Human Pluripotent Stem Cell-Derived Hepatocytes Show Higher Transcriptional Correlation with Adult Liver Tissue than with Fetal Liver Tissue

Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to, that is, distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation toward hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

RGD density along with substrate stiffness regulate hPSC hepatocyte functionality through YAP signalling

Human pluripotent stem cell-derived hepatocytes (hPSC-Heps) may be suitable for treating liver diseases, but differentiation protocols often fail to yield adult-like cells. We hypothesised that replicating healthy liver niche biochemical and biophysical cues would produce hepatocytes with desired metabolic functionality. Using 2D synthetic hydrogels which independently control mechanical properties and biochemical cues, we found that culturing hPSC-Heps on surfaces matching the stiffness of fibrotic liver tissue upregulated expression of genes for RGD-binding integrins, and increased expression of YAP/TAZ and their transcriptional targets. Alternatively, culture on soft, healthy liver-like substrates drove increases in cytochrome p450 activity and ureagenesis. Knockdown of ITGB1 or reducing RGD-motif-containing peptide concentration in stiff hydrogels reduced YAP activity and improved metabolic functionality; however, on soft substrates, reducing RGD concentration had the opposite effect. Furthermore, targeting YAP activity with verteporfin or forskolin increased cytochrome p450 activity, with forskolin dramatically enhancing urea synthesis. hPSC-Heps could also be successfully encapsulated within RGD peptide-containing hydrogels without negatively impacting hepatic functionality, and compared to 2D cultures, 3D cultured hPSC-Heps secreted significantly less fetal liver-associated alpha-fetoprotein, suggesting furthered differentiation. Our platform overcomes technical hurdles in replicating the liver niche, and allowed us to identify a role for YAP/TAZ-mediated mechanosensing in hPSC-Hep differentiation.