Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification

Molecular Design for Cardiac Cell Differentiation Using a Small Data Set and Decorated Shape Features

The discovery of small organic compounds for inducing stem cell differentiation is a time- and resource-intensive process. While data science could, in principle, streamline the discovery of these compounds, novel approaches are required due to the difficulty of acquiring training data from large numbers of example compounds. In this paper, we present the design of a new compound for inducing cardiomyocyte differentiation using simple regression models trained with a data set containing only 80 examples. We introduce decorated shape descriptors, an information-rich molecular feature representation that integrates both molecular shape and hydrophilicity information. These models demonstrate improved performance compared to ones using standard molecular descriptors based on shape alone. Model overtraining is diagnosed using a new type of sensitivity analysis. Our new compound is designed using a conservative molecular design strategy, and its effectiveness is confirmed through expression profiles of cardiomyocyte-related marker genes using real-time polymerase chain reaction experiments on human iPS cell lines. This work demonstrates a viable data-driven strategy for designing new compounds for stem cell differentiation protocols and will be useful in situations where training data is limited.

AI for biofabrication

Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticin vitrothree-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.

Enhanced drug classification using machine learning with multiplexed cardiac contractility assays

Cardiac screening of newly discovered drugs remains a longstanding challenge for the pharmaceutical industry. While therapeutic efficacy and cardiotoxicity are evaluated through preclinical biochemical and animal testing, 90 % of lead compounds fail to meet safety and efficacy benchmarks during human clinical trials. A preclinical model more representative of the human cardiac response is needed; heart tissue engineered from human pluripotent stem cell derived cardiomyocytes offers such a platform. In this study, three functionally distinct and independently validated engineered cardiac tissue assays are exposed to increasing concentrations of known compounds representing 5 classes of mechanistic action, creating a robust electrophysiology and contractility dataset. Combining results from six individual models, the resulting ensemble algorithm can classify the mechanistic action of unknown compounds with 86.2 % predictive accuracy. This outperforms single-assay models and offers a strategy to enhance future clinical trial success aligned with the recent FDA Modernization Act 2.0.

Automatic motion estimation with applications to hiPSC-CMs

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Understanding alterations in motion patterns within these cells is crucial for comprehending how the administration of a drug or the onset of a disease can impact the rhythm of the human heart. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently time consuming. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

Three-dimensional cardiac models: a pre-clinical testing platform

Major advancements in human pluripotent stem cell (hPSC) technology over recent years have yielded valuable tools for cardiovascular research. Multi-cell type 3-dimensional (3D) cardiac models in particular, are providing complementary approaches to animal studies that are better representatives than simple 2-dimensional (2D) cultures of differentiated hPSCs. These human 3D cardiac models can be broadly divided into two categories; namely those generated through aggregating pre-differentiated cells and those that form self-organizing structures during their in vitro differentiation from hPSCs. These models can either replicate aspects of cardiac development or enable the examination of interactions among constituent cell types, with some of these models showing increased maturity compared with 2D systems. Both groups have already emerged as physiologically relevant pre-clinical platforms for studying heart disease mechanisms, exhibiting key functional attributes of the human heart. In this review, we describe the different cardiac organoid models derived from hPSCs, their generation methods, applications in cardiovascular disease research and use in drug screening. We also address their current limitations and challenges as pre-clinical testing platforms and propose potential improvements to enhance their efficacy in cardiac drug discovery.

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.

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

Novel 2D/3D Hybrid Organoid System for High-Throughput Drug Screening in iPSC Cardiomyocytes

Human induced pluripotent stem cell cardiomyocytes (hiPSC-CMs) allow for high-throughput evaluation of cardiomyocyte (CM) physiology in health and disease. While multimodality testing provides a large breadth of information related to electrophysiology, contractility, and intracellular signaling in small populations of iPSC-CMs, current technologies for analyzing these parameters are expensive and resource-intensive. We sought to design a 2D/3D hybrid organoid system and harness optical imaging techniques to assess electromechanical properties, calcium dynamics, and signal propagation across CMs in a high-throughput manner. We validated our methods using a doxorubicin-based system, as the drug has well-characterized cardiotoxic, pro-arrhythmic effects. hiPSCs were differentiated into CMs, assembled into organoids, and thereafter treated with doxorubicin. The organoids were then replated to form a hybrid 2D/3D iPSC-CM construct where the 3D cardiac organoids acted as the source of electromechanical activity which propagated outwards into a 2D iPSC-CM sheet. The organoid recapitulated cardiac structure and connectivity, while 2D CMs facilitated analysis at an individual cellular level which recreated numerous doxorubicin-induced electrophysiologic and propagation abnormalities. Thus, we have developed a novel 2D/3D hybrid organoid model that employs an integrated optical analysis platform to provide a reliable high-throughput method for studying cardiotoxicity, providing valuable data on calcium, contractility, and signal propagation.

