| Literature DB >> 26392547 |
Michael P Schwartz1, Zhonggang Hou2, Nicholas E Propson2, Jue Zhang2, Collin J Engstrom3, Vitor Santos Costa4, Peng Jiang2, Bao Kim Nguyen2, Jennifer M Bolin2, William Daly1, Yu Wang2, Ron Stewart2, C David Page3, William L Murphy5, James A Thomson6.
Abstract
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.Entities:
Keywords: differentiation; machine learning; organoid; tissue engineering; toxicology
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Year: 2015 PMID: 26392547 PMCID: PMC4603492 DOI: 10.1073/pnas.1516645112
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 11.205