Classification of cardiac differentiation outcome, percentage of cardiomyocytes on day 10 of differentiation, for hydrogel‐encapsulated hiPSCs

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.

A review on machine learning approaches in cardiac tissue engineering

“…Similarly, Mohammadi et al studied random forest, GP classification, and SVM methods to identify important differentiation factors and conduct quality checks on hiPSC-CM embedded PEG-fibrinogen microspheres (Mohammadi et al, 2022). While extruded cardiac microspheres enhance 3D culture and differentiation efficiency, potentially facilitating scalable hiPSC-CM production, they may affect CM purity due to variations in physical properties.…”

“…Two models were created to predict the percentage of CM content on Day 10 of differentiation, using pre-differentiation experimental features and nondestructive images from Days 3 or 5. One CNN utilized only phase-contrast image, while another incorporated a combination of both images and the features identified in earlier work (Mohammadi et al, 2022). With an accuracy of 85% and precision of 92%, the best-performing model was the combination CNN using images from Day 5, outperforming the other ML models, including GP with features only, SVM with images only, and SVM with a combination of images and features.…”

Editorial

“…Mohammadi et al [ 3 ] develop a computational methodology to assist process optimization for cell‐based therapies. Specifically, they apply machine learning to identify the critical experimental parameters that control the yield of cardiomyocytes post‐differentiation of human induced pluripotent stem cells.…”