Application of Artificial Intelligence in Tissue Engineering

Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, biochemical factors, and biomaterials, it encounters challenges in designing complex 3D structures. Artificial intelligence (AI) enhances tissue engineering through computational modeling, biomaterial design, cell culture optimization, and personalized medicine. This review explores AI applications in organ tissue engineering (bone, heart, nerve, skin, cartilage), employing various machine learning (ML) algorithms for data analysis, prediction, and optimization. Each section discusses common ML algorithms and specific applications, emphasizing the potential and challenges in advancing regenerative therapies.

BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs

Goal: Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. Methods: We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). Results: Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. Conclusions: We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.

Advances in cardiac tissue engineering and heart-on-a-chip

Recent advances in both cardiac tissue engineering and hearts-on-a-chip are grounded in new biomaterial development as well as the employment of innovative fabrication techniques that enable precise control of the mechanical, electrical, and structural properties of the cardiac tissues being modelled. The elongated structure of cardiomyocytes requires tuning of substrate properties and application of biophysical stimuli to drive its mature phenotype. Landmark advances have already been achieved with induced pluripotent stem cell-derived cardiac patches that advanced to human testing. Heart-on-a-chip platforms are now commonly used by a number of pharmaceutical and biotechnology companies. Here, we provide an overview of cardiac physiology in order to better define the requirements for functional tissue recapitulation. We then discuss the biomaterials most commonly used in both cardiac tissue engineering and heart-on-a-chip, followed by the discussion of recent representative studies in both fields. We outline significant challenges common to both fields, specifically: scalable tissue fabrication and platform standardization, improving cellular fidelity through effective tissue vascularization, achieving adult tissue maturation, and ultimately developing cryopreservation protocols so that the tissues are available off the shelf.

AI-organoid integrated systems for biomedical studies and applications

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

Noninvasive analysis of contractility during identical maturations revealed two phenotypes in ventricular but not in atrial iPSC-CM

Patient-derived induced pluripotent stem cells (iPSCs) can be differentiated into atrial and ventricular cardiomyocytes to allow for personalized drug screening. A hallmark of differentiation is the manifestation of spontaneous beating in a two-dimensional (2-D) cell culture. However, an outstanding observation is the high variability in this maturation process. We valued that contractile parameters change during differentiation serving as an indicator of maturation. Consequently, we recorded noninvasively spontaneous motion activity during the differentiation of male iPSC toward iPSC cardiomyocytes (iPSC-CMs) to further analyze similar maturated iPSC-CMs. Surprisingly, our results show that identical differentiations into ventricular iPSC-CMs are variable with respect to contractile parameters resulting in two distinct subpopulations of ventricular-like cells. In contrast, differentiation into atrial iPSC-CMs resulted in only one phenotype. We propose that the noninvasive and cost-effective recording of contractile activity during maturation using a smartphone device may help to reduce the variability in results frequently reported in studies on ventricular iPSC-CMs.NEW & NOTEWORTHY Differentiation of induced pluripotent stem cells (iPSCs) into iPSC-derived cardiomyocytes (iPSC-CMs) exhibits a high variability in mature parameters. Here, we monitored noninvasively contractile parameters of iPSC-CM during full-time differentiation using a smartphone device. Our results show that parallel maturations of iPSCs into ventricular iPSC-CMs, but not into atrial iPSC-CMs, resulted in two distinct subpopulations of iPSC-CMs. These findings suggest that our cost-effective method may help to compare iPSC-CMs at the same maturation level.

Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients

Impaired relaxation of cardiomyocytes leads to diastolic dysfunction in the left ventricle. Relaxation velocity is regulated in part by intracellular calcium (Ca2+) cycling, and slower outflux of Ca2+ during diastole translates to reduced relaxation velocity of sarcomeres. Sarcomere length transient and intracellular calcium kinetics are integral parts of characterizing the relaxation behavior of the myocardium. However, a classifier tool that can separate normal cells from cells with impaired relaxation using sarcomere length transient and/or calcium kinetics remains to be developed. In this work, we employed nine different classifiers to classify normal and impaired cells, using ex-vivo measurements of sarcomere kinematics and intracellular calcium kinetics data. The cells were isolated from wild-type mice (referred to as normal) and transgenic mice expressing impaired left ventricular relaxation (referred to as impaired). We utilized sarcomere length transient data with a total of n = 126 cells (n = 60 normal cells and n = 66 impaired cells) and intracellular calcium cycling measurements with a total of n = 116 cells (n = 57 normal cells and n = 59 impaired cells) from normal and impaired cardiomyocytes as inputs to machine learning (ML) models for classification. We trained all ML classifiers with cross-validation method separately using both sets of input features, and compared their performance metrics. The performance of classifiers on test data showed that our soft voting classifier outperformed all other individual classifiers on both sets of input features, with 0.94 and 0.95 area under the receiver operating characteristic curves for sarcomere length transient and calcium transient, respectively, while multilayer perceptron achieved comparable scores of 0.93 and 0.95, respectively. However, the performance of decision tree, and extreme gradient boosting was found to be dependent on the set of input features used for training. Our findings highlight the importance of selecting appropriate input features and classifiers for the accurate classification of normal and impaired cells. Layer-wise relevance propagation (LRP) analysis demonstrated that the time to 50% contraction of the sarcomere had the highest relevance score for sarcomere length transient, whereas time to 50% decay of calcium had the highest relevance score for calcium transient input features. Despite the limited dataset, our study demonstrated satisfactory accuracy, suggesting that the algorithm can be used to classify relaxation behavior in cardiomyocytes when the potential relaxation impairment of the cells is unknown.

