Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

Controlling Morphology and Functions of Cardiac Organoids by Two-Dimensional Geometrical Templates.

Traditionally, tissue-specific organoids are generated as 3D aggregates of stem cells embedded in Matrigel or hydrogels, and the aggregates eventually end up a spherical shape and suspended in the matrix. Lack of geometrical control of organoid formation makes these spherical organoids limited for modeling the tissues with complex shapes. To address this challenge, we developed a new method to generate 3D spatial-organized cardiac organoids from 2D micropatterned human induced pluripotent stem cell (hiPSC) colonies, instead of directly from 3D stem cell aggregates. This new approach opens the possibility to create cardiac organoids that are templated by 2D non-spherical geometries, which potentially provides us a deeper understanding of biophysical controls on developmental organogenesis. Here, we designed 2D geometrical templates with quadrilateral shapes and pentagram shapes that had same total area but different geometrical shapes. Using this templated substrate, we grew cardiac organoids from hiPSCs and collected a series of parameters to characterize morphological and functional properties of the cardiac organoids. In quadrilateral templates, we found that increasing the aspect ratio impaired cardiac tissue 3D self-assembly, but the elongated geometry improved the cardiac contractile functions. However, in pentagram templates, cardiac organoid structure and function were optimized with a specific geometry of an ideal star shape. This study will shed a light on "organogenesis-by-design" by increasing the intricacy of starting templates from external geometrical cues to improve the organoid morphogenesis and functionality.

Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.

Utilizing recent advances in human induced pluripotent stem cell (hiPSC) technology, nonlinear analysis and machine learning we can create novel tools to evaluate drug-induced cardiotoxicity on human cardiomyocytes. With cardiovascular disease remaining the leading cause of death globally it has become imperative to create effective and modern tools to test the efficacy and toxicity of drugs to combat heart disease. The calcium transient signals recorded from hiPSC-derived cardiomyocytes (hiPSC-CMs) are highly complex and dynamic with great degrees of response characteristics to various drug treatments. However, traditional linear methods often fail to capture the subtle variation in these signals generated by hiPSC-CMs. In this work, we integrated nonlinear analysis, dimensionality reduction techniques and machine learning algorithms for better classifying the contractile signals from hiPSC-CMs in response to different drug exposure. By utilizing extracted parameters from a commercially available high-throughput testing platform, we were able to distinguish the groups with drug treatment from baseline controls, determine the drug exposure relative to IC50 values, and classify the drugs by its unique cardiac responses. By incorporating nonlinear parameters computed by phase space reconstruction, we were able to improve our machine learning algorithm's ability to predict cardiotoxic levels and drug classifications. We also visualized the effects of drug treatment and dosages with dimensionality reduction techniques, t-distributed stochastic neighbor embedding (t-SNE). We have shown that integration of nonlinear analysis and artificial intelligence has proven to be a powerful tool for analyzing cardiotoxicity and classifying toxic compounds through their mechanistic action.

Engineering spatial-organized cardiac organoids for developmental toxicity testing.

Emerging technologies in stem cell engineering have produced sophisticated organoid platforms by controlling stem cell fate via biomaterial instructive cues. By micropatterning and differentiating human induced pluripotent stem cells (hiPSCs), we have engineered spatially organized cardiac organoids with contracting cardiomyocytes in the center surrounded by stromal cells distributed along the pattern perimeter. We investigated how geometric confinement directed the structural morphology and contractile functions of the cardiac organoids and tailored the pattern geometry to optimize organoid production. Using modern data-mining techniques, we found that pattern sizes significantly affected contraction functions, particularly in the parameters related to contraction duration and diastolic functions. We applied cardiac organoids generated from 600 µm diameter circles as a developmental toxicity screening assay and quantified the embryotoxic potential of nine pharmaceutical compounds. These cardiac organoids have potential use as an in vitro platform for studying organoid structure-function relationships, developmental processes, and drug-induced cardiac developmental toxicity.

Biomaterial-guided stem cell organoid engineering for modeling development and diseases.

Organoids are miniature models of organs to recapitulate spatiotemporal cellular organization and tissue functionality. The production of organoids has revolutionized the field of developmental biology, providing the possibility to study and guide human development and diseases in a dish. More recently, novel biomaterial-based culture systems demonstrated the feasibility and versatility to engineer and produce the organoids in a consistent and reproducible manner. By engineering proper tissue microenvironment, functional organoids have been able to exhibit spatial-distinct tissue patterning and morphogenesis. This review focuses on enabling technologies in the field of organoid engineering, including the control of biochemical and biophysical cues via hydrogels, as well as size and geometry control via microwell and microfabrication techniques. In addition, this review discusses the enhancement of organoid systems for therapeutic applications using biofabrication and organoid-on-chip platforms, which facilitate the assembly of complex organoid systems for in vitro modeling of development and diseases. STATEMENT OF SIGNIFICANCE: Stem cell organoids have revolutionized the fields of developmental biology and tissue engineering, providing the opportunity to study human organ development and disease progression in vitro. Various works have demonstrated that organoids can be generated using a wide variety of engineering tools, materials, and systems. Specific culture microenvironment is tailored to support the formation, function, and physiology of the organ of interest. This review highlights the importance of cellular microenvironment in organoid culture, the versatility of organoid engineering techniques, and future perspectives to build better organoid systems.

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.

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

Understanding 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 does not 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.

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.