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

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Michael P Schwartz (Author) Other; Zhonggang G Hou (Author) Other; Nicholas E Propson (Author) Other; Jue Zhang (Author) Other; Collin J Engstrom (Author) Other; Vitor Santos Costa (Author) FCUP; Peng Jiang (Author) Other; Bao Kim Nguyen (Author) Other; Jennifer M Bolin (Author) Other; William Daly (Author) Other; Yu Wang (Author) Other; Ron Stewart (Author) Other; David D Page (Author) Other; William L Murphy (Author) Other; James A Thomson (Author) Other

Journal

Title: Proceedings of the National Academy of Sciences of the United States of America

Vol. 112

Pages: 12516-12521

ISSN: 0027-8424

Publisher: National Academy of Sciences

Indexing

ISI Web of Knowledge - 250 Citations

Scopus - 283 Citations

Other information

Authenticus ID: P-00G-Q4T

DOI: 10.1073/pnas.1516645112

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6