This proposal brings together leading experts in human pluripotent stem cell biology (Thomson), tissue engineering (Murphy), and machine learning (Page) to develop improved human cellular models for predicting developmental neural toxicity. Dramatic progress has been made in the derivation of many of the basic cellular components of the brain from human pluripotent stem cells (ES and iPS cells), but these advances have yet to be applied to predictive toxicology. The major components of the brain are derived from diverse embryological origins, including the neural plate (neurons, oligodendrocytes, and astrocytes), yolk sac myeloid progenitors (microglia), migratory mesodermal angioblasts (endothelial cells), and neural crest (vascular smooth muscle and pericytes). Because of their diverse origins, these components have very different inductive signaling histories. This means that deriving them all at once under the same conditions is not currently possible. For this reason, we will differentiate human pluripotent stem cells to early precursors of the major neural, glial, and vascular components of the cerebral cortex separately, cryopreserve the precursors, and subsequently combine them in 3D hydrogel assemblies to allow increased physiological interactions and maturation. Specifically, we will embed committed precursors for endothelial cells, pericytes, and microglia into hydrogels displaying combinations of peptide motifs that promote capillary network formation. We will then overlay this mesenchymal layer with neural and glial precursors to mimic the normal interactions between the cephalic mesenchyme and the neural epithelium, and promote the formation of the polarized layers of the cerebral cortex. After drug exposure, we will assess temporal changes in gene expression by these cerebral neural- vascular assemblies using highly multiplexed, deep RNA sequencing. Then, using safe drugs and known neural/developmental toxins from the NIH Clinical Collection, the University of Washington Teratogen Information System Database, and the EPA's Toxicity Reference Database as training sets, we will develop machine learning algorithms to predict neural toxicity of blinded drugs known to have failed in late stage animal testing or human clinical trials. This predictive, developmental neural toxicity model will be implemented on liquid handling robots and sequencers in widespread use, and will be readily adaptable to platforms being developed in complementary efforts by DARPA. The developmental potential of human pluripotent stem cells, the modular nature of the tunable hydrogels, and the discriminatory power of machine learning tools also makes the general approaches proposed readily applicable to predictive toxicity models for other tissue types throughout the body.
This project will develop three-dimensional constructs of human neural tissue to better predict the neural toxicity of drugs prior to clinical trials. To accomplish this, experts in human pluripotent stem cell biology will grow the required neural components in the laboratory, experts in tissue engineers will assemble those cells into multicellular constructs, and experts in machine learning will use changes in gene expression after drug exposure to predict whether a test compound is toxic.
|Adamski, Michal; Fontana, Gianluca; Gershlak, Joshua R et al. (2018) Two Methods for Decellularization of Plant Tissues for Tissue Engineering Applications. J Vis Exp :|
|Bacher, Rhonda; Leng, Ning; Chu, Li-Fang et al. (2018) Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC Bioinformatics 19:380|
|Jiang, Peng; Hou, Zhonggang; Bolin, Jennifer M et al. (2017) RNA-Seq of Human Neural Progenitor Cells Exposed to Lead (Pb) Reveals Transcriptome Dynamics, Splicing Alterations and Disease Risk Associations. Toxicol Sci 159:251-265|
|Zhang, Jue; Schwartz, Michael P; Hou, Zhonggang et al. (2017) A Genome-wide Analysis of Human Pluripotent Stem Cell-Derived Endothelial Cells in 2D or 3D Culture. Stem Cell Reports 8:907-918|
|Zhang, Jue; Chu, Li-Fang; Hou, Zhonggang et al. (2017) Functional characterization of human pluripotent stem cell-derived arterial endothelial cells. Proc Natl Acad Sci U S A 114:E6072-E6078|
|Barry, Christopher; Schmitz, Matthew T; Propson, Nicholas E et al. (2017) Uniform neural tissue models produced on synthetic hydrogels using standard culture techniques. Exp Biol Med (Maywood) 242:1679-1689|
|Fontana, Gianluca; Gershlak, Joshua; Adamski, Michal et al. (2017) Biofunctionalized Plants as Diverse Biomaterials for Human Cell Culture. Adv Healthc Mater 6:|
|Korthauer, Keegan D; Chu, Li-Fang; Newton, Michael A et al. (2016) A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol 17:222|
|Leng, Ning; Choi, Jeea; Chu, Li-Fang et al. (2016) OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data. Bioinformatics 32:1408-10|
|Chu, Li-Fang; Leng, Ning; Zhang, Jue et al. (2016) Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol 17:173|
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