Differentiating Engineered Tissue Images and Experimental Factors to Classify Cardiomyocyte Content.

In this study, we used machine learning (ML) to classify the cardiomyocyte (CM) content on day 10 of the differentiation of human-induced pluripotent stem cell (hiPSC)-laden microspheroids using easily acquirable nondestructive phase-contrast images taken in the middle of differentiation and tunable experimental parameters. Scale-up suspension culture, use of engineered tissues to support stem cell differentiation, and CM production for improved control over cellular microenvironment in the suspension system need nondestructive methods to track engineered tissue development. The ability to couple images that capture experimenter perceived "good" or "bad" batches based on visualization at early differentiation time points with actual experimental outcomes in an unbiased way is a step toward building these methods. In recent years, ML techniques have been successfully applied to identify critical process parameters and use this information to build models that describe process outcomes in cell production and hiPSC differentiation. Building upon these successes, here, we utilize convolutional neural networks (CNNs) to build a binary classifier model for CM content on differentiation day 10 (dd10) for hiPSC-CMs. We consider two separate data sets as potential input features for the classification models. The first set includes phase-contrast images of microspheroid tissues taken on days 3 and 5 of the differentiation batches at different experimental conditions. The second set supplements the images with tunable experimental differentiation parameters, such as cell concentration and microspheroids' size. The CM content classes were sufficient and insufficient. The accuracy of the CNN classifier using images only was 63%. The addition of experimental features increased the accuracy to 85%, indicating the importance of tunable parameters in predicting CM content. Impact statement Machine learning approaches were used to predict the final cardiomyocyte (CM) content class (sufficient vs. insufficient) of engineered cardiac tissue microspheroids produced through suspension-based cardiac differentiation of human-induced pluripotent stem cell-laden engineered tissue microspheroids. The models used specified experimental features and data collected using nondestructive inexpensive methods, specifically phase-contrast images taken during the initial days of differentiation as inputs. The best model was a convolutional neural network trained using experimental features and differentiation day 5 images. It classified the CM content with 85% accuracy and replicated and formalized experimenter's visual intuition about differentiation outcomes by incorporating images from early time points.

Human Induced Pluripotent Stem Cell Encapsulation Geometry Impacts Three-Dimensional Developing Human Engineered Cardiac Tissue Functionality.

Cardiac tissue engineering has been working to alleviate the immense burden of cardiovascular disease for several decades. To improve cardiac tissue homogeneity and cardiomyocyte (CM) maturation, in this study, we investigated altering initial encapsulation geometry in a three-dimensional (3D) direct cardiac differentiation platform. Traditional engineered cardiac tissue production utilizes predifferentiated CMs to produce 3D cardiac tissue and often involves various cell selection and exogenous stimulation methods to promote CM maturation. Starting tissue formation directly with human induced pluripotent stem cells (hiPSCs), rather than predifferentiated CMs, simplifies the engineered cardiac tissue formation process, making it more applicable for widespread implementation and scale-up. In this study, hiPSCs were encapsulated in poly (ethylene glycol)-fibrinogen in three tissue geometries (disc-shaped microislands, squares, and rectangles) and subjected to established cardiac differentiation protocols. Resulting 3D engineered cardiac tissues (3D-ECTs) from each geometry displayed similar CM populations (∼65%) and gene expression over time. Notably, rectangular tissues displayed less tissue heterogeneity and suggested more advanced features of maturing CMs, including myofibrillar alignment and Z-line formation. In addition, rectangular tissue showed significantly higher anisotropic contractile properties compared to square and microisland tissues (MI 0.28 ± 0.03, SQ 0.35 ± 0.05, RT 0.79 ± 0.04). This study demonstrates a straightforward method for simplifying and improving 3D-ECT production without the use of exogenous mechanical or electrical pacing and has the potential to be utilized in bioprinting and drug testing applications. Impact statement Current methods for improving cardiac maturation postdifferentiation remain tedious and complex. In this study, we examined the impact of initial encapsulation geometry on improvement of three-dimensional engineered cardiac tissue (3D-ECT) production and postdifferentiation maturation for three tissue geometries, including disc-shaped microislands, squares, and rectangles. Notably, rectangular 3D-ECTs displayed less tissue heterogeneity and more advanced features of maturing cardiomyocytes, including myofibrillar alignment, Z-line formation, and anisotropic contractile properties, compared to microisland and square tissues. This study demonstrates an initial human induced pluripotent stem cell-encapsulated rectangular tissue geometry can improve cardiac maturation, rather than implementing cell selection or tedious postdifferentiation manipulation, including exogenous mechanical and/or electrical pacing.