Rianú: Multi-tissue tracking software for increased throughput of engineered cardiac tissue screening

Background

The field of tissue engineering has provided valuable three-dimensional species-specific models of the human myocardium in the form of human Engineered Cardiac Tissues (hECTs) and similar constructs. However, hECT systems are often bottlenecked by a lack of openly available software that can collect data from multiple tissues at a time, even in multi-tissue bioreactors, which limits throughput in phenotypic and therapeutic screening applications.

Methods

We developed Rianú, an open-source web application capable of simultaneously tracking multiple hECTs on flexible end-posts. This software is operating system agnostic and deployable on a remote server, accessible via a web browser with no local hardware or software requirements. The software incorporates object-tracking capabilities for multiple objects simultaneously, an algorithm for twitch tracing analysis and contractile force calculation, and a data compilation system for comparative analysis within and amongst groups. Validation tests were performed using in-silico and in-vitro experiments for comparison with established methods and interventions.

Results

Rianú was able to detect the displacement of the flexible end-posts with a sub-pixel sensitivity of 0.555 px/post (minimum increment in post displacement) and a lower limit of 1.665 px/post (minimum post displacement). Compared to our established reference for contractility assessment, Rianú had a high correlation for all parameters analyzed (ranging from R 2 = 0.7514 to R 2 = 0.9695 ), demonstrating its high accuracy and reliability.

Conclusions

Rianú provides simultaneous tracking of multiple hECTs, expediting the recording and analysis processes, and simplifies time-based intervention studies. It also allows data collection from different formats and has scale-up capabilities proportional to the number of tissues per field of view. These capabilities will enhance throughput of hECTs and similar assays for in-vitro analysis in disease modeling and drug screening applications.

Organs-on-a-chip: a union of tissue engineering and microfabrication

We review the emergence of the new field of organ-on-a-chip (OOAC) engineering, from the parent fields of tissue engineering and microfluidics. We place into perspective the tools and capabilities brought into the OOAC field by early tissue engineers and microfluidics experts. Liver-on-a-chip and heart-on-a-chip are used as two case studies of systems that heavily relied on tissue engineering techniques and that were amongst the first OOAC models to be implemented, motivated by the need to better assess toxicity to human tissues in preclinical drug development. We review current challenges in OOAC that often stem from the same challenges in the parent fields, such as stable vascularization and drug absorption.

Intersection of stem cell biology and engineering towards next generation in vitro models of human fibrosis

Human fibrotic diseases constitute a major health problem worldwide. Fibrosis involves significant etiological heterogeneity and encompasses a wide spectrum of diseases affecting various organs. To date, many fibrosis targeted therapeutic agents failed due to inadequate efficacy and poor prognosis. In order to dissect disease mechanisms and develop therapeutic solutions for fibrosis patients, in vitro disease models have gone a long way in terms of platform development. The introduction of engineered organ-on-a-chip platforms has brought a revolutionary dimension to the current fibrosis studies and discovery of anti-fibrotic therapeutics. Advances in human induced pluripotent stem cells and tissue engineering technologies are enabling significant progress in this field. Some of the most recent breakthroughs and emerging challenges are discussed, with an emphasis on engineering strategies for platform design, development, and application of machine learning on these models for anti-fibrotic drug discovery. In this review, we discuss engineered designs to model fibrosis and how biosensor and machine learning technologies combine to facilitate mechanistic studies of fibrosis and pre-clinical drug testing.

