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.

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.

Probability model for estimating colorectal polyp progression rates.

According to the American Cancer Society, colorectal cancer (CRC) is the third most common cause of cancer related deaths in the United States. Experts estimate that about 85% of CRCs begin as precancerous polyps, early detection and treatment of which can significantly reduce the risk of CRC. Hence, it is imperative to develop population-wide intervention strategies for early detection of polyps. Development of such strategies requires precise values of population-specific rates of incidence of polyp and its progression to cancerous stage. There has been a considerable amount of research in recent years on developing screening based CRC intervention strategies. However, these are not supported by population-specific mathematical estimates of progression rates. This paper addresses this need by developing a probability model that estimates polyp progression rates considering race and family history of CRC; note that, it is ethically infeasible to obtain polyp progression rates through clinical trials. We use the estimated rates to simulate the progression of polyps in the population of the State of Indiana, and also the population of a clinical trial conducted in the State of Minnesota, which was obtained from literature. The results from the simulations are used to validate the probability model.