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：
8414419
负责人：
James Alexander Thomson
金额：
$ 112.5万
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 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8414419
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 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

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. PUBLIC HEALTH RELEVANCE: 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.

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）的领先专家，以开发改进的人类细胞模型，以预测发育神经毒性。从人多能干细胞（ES和IPS细胞）推导了许多脑的许多基本细胞成分（ES和IPS细胞）中取得了巨大进展，但是这些进步尚未应用于预测毒理学。大脑的主要组成部分来自多种胚胎学起源，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），迁移性中胚层血管生成细胞（内皮细胞）和新的creste crest（crest）（血管性的creste crest）（血管和骨质平滑（血管和毛皮）。由于它们的起源各不相同，这些组件具有非常不同的感应信号传导历史。这意味着目前不可能在相同条件下立即派生它们。因此，我们将分别将人类多能干细胞分别为大脑皮层的主要神经，神经胶质和血管成分的早期前体，分别将前体的冷冻剂，然后在3D水凝胶组件中将它们组合在一起以允许增加的物理相互作用和成熟。具体而言，我们将内皮细胞，周细胞和小胶质细胞的固定前体嵌入到显示肽基序的组合的水凝胶中，以促进毛细血管网络形成。然后，我们将用神经和神经胶质前体叠加这个间充质层，以模仿头骨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的，深的RNA测序来评估这些脑神经血管组件的基因表达时间变化。然后，使用NIH临床收集的安全药物和已知的神经/发育性毒素，华盛顿大学Teratogen Teratogen大学信息系统数据库以及EPA的毒性参考数据库作为培训集，我们将开发机器学习算法，以预测已知在后期动物测试或人类临床临床临床临床试验中已知的盲人药物的神经毒性。这种预测性的发育神经毒性模型将在液体处理机器人和测序仪广泛使用中实施，并且很容易适应DARPA的互补工作中开发的平台。人多能干细胞的发育潜力，可调节水凝胶的模块化性质以及机器学习工具的歧视能力也使一般方法易于适用于整个身体其他组织类型的预测毒性模型。公共卫生相关性：该项目将开发人类神经组织的三维结构，以更好地预测临床试验前药物的神经毒性。为此，人类多能干细胞生物学的专家将增长实验室中所需的神经成分，组织工程师的专家将将这些细胞组装成多细胞构建体，机器学习方面的专家将在药物暴露后使用基因表达的变化来预测测试化合物是否有毒。