Electrical stimulation through conductive scaffolds for cardiomyocyte tissue engineering: Systematic review and narrative synthesis

Electrical conductivity is of great significance to cardiac tissue engineering and permits the use of electrical stimulation in mimicking cardiac pacing. The development of biomaterials for tissue engineering can incorporate physical properties that are uncommon to standard cell culture and can facilitate improved cardiomyocyte function. In this review, the PICOT question asks, "How has the application of external electrical stimulation in conductive scaffolds for tissue engineering affected cardiomyocyte behavior in in vitro cell culture?" The Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, with predetermined inclusion and quality appraisal criteria, were used to assess publications from PubMed, Web of Science, and Scopus. Results revealed carbon nanotubes to be the most common conductive agent in biomaterials and rodent-sourced cell types as the most common cardiomyocytes used. To assess cardiomyocytes, immunofluorescence was used most often, utilizing proteins, such as connexin 43, cardiac α-actinin, and cardiac troponins. It was determined that the modal average stimulation protocol comprised 1-3 V square biphasic 50-ms pulses at 1 Hz, applied toward the end of cell culture. The addition of electrical stimulation to in vitro culture has exemplified it as a powerful tool for cardiac tissue engineering and brings researchers closer to creating optimal artificial cardiac tissue constructs.

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.

From engineered heart tissue to cardiac organoid

The advent of human pluripotent stem cells (hPSCs) presented a new paradigm to employ hPSC-derived cardiomyocytes (hPSC-CMs) in drug screening and disease modeling. However, hPSC-CMs differentiated in conventional two-dimensional systems are structurally and functionally immature. Moreover, these differentiation systems generate predominantly one type of cell. Since the heart includes not only CMs but other cell types, such monolayer cultures have limitations in simulating the native heart. Accordingly, three-dimensional (3D) cardiac tissues have been developed as a better platform by including various cardiac cell types and extracellular matrices. Two advances were made for 3D cardiac tissue generation. One type is engineered heart tissues (EHTs), which are constructed by 3D cell culture of cardiac cells using an engineering technology. This system provides a convenient real-time analysis of cardiac function, as well as a precise control of the input/output flow and mechanical/electrical stimulation. The other type is cardiac organoids, which are formed through self-organization of differentiating cardiac lineage cells from hPSCs. While mature cardiac organoids are more desirable, at present only primitive forms of organoids are available. In this review, we discuss various models of hEHTs and cardiac organoids emulating the human heart, focusing on their unique features, utility, and limitations.

Data analytics for cardiac diseases

In the present research we tackled the classification of seven genetic cardiac diseases and control subjects by using an extensive set of machine learning algorithms with their variations from simple K-nearest neighbor searching method to support vector machines. The research was based on calcium transient signals measured from induced pluripotent stem cell-derived cardiomyocytes. All in all, 55 different machine learning alternatives were used to model eight classes by applying the principle of 10-fold crossvalidation with the peak data of 1626 signals. The best classification accuracy of approximately 69% was given by random forests, which can be seen high enough here to show machine learning to be potential for the differentiation of the eight disease classes.

Organ-on-a-chip technology: a novel approach to investigate cardiovascular diseases

The development of organs-on-chip (OoC) has revolutionized in vitro cell-culture experiments by allowing a better mimicry of human physiology and pathophysiology that has consequently led researchers to gain more meaningful insights into disease mechanisms. Several models of hearts-on-chips and vessels-on-chips have been demonstrated to recapitulate fundamental aspects of the human cardiovascular system in the recent past. These 2D and 3D systems include synchronized beating cardiomyocytes in hearts-on-chips and vessels-on-chips with layer-based structures and the inclusion of physiological and pathological shear stress conditions. The opportunities to discover novel targets and to perform drug testing with chip-based platforms have substantially enhanced, thanks to the utilization of patient-derived cells and precise control of their microenvironment. These organ models will provide an important asset for future approaches to personalized cardiovascular medicine and improved patient care. However, certain technical and biological challenges remain, making the global utilization of OoCs to tackle unanswered questions in cardiovascular science still rather challenging. This review article aims to introduce and summarize published work on hearts- and vessels-on chips but also to provide an outlook and perspective on how these advanced in vitro systems can be used to tailor disease models with patient-specific characteristics.

Application of the Pluripotent Stem Cells and Genomics in Cardiovascular Research-What We Have Learnt and Not Learnt until Now

Personalized regenerative medicine and biomedical research have been galvanized and revolutionized by human pluripotent stem cells in combination with recent advances in genomics, artificial intelligence, and genome engineering. More recently, we have witnessed the unprecedented breakthrough life-saving translation of mRNA-based vaccines for COVID-19 to contain the global pandemic and the investment in billions of US dollars in space exploration projects and the blooming space-tourism industry fueled by the latest reusable space vessels. Now, it is time to examine where the translation of pluripotent stem cell research stands currently, which has been touted for more than the last two decades to cure and treat millions of patients with severe debilitating degenerative diseases and tissue injuries. This review attempts to highlight the accomplishments of pluripotent stem cell research together with cutting-edge genomics and genome editing tools and, also, the promises that have still not been transformed into clinical applications, with cardiovascular research as a case example. This review also brings to our attention the scientific and socioeconomic challenges that need to be effectively addressed to see the full potential of pluripotent stem cells at the clinical bedside.