Engineered cardiac tissue microsphere production through direct differentiation of hydrogel-encapsulated human pluripotent stem cells.

Engineered cardiac tissues that can be directly produced from human induced pluripotent stem cells (hiPSCs) in scalable, suspension culture systems are needed to meet the demands of cardiac regenerative medicine. Here, we demonstrate successful production of functional cardiac tissue microspheres through direct differentiation of hydrogel encapsulated hiPSCs. To form the microspheres, hiPSCs were suspended within the photocrosslinkable biomaterial, PEG-fibrinogen (25 million cells/mL), and encapsulated at a rate of 420,000 cells/minute using a custom microfluidic system. Even at this high cell density and rapid production rate, high intra-batch and batch-to-batch reproducibility was achieved. Following microsphere formation, hiPSCs maintained high cell viability and continued to grow within and beyond the original PEG-fibrinogen matrix. These initially soft microspheres (<250 Pa) supported efficient cardiac differentiation; spontaneous contractions initiated by differentiation day 8, and the microspheres contained >75% cardiomyocytes (CMs). CMs responded appropriately to pharmacological stimuli and exhibited 1:1 capture up to 6.0 Hz when electrically paced. Over time, cells formed cell-cell junctions and aligned myofibril fibers; engineered cardiac microspheres were maintained in culture over 3 years. The capability to rapidly generate uniform cardiac microsphere tissues is critical for advancing downstream applications including biomanufacturing, multi-well plate drug screening, and injection-based regenerative therapies.

Prediction of Human Induced Pluripotent Stem Cell Cardiac Differentiation Outcome by Multifactorial Process Modeling.

Human cardiomyocytes (CMs) have potential for use in therapeutic cell therapy and high-throughput drug screening. Because of the inability to expand adult CMs, their large-scale production from human pluripotent stem cells (hPSC) has been suggested. Significant improvements have been made in understanding directed differentiation processes of CMs from hPSCs and their suspension culture-based production at chemically defined conditions. However, optimization experiments are costly, time-consuming, and highly variable, leading to challenges in developing reliable and consistent protocols for the generation of large CM numbers at high purity. This study examined the ability of data-driven modeling with machine learning for identifying key experimental conditions and predicting final CM content using data collected during hPSC-cardiac differentiation in advanced stirred tank bioreactors (STBRs). Through feature selection, we identified process conditions, features, and patterns that are the most influential on and predictive of the CM content at the process endpoint, on differentiation day 10 (dd10). Process-related features were extracted from experimental data collected from 58 differentiation experiments by feature engineering. These features included data continuously collected online by the bioreactor system, such as dissolved oxygen concentration and pH patterns, as well as offline determined data, including the cell density, cell aggregate size, and nutrient concentrations. The selected features were used as inputs to construct models to classify the resulting CM content as being "sufficient" or "insufficient" regarding pre-defined thresholds. The models built using random forests and Gaussian process modeling predicted insufficient CM content for a differentiation process with 90% accuracy and precision on dd7 of the protocol and with 85% accuracy and 82% precision at a substantially earlier stage: dd5. These models provide insight into potential key factors affecting hPSC cardiac differentiation to aid in selecting future experimental conditions and can predict the final CM content at earlier process timepoints, providing cost and time savings. This study suggests that data-driven models and machine learning techniques can be employed using existing data for understanding and improving production of a specific cell type, which is potentially applicable to other lineages and critical for realization of their therapeutic applications.

Emulsion-based encapsulation of pluripotent stem cells in hydrogel microspheres for cardiac differentiation.

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.