Open Collections

Ashraf, Mishal

University of British Columbia

Introduction: Proper characterization of cardiac tissues is a complex problem, as in addition to the metabolic and transcriptional machinery that may govern the activity of other cell types, cardiomyocytes also possess important electrical and mechanical properties. Optical mapping is a fluorescence microscopy technique which uses fluorescent voltage and calcium sensitive dyes and high-framerate imaging to capture electrical behaviors. This technique is uniquely valuable because of its ability to provide both temporal and spatial data at high-resolution, which can be useful when considering the importance of tissue engineering techniques to modern research in cardiovascular regenerative medicine. The challenge with optical mapping is that data analysis software is inaccessible to many groups. A lack of open-source options requires research groups to develop in-house expertise in software development, image processing and signal processing to employ the technique. The impact of this inaccessibility is that many groups are unable to adequately characterize electrical activity, which due to excitation-contraction coupling is a fundamental precursor to any discussion of contractile activity and therefore limits future utility in regenerative medicine applications. Methods: Our group has developed an analysis package for the analysis of optical mapping data. The package is written in Python and uses open-source packages such as numpy, scipy, opencv and scikit-image. The package enables the user to manually explore data for parameter optimization and perform automated batch analysis. The tool is flexible; it can be used with a variety of model systems and can be scaled to use modern cloud computing infrastructures for analyzing high-throughput experiments at scale. Results: Our analysis system has been validated using tissue from three model systems: cardiac monolayers, embryoid bodies, and engineered heart tissues. The tool can extract feature space representations describing both repolarization and depolarization dynamics within time-series recordings. Our results validate our implementation by recapitulating known differences between different biological preparations (ex. atrial and ventricular cells). We also present results from novel feature engineering techniques for the characterization of diastolic depolarization to distinguish pacemaker and working cardiomyocytes. Conclusion: Our tool will help biologists in the cardiovascular research space accelerate their discoveries by more holistically characterizing their samples.

Text

2022-10-04

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82817

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/