On computational classification of genetic cardiac diseases applying iPSC cardiomyocytes

BACKGROUND

Cardiomyocytes differentiated from human induced pluripotent stem cells (iPSC-CMs) can be used to study genetic cardiac diseases. In patients these diseases are manifested e.g. with impaired contractility and fatal cardiac arrhythmias, and both of these can be due to abnormal calcium transients in cardiomyocytes. Here we classify different genetic cardiac diseases using Ca²⁺ transient data and different machine learning algorithms.

METHODS

By studying calcium cycling of disease-specific iPSC-CMs and by using calcium transients measured from these cells it is possible to classify diseases from each other and also from healthy controls by applying machine learning computation on the basis of peak attributes detected from calcium transient signals.

RESULTS

In the current research we extend our previous study having Ca-transient data from four different genetic diseases by adding data from two additional diseases (dilated cardiomyopathy and long QT Syndrome 2). We also study, in the light of the current data, possible differences and relations when machine learning modelling and classification accuracies were computed by using either leave-one-out test or 10-fold cross-validation.

CONCLUSIONS

Despite more complex classification tasks compared to our earlier research and having more different genetic cardiac diseases in the analysis, it is still possible to attain good disease classification results. As excepted, leave-one-out test and 10-fold cross-validation achieved virtually equal results.

Machine Learning Techniques to Classify Healthy and Diseased Cardiomyocytes by Contractility Profile

Cardiomyocytes derived from human induced pluripotent stem (iPS) cells enable the study of cardiac physiology and the developmental testing of new therapeutic drugs in a human setting. In parallel, machine learning methods are being applied to biomedical science in unprecedented ways. Machine learning has been used to distinguish healthy from diseased cardiomyocytes using calcium (Ca²⁺) transient signals. Most Ca²⁺ transient signals are obtained via terminal assays that do not permit longitudinal studies, although some recently developed options can circumvent these concerns. Here, we describe the use of machine learning to identify healthy and diseased cardiomyocytes according to their contractility profiles, which are derived from brightfield videos. This noncontact, label-free approach allows for the continued cultivation of cells after they have been evaluated for use in other assays and can be readily extended to organs-on-chip. To demonstrate utility, we assessed contractility profiles of cardiomyocytes obtained from patients with Timothy Syndrome (TS), a long QT disease which can lead to fatal arrhythmias, and from healthy individuals. The videos were processed and classified using machine learning methods and their performance was evaluated according to several parameters. The trained algorithms were able to distinguish the TS cardiomyocytes from healthy controls and classify two different healthy controls. The proposed computational machine learning evaluation of human iPS cell-derived cardiomyocytes' contractility profiles has the potential to identify other genetic proarrhythmic events, screen therapeutic agents for inducing or suppressing long QT events, and predict drug-target interactions. The same approach could be readily extended to the evaluation of engineered cardiac tissues within single-tissue and multi-tissue organs-on-chip.

Carbon Nanotube-Based Scaffolds for Cardiac Tissue Engineering-Systematic Review and Narrative Synthesis

Cardiovascular disease is currently the top global cause of death, however, research into new therapies is in decline. Tissue engineering is a solution to this crisis and in combination with the use of carbon nanotubes (CNTs), which have drawn recent attention as a biomaterial, could facilitate the development of more dynamic and complex in vitro models. CNTs' electrical conductivity and dimensional similarity to cardiac extracellular proteins provide a unique opportunity to deliver scaffolds with stimuli that mimic the native cardiac microenvironment in vitro more effectively. This systematic review aims to evaluate the use and efficacy of CNTs for cardiac tissue scaffolds and was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Three databases were searched: PubMed, Scopus, and Web of Science. Papers resulting from these searches were then subjected to analysis against pre-determined inclusion and quality appraisal criteria. From 249 results, 27 manuscripts met the criteria and were included in this review. Neonatal rat cardiomyocytes were most commonly used in the experiments, with multi-walled CNTs being most common in tissue scaffolds. Immunofluorescence was the experimental technique most frequently used, which was employed for the staining of cardiac-specific proteins relating to contractile and electrophysiological function.

