Human pluripotent stem cell-derived neural constructs for predicting neural toxicity
Author(s) -
Michael P. Schwartz,
Zhonggang Hou,
Nicholas E. Propson,
Jue Zhang,
Collin J. Engstrom,
Vı́tor Santos Costa,
Peng Jiang,
Bao Kim Nguyen,
Jennifer M. Bolin,
William T. Daly,
Yu Wang,
Ron Stewart,
C. David Page,
William L. Murphy,
James A. Thomson
Publication year - 2015
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.1516645112
Subject(s) - induced pluripotent stem cell , neurogenesis , neural stem cell , microglia , embryonic stem cell , self healing hydrogels , computational biology , mesenchymal stem cell , biology , progenitor cell , neural cell , stem cell , cell , microbiology and biotechnology , bioinformatics , chemistry , immunology , biochemistry , gene , inflammation , organic chemistry
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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom