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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. 

Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate for several applications, including surface approximation and surrogate-based optimization. Many techniques have been developed for surrogate modeling; however, a systematic method for selecting suitable techniques for an application remains an open challenge. This work compares the performance of eight surrogate modeling techniques for approximating a surface over a set of simulated data. Using the comparison results, we constructed a Random Forest based tool to recommend the appropriate surrogate modeling technique for a given dataset using attributes calculated only from the available input and output values. The tool identifies the appropriate surrogate modeling techniques for surface approximation with an accuracy of 87% and a precision of 86%. Using the tool for surrogate model form selection enables computational time savings by avoiding expensive trial-and-error selection methods.