Engineered cardiac tissues: a novel in vitro model to investigate the pathophysiology of mouse diabetic cardiomyopathy

Rodent diabetic models, used to understand the pathophysiology of diabetic cardiomyopathy (DCM), remain several limitations. Engineered cardiac tissues (ECTs) have emerged as robust 3D in vitro models to investigate structure-function relationships as well as cardiac injury and repair. Advanced glycation end-products (AGEs), produced through glycation of proteins or lipids in response to hyperglycemia, are important pathogenic factor for the development of DCM. In the current study, we developed a murine-based ECT model to investigate cardiac injury produced by AGEs. We treated ECTs composed of neonatal murine cardiac cells with AGEs and observed AGE-related functional, cellular, and molecular alterations: (1) AGEs (150 µg/mL) did not cause acute cytotoxicity, which displayed as necrosis detected by medium LDH release or apoptosis detected by cleaved caspase 3 and TUNEL staining, but negatively impacted ECT function on treatment day 9; (2) AGEs treatment significantly increased the markers of fibrosis (TGF-β, α-SMA, Ctgf, Collagen I-α1, Collagen III-α1, and Fn1) and hypertrophy (Nppa and Myh7); (3) AGEs treatment significantly increased ECT oxidative stress markers (3-NT, 4-HNE, HO-1, CAT, and SOD2) and inflammation response markers (PAI-1, TNF-α, NF-κB, and ICAM-1); and (4) AGE-induced pathogenic responses were all attenuated by pre-application of AGE receptor antagonist FPS-ZM1 (20 µM) or the antioxidant glutathione precursor N-acetylcysteine (5 mM). Therefore, AGEs-treated murine ECTs recapitulate the key features of DCM's functional, cellular and molecular pathogenesis, and may serve as a robust in vitro model to investigate cellular structure-function relationships, signaling pathways relevant to DCM and pharmaceutical intervention strategies.

Model Systems for Addressing Mechanism of Arrhythmogenesis in Cardiac Repair

PURPOSE OF REVIEW

Cardiac cell-based therapy represents a promising approach for cardiac repair. However, one of the main challenges is cardiac arrhythmias associated with stem cell transplantation. The current review summarizes the recent progress in model systems for addressing mechanisms of arrhythmogenesis in cardiac repair.

RECENT FINDINGS

Animal models have been extensively developed for mechanistic studies of cardiac arrhythmogenesis. Advances in human induced pluripotent stem cells (hiPSCs), patient-specific disease models, tissue engineering, and gene editing have greatly enhanced our ability to probe the mechanistic bases of cardiac arrhythmias. Additionally, recent development in multiscale computational studies and machine learning provides yet another powerful tool to quantitatively decipher the mechanisms of cardiac arrhythmias. Advancing efforts towards the integrations of experimental and computational studies are critical to gain insights into novel mitigation strategies for cardiac arrhythmias in cell-based therapy.

Generation, functional analysis and applications of isogenic three-dimensional self-aggregating cardiac microtissues from human pluripotent stem cells

Tissue-like structures from human pluripotent stem cells containing multiple cell types are transforming our ability to model and understand human development and disease. Here we describe a protocol to generate cardiomyocytes (CMs), cardiac fibroblasts (CFs) and cardiac endothelial cells (ECs), the three principal cell types in the heart, from human induced pluripotent stem cells (hiPSCs) and combine them in three-dimensional (3D) cardiac microtissues (MTs). We include details of how to differentiate, isolate, cryopreserve and thaw the component cells and how to construct and analyze the MTs. The protocol supports hiPSC-CM maturation and allows replacement of one or more of the three heart cell types in the MTs with isogenic variants bearing disease mutations. Differentiation of each cell type takes ~30 d, while MT formation and maturation requires another 20 d. No specialist equipment is needed and the method is inexpensive, requiring just 5,000 cells per MT.

Machine Learning in Arrhythmia and Electrophysiology

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.

From Human Pluripotent Stem Cells to 3D Cardiac Microtissues: Progress, Applications and Challenges

The knowledge acquired throughout the years concerning the in vivo regulation of cardiac development has promoted the establishment of directed differentiation protocols to obtain cardiomyocytes (CMs) and other cardiac cells from human pluripotent stem cells (hPSCs), which play a crucial role in the function and homeostasis of the heart. Among other developments in the field, the transition from homogeneous cultures of CMs to more complex multicellular cardiac microtissues (MTs) has increased the potential of these models for studying cardiac disorders in vitro and for clinically relevant applications such as drug screening and cardiotoxicity tests. This review addresses the state of the art of the generation of different cardiac cells from hPSCs and the impact of transitioning CM differentiation from 2D culture to a 3D environment. Additionally, current methods that may be employed to generate 3D cardiac MTs are reviewed and, finally, the adoption of these models for in vitro applications and their adaptation to medium- to high-throughput screening settings are also highlighted.

