Human pluripotent stem cell-derived neural constructs for predicting neural toxicity

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, endothe...

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Published in:Proceedings of the National Academy of Sciences - PNAS 2015-10, Vol.112 (40), p.12516-12521
Main Authors: Schwartz, Michael P., Hou, Zhonggang, Propson, Nicholas E., Zhang, Jue, Engstrom, Collin J., Costa, Vitor Santos, Jiang, Peng, Nguyen, Bao Kim, Bolin, Jennifer M., Daly, William, Wang, Yu, Stewart, Ron, Page, C. David, Murphy, William L., Thomson, James A.
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Language:English
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Summary: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 hold-out 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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1516645112