensemble averaging in lieu of other methods can improve SNR from isolated murine left atria. A change of pacing frequency from three hertz to 10 hertz did not alter APD when no ensemble averaging is undertaken, yet an expected decrease in APD at 10 hertz pacing was observed when measured from ensemble averaged data. Further, mouse left atria were paced at a 120 millisecond cycle length and cycle length was incrementally shortened by 10 milliseconds until it reached 50 milliseconds.

ElectroMap automatically identify the pacing cycle length and group tissue average peaks accordingly. APD and diastolic interval shortened. Amplitude of the optically measured peaks decreased while time to peak increased.

This protocol shows how you can install and set up the settings in ElectroMap in order to quickly analyze action potential duration, conduction velocity, peak-to-peak variability, and also how you can ensemble average in order to improve the signal to noise ratio. We hope that the release of this freely available software will help researchers with their analysis needs and lead to some major discoveries in cardiac research. We will continue to build any algorithms we developed and we encourage others to help us further expand the ElectroMap capabilities.