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

TitleHuman pluripotent stem cell-derived neural constructs for predicting neural toxicity.
Publication TypeJournal Article
Year of Publication2015
AuthorsSchwartz MP, Hou Z, Propson NE, Zhang J, Engstrom CJ, Costa VSantos, Jiang P, Nguyen BKim, Bolin JM, Daly W, Wang Y, Stewart R, C Page D, Murphy WL, Thomson JA
JournalProc Natl Acad Sci U S A
Volume112
Issue40
Pagination12516-21
Date Published2015 Oct 6
ISSN1091-6490
KeywordsBrain, Cell Communication, Cell Differentiation, Cells, Cultured, Culture Media, Serum-Free, Embryonic Stem Cells, Endothelial Cells, Gene Expression Regulation, Developmental, Gene Ontology, Humans, Hydrogels, Macrophages, Mesenchymal Stromal Cells, Microglia, Models, Biological, Neural Stem Cells, Neurogenesis, Pluripotent Stem Cells, Polyethylene Glycols, Support Vector Machine, Tissue Engineering, Xenobiotics
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.

DOI10.1073/pnas.1516645112
Alternate JournalProc. Natl. Acad. Sci. U.S.A.
PubMed ID26392547
PubMed Central IDPMC4603492
Grant List1UH2TR000506-01 / TR / NCATS NIH HHS / United States
3UH2TR000506-02S1 / TR / NCATS NIH HHS / United States
4UH3TR000506-03 / TR / NCATS NIH HHS / United States
R21EB016381-01 / EB / NIBIB NIH HHS / United States
UH3 TR000506 / TR / NCATS NIH HHS / United States