Cardiac organoid technology and computational processing of cardiac physiology for advanced drug screening applications

Stem cell technology has gained considerable recognition since its inception to advance disease modeling and drug screening. This is especially true for tissues that are difficult to study due to tissue sensitivity and limited regenerative capacity, such as the heart. Previous work in stem cell-derived cardiac tissue has exploited how we can engineer biologically functional heart tissue by providing the appropriate external stimuli to facilitate tissue development. The goal of this dissertation is to explore the potentials of stem cell cardiac organoid models to recapitulate heart development and implement analytical computational tools to study cardiac physiology. These new tools were implemented as potential advancements in drug screening applications for better predictions of drug-related cardiotoxicity. Cardiac organoids, generated via micropatterning techniques, were explored to determine how controlling engineering parameters, specifically the geometry, direct tissue fate and organoid function. The advantage of cardiac organoid models is the ability to recapitulate and study human tissue morphogenesis and development, which has currently been restricted through animal models. The cardiac organoids demonstrated responsiveness manifested as impairments to tissue formation and contractile functions as a result of developmental drug toxicity. Single-cell genomic characterization of cardiac organoids unveiled a co-emergence of cardiac and endoderm tissue, which is seen in vivo through paracrine signaling between the liver and heart. We then implemented computational tools based on nonlinear mathematical analysis to evaluate the cardiac physiological drug response of stem cell-derived cardiomyocytes. This dissertation discusses in vitro tissue platforms as well as computational tools to study drug-induced cardiotoxicity. Using these tools, we can extend current toolboxes of understanding cardiac physiology for advanced investigations of stem-cell based cardiac tissue engineering

Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis

International audienceUnderstanding the complexity of biological signals has been gaining widespread attention due to increasing knowledge on the nonlinearity that exists in these systems. Cardiac signals are known to exhibit highly complex dynamics, consisting of high degrees of interdependency that regulate the cardiac contractile functions. These regulatory mechanisms are important to understand for the development of novel in vitro cardiac systems, especially with the exponential growth in deriving cardiac tissue directly from human induced pluripotent stem cells (hiPSCs). This work describes a unique analytical approach that integrates linear amplitude and frequency analysis of physical cardiac contraction, with nonlinear analysis of the contraction signals to measure the signals' complexity. We generated contraction motion waveforms reflecting the physical contraction of hiPSC-derived cardiomyocytes (hiPSC-CMs) and implemented these signals to nonlinear analysis to compute the capacity and correlation dimensions. These parameters allowed us to characterize the dynamics of the cardiac signals when reconstructed into a phase space and provided a measure of signal complexity to supplement contractile physiology data. Thus, we applied this approach to evaluate drug response and observed that relationships between contractile physiology and dynamic complexity were unique to each tested drug. This illustrated the applicability of this approach in not only characterization of cardiac signals, but also monitoring and diagnostics of cardiac health in response to external stress

Quantitatively Characterizing Drug-Induced Arrhythmic Contractile Motions of Human Stem Cell-Derived Cardiomyocytes.

Quantification of abnormal contractile motions of cardiac tissue has been a noteworthy challenge and significant limitation in assessing and classifying the drug-induced arrhythmias (i.e. Torsades de pointes). To overcome these challenges, researchers have taken advantage of computational image processing tools to measure contractile motion from cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs). However, the amplitude and frequency analysis of contractile motion waveforms doesn't produce sufficient information to objectively classify the degree of variations between two or more sets of cardiac contractile motions. In this paper, we generated contractile motion data from beating hiPSC-CMs using motion tracking software based on optical flow analysis, and then implemented a computational algorithm, phase space reconstruction (PSR), to derive parameters (embedding, regularity, and fractal dimensions) to further characterize the dynamic nature of the cardiac contractile motions. Application of drugs known to cause cardiac arrhythmia induced significant changes to these resultant dimensional parameters calculated from PSR analysis. Integrating this new computational algorithm with the existing analytical toolbox of cardiac contractile motions will allow us to expand current assessments of cardiac tissue physiology into an automated, high-throughput, and quantifiable manner which will allow more objective assessments of drug-induced proarrhythmias. This article is protected by copyright. All rights reserved

Maladaptive Contractility of 3D Human Cardiac Microtissues to Mechanical Nonuniformity

Cardiac tissues are able to adjust their contractile behavior to adapt to the local mechanical environment. Nonuniformity of the native tissue mechanical properties contributes to the development of heart dysfunctions, yet the current in vitro cardiac tissue models often fail to recapitulate the mechanical nonuniformity. To address this issue, a 3D cardiac microtissue model is developed with engineered mechanical nonuniformity, enabled by 3D-printed hybrid matrices composed of fibers with different diameters. When escalating the complexity of tissue mechanical environments, cardiac microtissues start to develop maladaptive hypercontractile phenotypes, demonstrated in both contractile motion analysis and force-power analysis. This novel hybrid system could potentially facilitate the establishment of "pathologically-inspired" cardiac microtissue models for deeper understanding of heart pathology due to nonuniformity of the tissue mechanical environment

Generation of spatial-patterned early-developing cardiac organoids using human pluripotent stem cells

The creation of human induced pluripotent stem cells (hiPSCs) has provided an unprecedented opportunity to study tissue morphogenesis and organ development through 'organogenesis-in-a-dish'. Current approaches to cardiac organoid engineering rely on either direct cardiac differentiation from embryoid bodies (EBs) or generation of aligned cardiac tissues from predifferentiated cardiomyocytes from monolayer hiPSCs. To experimentally model early cardiac organogenesis in vitro, our protocol combines biomaterials-based cell patterning with stem cell organoid engineering. 3D cardiac microchambers are created from 2D hiPSC colonies; these microchambers approximate an early-development heart with distinct spatial organization and self-assembly. With proper training in photolithography microfabrication, maintenance of human pluripotent stem cells, and cardiac differentiation, a graduate student with guidance will likely be able to carry out this experimental protocol, which requires ∼3 weeks. We envisage that this in vitro model of human early heart development could serve as an embryotoxicity screening assay in drug discovery, regulation, and prescription for healthy fetal development. We anticipate that, when applied to hiPSC lines derived from patients with inherited diseases, this protocol can be used to study the disease mechanisms of cardiac malformations at an early stage of embryogenesis