Human iPS/ES Cell-Based Models for Predictive Neural Toxicity and Teratogenicity

基于人类 iPS/ES 细胞的预测神经毒性和致畸性模型

基本信息

批准号：
8668606
负责人：
James Alexander Thomson
金额：
$ 13.95万
依托单位：
MORGRIDGE INSTITUTE FOR RESEARCH, INC.
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-24 至 2014-09-15
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8668606
关键词：
Adult Algorithms Angioblast Animal Testing Astrocytes Behavioral Blinded Blood Vessels Blood capillaries Brain Cell model Cells Cephalic Cerebral cortex Cerebrum Clinical Clinical Trials Collection Commit Databases Derivation procedure Development Drug Exposure Drug toxicity Endothelial Cells Epithelium Erinaceidae Exposure to Fetus Gene Expression Histocompatibility Testing Human Human body Hydrogels Information Systems Laboratories Liquid substance Machine Learning Mediating Mesenchymal Mesenchyme Microglia Modeling Molecular Profiling Myelogenous Nature Neural Crest Neurons Oligodendroglia Organ Pathway interactions Peptides Pericytes Pharmaceutical Preparations Physiological Pluripotent Stem Cells Population RNA Sequences Receptor Protein-Tyrosine Kinases Recording of previous events Robot Rodent Model Signal Pathway Signal Transduction Staging Teratogens Testing Tissue Engineering Tissues Toxic effect Toxicology Toxin Training United States National Institutes of Health Universities Vascular Smooth Muscle Washington Yolk Sac base capillary cognitive change cost effective drug development embryonic stem cell experience improved in vitro testing induced pluripotent stem cell neural plate neurodevelopment notch protein precursor cell predictive modeling prenatal exposure progenitor relating to nervous system stem cell biology tool toxicant transcriptome sequencing

项目摘要

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：该提案汇集了人类多能干细胞生物学（Thomson）、组织工程（Murphy）和机器学习（Page）领域的领先专家，以开发改进的人类细胞模型来预测发育神经毒性。从人类多能干细胞（ES 和 iPS 细胞）衍生大脑的许多基本细胞成分方面已经取得了巨大进展，但这些进展尚未应用于预测毒理学。大脑的主要组成部分来自不同的胚胎起源，包括神经板（神经元、少突胶质细胞和星形胶质细胞）、卵黄囊髓样祖细胞（小胶质细胞）、迁移性中胚层成血管细胞（内皮细胞）和神经嵴（血管平滑肌和神经嵴）。周细胞）。由于它们的起源不同，这些成分具有非常不同的感应信号历史。这意味着目前不可能在相同条件下一次性导出它们。因此，我们将分别将人类多能干细胞分化为大脑皮层主要神经、神经胶质和血管成分的早期前体细胞，冷冻保存前体细胞，然后将它们组合在 3D 水凝胶组件中，以增加生理相互作用和成熟。具体来说，我们将把内皮细胞、周细胞和小胶质细胞的定向前体嵌入水凝胶中，该水凝胶显示促进毛细血管网络形成的肽基序组合。然后，我们将用神经和神经胶质前体覆盖该间充质层，以模拟头部间充质和神经上皮之间的正常相互作用，并促进大脑皮层极化层的形成。药物暴露后，我们将使用高度多重、深度 RNA 测序来评估这些脑神经血管组件基因表达的时间变化。然后，使用来自 NIH 临床收藏、华盛顿大学致畸信息系统数据库和 EPA 毒性参考数据库的安全药物和已知神经/发育毒素作为训练集，我们将开发机器学习算法来预测已知盲药的神经毒性在后期动物试验或人体临床试验中失败。这种预测性发育神经毒性模型将在广泛使用的液体处理机器人和测序仪上实施，并且很容易适应 DARPA 正在开发的平台。人类多能干细胞的发育潜力、可调谐水凝胶的模块化性质以及机器学习工具的区分能力也使得所提出的通用方法很容易适用于全身其他组织类型的预测毒性模型。