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

Michael P. Schwartz, Zhonggang Hou, Nicholas E. Propson, Jue Zhang, Collin J. Engstrom, Vitor Santos Costa, Peng Jiang, Bao Kim Nguyen, Jennifer M. Bolin, William Daly, Yu Wang, Ron Stewart, C. David Page, William L. Murphy, and James A. Thomson
PNAS published ahead of print September 21, 2015 https://doi.org/10.1073/pnas.1516645112
Michael P. Schwartz
aDepartment of Biomedical Engineering, University of Wisconsin, Madison, WI 53706;
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Zhonggang Hou
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
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Nicholas E. Propson
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
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Jue Zhang
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
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Collin J. Engstrom
cDepartment of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53792;dDepartment of Computer Sciences, University of Wisconsin, Madison, WI 53706;
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Vitor Santos Costa
eCenter 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;
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Peng Jiang
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
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Bao Kim Nguyen
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
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Jennifer M. Bolin
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
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William Daly
aDepartment of Biomedical Engineering, University of Wisconsin, Madison, WI 53706;
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Yu Wang
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
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Ron Stewart
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
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C. David Page
cDepartment of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53792;dDepartment of Computer Sciences, University of Wisconsin, Madison, WI 53706;
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William L. Murphy
aDepartment of Biomedical Engineering, University of Wisconsin, Madison, WI 53706;fDepartment of Orthopedics and Rehabilitation, University of Wisconsin, Madison, WI 53705;
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James A. Thomson
bRegenerative Biology, Morgridge Institute for Research, Madison, WI 53715;gDepartment of Cell and Regenerative Biology, University of Wisconsin, Madison, WI 53705;hDepartment of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA 93106
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  • For correspondence: jthomson@morgridge.org
  1. Contributed by James A. Thomson, August 26, 2015 (sent for review April 3, 2015; reviewed by Fred H. Gage and Russell Thomas)

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Significance

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.

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.

  • organoid
  • machine learning
  • tissue engineering
  • differentiation
  • toxicology

Footnotes

  • ↵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.

  • ↵4To whom correspondence should be addressed. Email: jthomson{at}morgridge.org.
  • 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.

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A human tissue model for predicting neurotoxicity
Michael P. Schwartz, Zhonggang Hou, Nicholas E. Propson, Jue Zhang, Collin J. Engstrom, Vitor Santos Costa, Peng Jiang, Bao Kim Nguyen, Jennifer M. Bolin, William Daly, Yu Wang, Ron Stewart, C. David Page, William L. Murphy, James A. Thomson
Proceedings of the National Academy of Sciences Sep 2015, 201516645; DOI: 10.1073/pnas.1516645112

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A human tissue model for predicting neurotoxicity
Michael P. Schwartz, Zhonggang Hou, Nicholas E. Propson, Jue Zhang, Collin J. Engstrom, Vitor Santos Costa, Peng Jiang, Bao Kim Nguyen, Jennifer M. Bolin, William Daly, Yu Wang, Ron Stewart, C. David Page, William L. Murphy, James A. Thomson
Proceedings of the National Academy of Sciences Sep 2015, 201516645; DOI: 10.1073/pnas.1516645112
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