PIV-MyoMonitor: an accessible particle image velocimetry-based software tool for advanced contractility assessment of cardiac organoids

Induced pluripotent stem cell (iPSC)-derived cardiac organoids offer a versatile platform for personalized cardiac toxicity assessment, drug screening, disease modeling, and regenerative therapies. While previous image-based contractility analysis techniques allowed the assessment of contractility of two-dimensional cardiac models, they face limitations, including encountering high noise levels when applied to three-dimensional organoid models and requiring expensive equipment. Additionally, they offer fewer functional parameters compared to commercial software. To address these challenges, we developed an open-source, particle image velocimetry-based software (PIV-MyoMonitor) and demonstrated its capacity for accurate contractility analysis in both two- and three-dimensional cardiac models using standard lab equipment. Comparisons with four other open-source software programs highlighted the capability of PIV-MyoMonitor for more comprehensive quantitative analysis, providing 22 functional parameters and enhanced video outputs. We showcased its applicability in drug screening by characterizing the response of cardiac organoids to a known isotropic drug, isoprenaline. In sum, PIV-MyoMonitor enables reliable contractility assessment across various cardiac models without costly equipment or software. We believe this software will benefit a broader scientific community.

Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors

Introduction: Tyrosine kinase inhibitor drugs (TKIs) are highly effective cancer drugs, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood. We studied mechanisms of TKI-induced cardiotoxicity by integrating several complementary approaches, including comprehensive transcriptomics, mechanistic mathematical modeling, and physiological assays in cultured human cardiac myocytes. Methods: Induced pluripotent stem cells (iPSCs) from two healthy donors were differentiated into cardiac myocytes (iPSC-CMs), and cells were treated with a panel of 26 FDA-approved TKIs. Drug-induced changes in gene expression were quantified using mRNA-seq, changes in gene expression were integrated into a mechanistic mathematical model of electrophysiology and contraction, and simulation results were used to predict physiological outcomes. Results: Experimental recordings of action potentials, intracellular calcium, and contraction in iPSC-CMs demonstrated that modeling predictions were accurate, with 81% of modeling predictions across the two cell lines confirmed experimentally. Surprisingly, simulations of how TKI-treated iPSC-CMs would respond to an additional arrhythmogenic insult, namely, hypokalemia, predicted dramatic differences between cell lines in how drugs affected arrhythmia susceptibility, and these predictions were confirmed experimentally. Computational analysis revealed that differences between cell lines in the upregulation or downregulation of particular ion channels could explain how TKI-treated cells responded differently to hypokalemia. Discussion: Overall, the study identifies transcriptional mechanisms underlying cardiotoxicity caused by TKIs, and illustrates a novel approach for integrating transcriptomics with mechanistic mathematical models to generate experimentally testable, individual-specific predictions of adverse event risk.