Quantifying the spatiotemporal mechanical dynamics of engineered cardiac microbundles

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.Author summary: Stem cells can be guided to form many types of cells, including cardiomyocytes, offering new ways to repair damaged tissue and to model disease. In cardiac tissue engineering, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are grown in two- and three-dimensional systems to create functional heart tissues. These constructs, currently valuable for drug testing and heart disease modeling, are promising as future implantable patches. However, the field lacks consistent protocols and quantitative metrics that are both standardized and reproducible to evaluate tissue function. We address this need by building on our open-source tools, “MicroBundleCompute” and “MicroBundlePillarTrack,” and a public dataset of videos of beating engineered tissues. We introduce quantitative metrics that describe tissue behavior across space and time, including motion patterns, beat timing, and the coordination and propagation of contraction. Using these metrics, we apply statistical analyses and machine learning approaches to identify distinct contraction phenotypes and to compare performance across samples. We also demonstrate how the choice of metrics has the potential to influence scientific conclusions. All code, documentation, and analysis workflows are openly available. By sharing these methods and a reproducible computational pipeline, we aim to support transparent benchmarking, improve cross-lab comparisons, and accelerate the development of reliable cardiac tissue models.