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Proc Natl Acad Sci U S A. 2015 Oct 6;112(40):12516-21. doi: 10.1073/pnas.1516645112. Epub 2015 Sep 21.

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

Author information

1
Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53706;
2
Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
3
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53792; Department of Computer Sciences, University of Wisconsin, Madison, WI 53706;
4
Center for Research in Advanced Computing Systems, Institute for Systems and Computer Engineering, Technology and Science, and Department of Computer Science, Faculty of Sciences, University of Porto, Porto 4169-007, Portugal;
5
Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53706; Department of Orthopedics and Rehabilitation, University of Wisconsin, Madison, WI 53705;
6
Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715; Department of Cell and Regenerative Biology, University of Wisconsin, Madison, WI 53705; Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA 93106 jthomson@morgridge.org.

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.

KEYWORDS:

differentiation; machine learning; organoid; tissue engineering; toxicology

PMID:
26392547
PMCID:
PMC4603492
DOI:
10.1073/pnas.1516645112
[Indexed for MEDLINE]
Free PMC Article

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