Modeling Cardiovascular Diseases with hiPSC-Derived Cardiomyocytes in 2D and 3D Cultures

In the last decade, the generation of cardiac disease models based on human-induced pluripotent stem cells (hiPSCs) has become of common use, providing new opportunities to overcome the lack of appropriate cardiac models. Although much progress has been made toward the generation of hiPSC-derived cardiomyocytes (hiPS-CMs), several lines of evidence indicate that two-dimensional (2D) cell culturing presents significant limitations, including hiPS-CMs immaturity and the absence of interaction between different cell types and the extracellular matrix. More recently, new advances in bioengineering and co-culture systems have allowed the generation of three-dimensional (3D) constructs based on hiPSC-derived cells. Within these systems, biochemical and physical stimuli influence the maturation of hiPS-CMs, which can show structural and functional properties more similar to those present in adult cardiomyocytes. In this review, we describe the latest advances in 2D- and 3D-hiPSC technology for cardiac disease mechanisms investigation, drug development, and therapeutic studies.

Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning

Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient signals and drug effects by means of machine learning, a central part of artificial intelligence. Drug effects were assessed in six iPSC-lines carrying different mutations causing catecholaminergic polymorphic ventricular tachycardia (CPVT), a highly malignant inherited arrhythmogenic disorder. The antiarrhythmic effect of dantrolene, an inhibitor of sarcoplasmic calcium release, was studied in iPSC-CMs after adrenaline, an adrenergic agonist, stimulation by machine learning analysis of calcium transient signals. First, beats of transient signals were identified with our peak recognition algorithm previously developed. Then 12 peak variables were computed for every identified peak of a signal and by means of this data signals were classified into different classes corresponding to those affected by adrenaline or, thereafter, affected by a drug, dantrolene. The best classification accuracy was approximately 79% indicating that machine learning methods can be utilized in analysis of iPSC-CM drug effects. In the future, data analysis of iPSC-CM drug effects together with machine learning methods can create a very valuable and efficient platform to individualize medication in addition to drug screening and cardiotoxicity studies.

Modeling the heart with Novoheart’s MyHeart™ platform

Reliable and predictive human-specific in vitro heart models can revolutionize drug discovery and development. With the advent of pluripotent stem cell technologies, human cardiomyocytes can now be readily produced in large quantities. Using tissue engineering techniques, they can be further assembled into cardiac tissues of specific 2D and 3D configurations, to create models that behave and function like the native human heart. Novoheart (BC, Canada) uniquely offers the MyHeartTM Platform of bioengineered human heart constructs, designed to provide researchers with effective models of either healthy or diseased human hearts. As in vitro, human-based assays become more widely accepted, the next decade could witness a shift away from animal testing towards more accurate and scalable human assays like the MyHeartTM Platform.

Neonatal Murine Engineered Cardiac Tissue Toxicology Model: Impact of Metallothionein Overexpression on Cadmium-Induced Injury

Engineered cardiac tissues (ECTs) serve as robust in vitro models to study human cardiac diseases including cardiac toxicity assays due to rapid structural and functional maturation and the ability to vary ECT composition. Metallothionein (MT) has been shown to be cardioprotective for environmental toxicants including heavy metals. To date, studies on the role of cardiomyocyte (CM)-specific MT expression and function have occurred in dissociated single cell assays or expensive in vivo small animal models. Therefore, we generated 3D ECTs using neonatal mouse ventricular cells from wild-type (WT) and the CM-specific overexpressing MT-transgenic (MT-TG) to determine the effect of MT overexpression on ECT maturation and function. Because Cadmium (Cd) is an environmentally prevalent heavy metal toxicant with direct negative impact on cardiac structure and function, we then determined the effect of MT overexpression to reduce Cd mediated CM toxicity within ECTs. We found: (1) structural and functional maturation was similar in WT and MT-TG ECTs; (2) Cd exposure negatively impacted ECT cell survival, maturation, and function; and (3) MT-ECTs showed reduced Cd toxicity as defined by reduced cleaved caspase 3, reduced Bax/Bcl2 ratio, reduced TdT-mediated dUTP nick-end labeling positive cells, reduced CM loss after Cd treatment, and delayed onset of cardiac dysfunction after Cd treatment. Thus, neonatal murine ECTs can serve as a robust in vitro model for heavy metal toxicity screening and as a platform to evaluate the role cardioprotective mechanisms, such as the MT-TG model, on environmentally relevant toxicants.

Development of a Human iPSC Cardiomyocyte-Based Scoring System for Cardiac Hazard Identification in Early Drug Safety De-risking

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as a promising cardiac safety platform, demonstrated by numerous validation studies using drugs with known cardiac adverse effects in humans. However, the challenge remains to implement hiPSC-CMs into cardiac de-risking of new chemical entities (NCEs) during preclinical drug development. Here, we used the calcium transient screening assay in hiPSC-CMs to develop a hazard score system for cardiac electrical liabilities. Tolerance interval calculations and evaluation of different classes of cardio-active drugs enabled us to develop a weighted scoring matrix. This approach allowed the translation of various pharmacological effects in hiPSC-CMs into a single hazard label (no, low, high, or very high hazard). Evaluation of 587 internal NCEs and good translation to ex vivo and in vivo models for a subset of these NCEs highlight the value of the cardiac hazard scoring in facilitating the selection of compounds during early drug safety screening.

