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Here, we report the derivation of arterial endothelial cells from human pluripotent stem cells that exhibit arterial-specific functions in vitro and in vivo. We combine single-cell RNA sequencing of embryonic mouse endothelial cells with an EFNB2-tdTomato/EPHB4-EGFP dual reporter human embryonic stem cell line to identify factors that regulate arterial endothelial cell specification. The resulting xeno-free protocol produces cells with gene expression profiles, oxygen consumption rates, nitric oxide production levels, shear stress responses, and TNFα-induced leukocyte adhesion rates characteristic of arterial endothelial cells. Arterial endothelial cells were robustly generated from multiple human embryonic and induced pluripotent stem cell lines and have potential applications for both disease modeling and regenerative medicine. [ABSTRACT FROM AUTHOR]

Human pluripotent stem cell-based in vitro models that reflect human physiology have the potential to reduce the number of drug failures in clinical trials and offer a cost-effective approach for assessing chemical safety. Here, human embryonic stem (ES) cell-derived neural progenitor cells, endothelial cells, mesenchymal stem cells, and microglia/ macrophage precursors were combined on chemically defined polyethylene glycol hydrogels and cultured in serum-free medium to model cellular interactions within the developing brain. The precursors self-assembled into 3D neural constructs with diverse neuronal and glial populations, interconnected vascular networks, and ramified microglia. Replicate constructs were reproducible by RNA sequencing (RNA-Seq) and expressed neurogenesis, vasculature development, and microglia genes. Linear support vector machines were used to construct a predictive model from RNA-Seq data for 240 neural constructs treated with 34 toxic and 26 nontoxic chemicals. The predictive model was evaluated using two standard hold-out testing methods: a nearly unbiased leave-one-out cross-validation for the 60 training compounds and an unbiased blinded trial using a single holdout set of 10 additional chemicals. The linear support vector produced an estimate for future data of 0.91 in the cross-validation experiment and correctly classified 9 of 10 chemicals in the blinded trial. [ABSTRACT FROM AUTHOR]