title: Human pluripotent stem cell-derived neural constructs for predicting neural toxicity.
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.
authors.affiliation: Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53706;
authors.forename: Michael P
authors.initials: MP
authors.lastname: Schwartz
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
authors.forename: Zhonggang
authors.initials: Z
authors.lastname: Hou
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
authors.forename: Nicholas E
authors.initials: NE
authors.lastname: Propson
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
authors.forename: Jue
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53792; Department of Computer Sciences, University of Wisconsin, Madison, WI 53706;
authors.forename: Collin J
authors.initials: CJ
authors.lastname: Engstrom
authors.affiliation: 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;
authors.forename: Vitor
authors.initials: V
authors.lastname: Santos Costa
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
authors.forename: Peng
authors.initials: P
authors.lastname: Jiang
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
authors.forename: Bao Kim
authors.initials: BK
authors.lastname: Nguyen
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Bolin
authors.affiliation: Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53706;
authors.forename: William
authors.initials: W
authors.lastname: Daly
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
authors.forename: Yu
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI 53715;
authors.forename: Ron
authors.initials: R
authors.lastname: Stewart
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53792; Department of Computer Sciences, University of Wisconsin, Madison, WI 53706;
authors.forename: C David
authors.initials: CD
authors.lastname: Page
authors.affiliation: Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53706; Department of Orthopedics and Rehabilitation, University of Wisconsin, Madison, WI 53705;
authors.forename: William L
authors.initials: WL
authors.lastname: Murphy
authors.affiliation: 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.
authors.forename: James A
authors.initials: JA
authors.lastname: Thomson
mesh_terms: D001921:Brain / Q000166:cytology / Q000254:growth & development / Q000378:metabolism; D002450:Cell Communication / Q000187:drug effects / Q000235:genetics; D002454:Cell Differentiation; D002478:Cells, Cultured; D016895:Culture Media, Serum-Free / Q000494:pharmacology; D053595:Embryonic Stem Cells / Q000166:cytology* / Q000187:drug effects / Q000378:metabolism; D042783:Endothelial Cells / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D018507:Gene Expression Regulation, Developmental; D063990:Gene Ontology; D006801:Humans; D020100:Hydrogels / Q000494:pharmacology; D008264:Macrophages / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D059630:Mesenchymal Stem Cells / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D017628:Microglia / Q000166:cytology / Q000187:drug effects / Q000378:metabolism; D008954:Models, Biological; D058953:Neural Stem Cells / Q000166:cytology* / Q000187:drug effects / Q000378:metabolism; D055495:Neurogenesis / Q000187:drug effects / Q000235:genetics; D039904:Pluripotent Stem Cells / Q000166:cytology* / Q000187:drug effects / Q000378:metabolism; D011092:Polyethylene Glycols / Q000494:pharmacology; D060388:Support Vector Machine; D023822:Tissue Engineering / Q000379:methods; D015262:Xenobiotics / Q000145:classification / Q000494:pharmacology
chemical_list: D016895:Culture Media, Serum-Free; D020100:Hydrogels; D015262:Xenobiotics; D011092:Polyethylene Glycols
keywords: differentiation; machine learning; organoid; tissue engineering; toxicology