Quantifying the Spatiotemporal Dynamics of Engineered Cardiac Microbundles

2026-04-08

AI Summary

Current brightfield time-lapse imaging of cardiac microbundles lacks a standardized, interpretable analytical framework, limiting data reproducibility and cross-platform comparability. This study presents an open, extensible computational pipeline that integrates whole-field displacement tracking, strain field reconstruction, spatial registration, and topological vector field analysis to define 16 structural–functional–spatiotemporal metrics. Validated across 670 microbundles under 20 experimental conditions, the framework reveals for the first time that contractile phenotypes vary continuously rather than forming discrete clusters. It identifies a minimal set of 10 core metrics that substantially reduce multicollinearity while preserving essential information. The analysis further demonstrates that contraction is predominantly governed by a global isotropic mode, with approximately half of the samples exhibiting localized saddle-shaped deformations. Accompanying open-source tools are MicroBundleCompute and MicroBundlePillarTrack.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Web measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Brightfield time-lapse imaging is widely used in cardiac tissue engineering, yet the absence of standardized, interpretable analytical frameworks limits reproducibility and cross-platform comparison. We present an open, scalable computational pipeline for quantifying spatiotemporal contractile dynamics in microscopy videos of human induced pluripotent stem cell-derived cardiac microbundles. Building on our open-source tools "MicroBundleCompute" and "MicroBundlePillarTrack," we define a suite of 16 interpretable structural, functional, and spatiotemporal metrics that capture tissue deformation, synchrony, and heterogeneity. The framework integrates full-field displacement tracking, strain reconstruction, spatial registration, dimensionality reduction, and topology-based vector-field analysis within a unified workflow. Applied to a dataset of 670 cardiac microbundles spanning 20 experimental conditions, the pipeline reveals continuous variation in contractile phenotypes rather than discrete condition-specific clustering, with intra-condition variability often exceeding inter-condition differences. Redundancy analysis identifies a reduced core set of 10 metrics that retain most informational content while minimizing multicollinearity. Analysis of denoised displacement fields shows that contraction is dominated by a global isotropic mode, with localized saddle-type deformation patterns present in approximately half of the samples. All software and workflows are released openly to enable reproducible, scalable analysis of dynamic tissue mechanics.

Problem

• cardiac tissue engineering
• spatiotemporal dynamics
• contractile phenotypes
• analytical framework
• reproducibility

Innovation

• spatiotemporal dynamics
• cardiac microbundles
• displacement tracking
• topology-based vector-field analysis
• open-source pipeline