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

• 批准号: 8516134
• 负责人: James Alexander Thomson
• 金额: $ 109.43万
• 依托单位: MORGRIDGE INSTITUTE FOR RESEARCH, INC.
• 依托单位国家: 美国
• 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/8516134
• 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

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）方面的领先专家，以开发改进的人类细胞模型来预测发育性神经毒性。从人类多能干细胞（ES和iPS细胞）中衍生出大脑的许多基本细胞成分已经取得了巨大的进展，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）、卵黄囊髓系祖细胞（小胶质细胞）、迁移的中胚层成血管细胞（内皮细胞）和神经嵴（血管平滑肌和周细胞）。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。这意味着在相同的条件下同时推导出它们目前是不可能的。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、胶质和血管成分的早期前体，冷冻保存这些前体，然后将它们结合在3D水凝胶中，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前体嵌入到水凝胶中，显示促进毛细血管网络形成的肽基序组合。然后，我们将在这个间充质层上覆盖神经和胶质前体，以模拟头侧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。在药物暴露后，我们将使用高度多路复用的深度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，使用来自美国国立卫生研究院临床收集的安全药物和已知的神经/发育毒素，华盛顿大学致畸物信息系统数据库和EPA毒性参考数据库作为训练集，我们将开发机器学习算法来预测在后期动物试验或人体临床试验中失败的盲法药物的神经毒性。这种预测的、发育的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，并将很容易适应DARPA补充努力开发的平台。人类多能干细胞的发育潜力、可调水凝胶的模块化特性以及机器学习工具的鉴别能力，也使得提出的一般方法很容易适用于预测全身其他组织类型的毒性模型。