MicroBundlePillarTrack: A Python package for automated segmentation, tracking, and analysis of pillar deflection in cardiac microbundles

TL;DR

MicroBundlePillarTrack is an open-source Python package that automates the segmentation, tracking, and analysis of pillar deflections in cardiac microbundles, enabling high-throughput, reproducible, and cross-platform contractility assessments.

Contribution

We developed a fully automated, open-source software for analyzing pillar deflections in cardiac microbundles, facilitating reproducible and high-throughput data analysis across different experimental platforms.

Findings

Successfully tracked pillar deflections in diverse microbundle datasets.

Enabled quantitative comparison of contractility metrics across platforms.

Shared a large dataset of 1,540 movies for benchmarking and further research.

Abstract

Movies of human induced pluripotent stem cell (hiPSC)-derived engineered cardiac tissue (microbundles) contain abundant information about structural and functional maturity. However, extracting these data in a reproducible and high-throughput manner remains a major challenge. Furthermore, it is not straightforward to make direct quantitative comparisons across the multiple in vitro experimental platforms employed to fabricate these tissues. Here, we present "MicroBundlePillarTrack," an open-source optical flow-based package developed in Python to track the deflection of pillars in cardiac microbundles grown on experimental platforms with two different pillar designs ("Type 1" and "Type 2" design). Our software is able to automatically segment the pillars, track their displacements, and output time-dependent metrics for contractility analysis, including beating amplitude and rate, contractile force, and tissue stress. Because this software is fully automated, it will allow for both faster and more reproducible analyses of larger datasets and it will enable more reliable cross-platform comparisons as compared to existing approaches that require manual steps and are tailored to a specific experimental platform. To complement this open-source software, we share a dataset of 1,540 brightfield example movies on which we have tested our software. Through sharing this data and software, our goal is to directly enable quantitative comparisons across labs, and facilitate future collective progress via the biomedical engineering open-source data and software ecosystem.