•

Scoring system identifies different degrees of cardiac hazard

•

Can be applied within R&D to cardiac safety screening of NCEs

•

Controls and reference drugs are essential for development of scoring matrix

•

Analysis can be applied to other in vitro drug safety assays

Considerations for an In Vitro, Cell-Based Testing Platform for Detection of Adverse Drug-Induced Inotropic Effects in Early Drug Development. Part 1: General Considerations for Development of Novel Testing Platforms

Drug-induced effects on cardiac contractility can be assessed through the measurement of the maximal rate of pressure increase in the left ventricle (LVdP/dt_max) in conscious animals, and such studies are often conducted at the late stage of preclinical drug development. Detection of such effects earlier in drug research using simpler, in vitro test systems would be a valuable addition to our strategies for identifying the best possible drug development candidates. Thus, testing platforms with reasonably high throughput, and affordable costs would be helpful for early screening purposes. There may also be utility for testing platforms that provide mechanistic information about how a given drug affects cardiac contractility. Finally, there could be in vitro testing platforms that could ultimately contribute to the regulatory safety package of a new drug. The characteristics needed for a successful cell or tissue-based testing platform for cardiac contractility will be dictated by its intended use. In this article, general considerations are presented with the intent of guiding the development of new testing platforms that will find utility in drug research and development. In the following article (part 2), specific aspects of using human-induced stem cell-derived cardiomyocytes for this purpose are addressed.

Cardiac tissue engineering: state-of-the-art methods and outlook

The purpose of this review is to assess the state-of-the-art fabrication methods, advances in genome editing, and the use of machine learning to shape the prospective growth in cardiac tissue engineering. Those interdisciplinary emerging innovations would move forward basic research in this field and their clinical applications. The long-entrenched challenges in this field could be addressed by novel 3-dimensional (3D) scaffold substrates for cardiomyocyte (CM) growth and maturation. Stem cell-based therapy through genome editing techniques can repair gene mutation, control better maturation of CMs or even reveal its molecular clock. Finally, machine learning and precision control for improvements of the construct fabrication process and optimization in tissue-specific clonal selections with an outlook of cardiac tissue engineering are also presented.

Disease modeling of cardiac arrhythmias using human induced pluripotent stem cells

INTRODUCTION

Inherited arrhythmias are an uncommon, but malignant family of cardiac diseases that result from genetic abnormalities in the ion channels and/or structural proteins within cardiomyocytes. Given the inherent differences between species and the limited reproducibility of in vitro heterologous cell models, progress in understanding the mechanisms underlying these malignant diseases has always languished far behind the clinical science and need. The ability to study human induced pluripotent stem cells (iPSCs) derived cardiomyocytes promises to change this paradigm as patient cells have the potential to become testing platforms for disease phenotyping or therapeutic discovery.

AREAS COVERED

This review will outline methods developed to genetically reprogram adult cells into iPSCs, differentiate iPSCs into ex vivo models of adult cardiac tissue and iPSCs-based progress in exploring the mechanisms underlying pro-arrhythmic disease phenotypes.

EXPERT OPINION

Despite being discovered less than 15 years ago, several studies have successfully leveraged iPSCs-derived cardiomyocytes to study malignant arrhythmogenic diseases. These models promise to increase our understanding of the pathophysiology underlying these complex diseases and may identify personalized approaches to treatment.

Introduction to biological complexity as a missing link in drug discovery

INTRODUCTION

Despite a burgeoning knowledge of the intricacies and mechanisms responsible for human disease, technological advances in medicinal chemistry, and more efficient assays used for drug screening, it remains difficult to discover novel and effective pharmacologic therapies. Areas covered: By reference to the primary literature and concepts emerging from academic and industrial drug screening landscapes, the authors propose that this disconnect arises from the inability to scale and integrate responses from simpler model systems to outcomes from more complex and human-based biological systems. Expert opinion: Further collaborative efforts combining target-based and phenotypic-based screening along with systems-based pharmacology and informatics will be necessary to harness the technological breakthroughs of today to derive the novel drug candidates of tomorrow. New questions must be asked of enabling technologies-while recognizing inherent limitations-in a way that moves drug development forward. Attempts to integrate mechanistic and observational information acquired across multiple scales frequently expose the gap between our knowledge and our understanding as the level of complexity increases. We hope that the thoughts and actionable items highlighted will help to inform the directed evolution of the drug discovery process.