Contributed by James A. Thomson, August 26, 2015 (sent for review April 3, 2015; reviewed by Fred H. Gage and Russell Thomas)
Stem cell biology, tissue engineering, bioinformatics, and machine learning were combined to implement an in vitro human cellular model for developmental neurotoxicity screening. Human pluripotent stem cell-derived neural tissue constructs with vascular networks and microglia were produced with high sample uniformity by combining precursor cells on synthetic hydrogels using standard culture techniques. Machine learning was used to build a predictive model from changes in global gene expression for neural constructs exposed to 60 toxic and nontoxic training chemicals. The model correctly classified 9 of 10 additional chemicals in a blinded trial. This combined strategy demonstrates the value of human cell-based assays for predictive toxicology and should be useful for both drug and chemical safety assessment.
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.
↵1M.P.S. and Z.H. contributed equally to this work.
↵2Present address: Department of Cell Biology, Harvard Medical School, Boston, MA 02115.
↵3Present address: State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
Author contributions: M.P.S., Z.H., N.E.P., J.Z., R.S., C.D.P., W.L.M., and J.A.T. designed research; M.P.S., Z.H., N.E.P., J.Z., C.J.E., V.S.C., B.K.N., J.M.B., W.D., and Y.W. performed research; M.P.S. and Z.H. contributed new reagents/analytic tools; M.P.S., Z.H., N.E.P., J.Z., C.J.E., V.S.C., P.J., R.S., and C.D.P. analyzed data; and M.P.S., Z.H., C.D.P., and J.A.T. wrote the paper.
Reviewers: F.H.G., The Salk Institute for Biological Studies; and R.T., Environmental Protection Agency.
Conflict of interest statement: W.L.M. is a founder and stockholder for Stem Pharm, Inc., and Tissue Regeneration Systems, Inc.
Data deposition: The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (GEO) (Edgar et al., 2002) and are accessible through GEO Series accession number GSE63935 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=wfepekigrfqbfot&acc=GSE63935).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1516645112/-/DCSupplemental.
Freely available online through the PNAS open access option.
