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MicroBundlePillarTrack: A Python package for automated segmentation, tracking, and analysis of pillar deflection in
Hiba Kobeissi1, Xining Gao2,3,4, Samuel J DePalma5
1Department of Mechanical Engineering, Center for Multiscale and Translational Mechanobiology, Boston University, Boston, Massachusetts, United States.
Arxiv
|August 26, 2024
Summary
This study introduces MicroBundlePillarTrack, an open-source software for analyzing human induced pluripotent stem cell-derived cardiac microbundles. It enables reproducible, high-throughput contractility analysis and cross-platform comparisons.
Area of Science:
- Biomedical Engineering
- Stem Cell Biology
- Cardiovascular Research
Background:
- Human induced pluripotent stem cell (hiPSC)-derived engineered cardiac tissues (microbundles) offer insights into structural and functional maturity.
- Extracting reproducible, high-throughput data from these tissues is challenging.
- Quantitative comparisons across different in vitro experimental platforms are difficult.
Purpose of the Study:
- To develop an open-source software package for automated analysis of cardiac microbundle contractility.
- To enable reproducible and high-throughput data extraction from hiPSC-derived cardiac tissues.
- To facilitate reliable quantitative comparisons across different experimental platforms.
Main Methods:
- Developed "MicroBundlePillarTrack," an open-source Python package utilizing optical flow.
- The software automatically segments pillars and tracks their displacements.
- Analyzes pillar deflection to quantify metrics like beating amplitude, rate, contractile force, and tissue stress.
Main Results:
- MicroBundlePillarTrack successfully automates the analysis of cardiac microbundle contractility.
- The software provides time-dependent metrics for detailed functional assessment.
- Tested on a dataset of 1,540 brightfield movies, demonstrating its capability.
Conclusions:
- The automated, open-source MicroBundlePillarTrack software overcomes limitations in current analysis methods.
- It facilitates faster, more reproducible analyses and enables reliable cross-platform comparisons.
- Sharing the software and dataset promotes collective progress in biomedical engineering.
