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Cardiomyocytes (CMs) are heart cells responsible for heart contraction and relaxation. CMs can be derived from human induced pluripotent stem cells (hiPSCs) with high yield and purity. Mature CMs can potentially replace dead and dysfunctional cardiac tissue and be used for screening cardiac drugs and toxins. However, hiPSCs-derived CMs (hiPSC-CMs) are immature, which limits their utilization. Therefore, it is crucial to understand how experimental variables, especially tunable ones, of hiPSC expansion and differentiation phases affect the hiPSC-CM maturity stage. This study applied clustering algorithms to day 30 cardiac differentiation data to investigate if any maturity-related cell features could be related to the experimental variables. The best models were obtained using k-means and Gaussian mixture model clustering algorithms based on the evaluation metrics. They grouped the cells based on eccentricity and elongation. The cosine similarity between the clustering results and the experimental parameters revealed that the Gaussian mixture model results have strong similarities of 0.88, 0.94, and 0.93 with axial ratio, diameter, and cell concentration. 

Cardiovascular diseases (CVD) are the leading cause of death worldwide. Engineered heart tissue produced by differentiation of human induced pluripotent stem cells may provide an encompassing treatment for heart failure due to CVD. However, considerable difficulties exist in producing the large number of cardiomyocytes needed for therapeutic purposes through differentiation protocols. Data-driven modeling with machine learning techniques has the potential to identify factors that significantly affect the outcomes of these differentiation experiments. Using data from previous cardiac differentiation experiments, we have developed data-driven modeling methods for determining which experimental conditions are most influential on the final cardiomyocyte content of a differentiation experiment. With those identified conditions, we were able to build classification models that can predict whether an experiment will have a sufficient cardiomyocyte content to continue with the experiment on the seventh (out of 10) day of the differentiation with a 90% accuracy. This early failure prediction will provide cost and time savings, as each day the differentiation continues requires significant resources. 

Abstract This study employed machine learning (ML) models to predict the cardiomyocyte (CM) content following differentiation of human induced pluripotent stem cells (hiPSCs) encapsulated in hydrogel microspheroids and to identify the main experimental variables affecting the CM yield. Understanding how to enhance CM generation using hiPSCs is critical in moving toward large‐scale production and implementing their use in developing therapeutic drugs and regenerative treatments. Cardiomyocyte production has entered a new era with improvements in the differentiation process. However, existing processes are not sufficiently robust for reliable CM manufacturing. Using ML techniques to correlate the initial, experimentally specified stem cell microenvironment's impact on cardiac differentiation could identify important process features. The initial tunable (controlled) input features for training ML models were extracted from 85 individual experiments. Subsets of the controlled input features were selected using feature selection and used for model construction. Random forests, Gaussian process, and support vector machines were employed as the ML models. The models were built to predict two classes of sufficient and insufficient for CM content on differentiation day 10. The best model predicted the sufficient class with an accuracy of 75% and a precision of 71%. The identified key features including post‐freeze passage number, media type, PF fibrinogen concentration, CHIR/S/V, axial ratio, and cell concentration provided insight into the significant experimental conditions. This study showed that we can extract information from the experiments and build predictive models that could enhance the cell production process by using ML techniques. 

Abstract Cardiovascular disease is the leading cause of death worldwide, and current treatments are ineffective or unavailable to majority of patients. Engineered cardiac tissue (ECT) is a promising treatment to restore function to the damaged myocardium; however, for these treatments to become a reality, tissue fabrication must be amenable to scalable production and be used in suspension culture. Here, we have developed a low‐cost and scalable emulsion‐based method for producing ECT microspheres from poly(ethylene glycol) (PEG)–fibrinogen encapsulated mouse embryonic stem cells (mESCs). Cell‐laden microspheres were formed via water‐in‐oil emulsification; encapsulation occurred by suspending the cells in hydrogel precursor solution at cell densities from 5 to 60 million cells/ml, adding to mineral oil and vortexing. Microsphere diameters ranged from 30 to 570 μm; size variability was decreased by the addition of 2% poly(ethylene glycol) diacrylate. Initial cell encapsulation density impacted the ability for mESCs to grow and differentiate, with the greatest success occurring at higher cell densities. Microspheres differentiated into dense spheroidal ECTs with spontaneous contractions occurring as early as Day 10 of cardiac differentiation; furthermore, these ECT microspheres exhibited appropriate temporal changes in gene expression and response to pharmacological stimuli. These results demonstrate the ability to use an emulsion approach to encapsulate pluripotent stem cells for use in microsphere‐based cardiac differentiation.