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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
  • , P. Jiang
  • , Bao Kim Nguyen
  • , Jennifer M. Bolin
  • , William Daly
  • , Yu Wang
  • , Ron Stewart
  • , C. David Page
  • , William L. Murphy
  • , James A. Thomson
  • University of Wisconsin
  • Morgridge Institute for Research
  • Harvard Medical School
  • University of Wisconsin School of Medicine and Public Health
  • University of Wisconsin – Madison
  • Institute for Systems and Computer Engineering, Technology and Science
  • Institute of Zoology Chinese Academy of Sciences
  • University of California Santa Barbara

Research output: Contribution to journalArticlepeer-review

298 Scopus citations

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 holdout 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.
Original languageEnglish
Pages (from-to)12516-12521
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume112
Issue number40
DOIs
StatePublished - Oct 6 2015

Keywords

  • Differentiation
  • Machine learning
  • Organoid
  • Tissue engineering
  • Toxicology

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