Neuron and Glial Cellular Signatures from Normal and Diseased iPS Cells

描述（由申请人提供）：目前迫切需要确定人类脑细胞在健康和疾病中的状态和预测行为。中枢神经系统中不同细胞类型的数量仍然不明确，尽管在人口统计学上出现了神经退行性疾病的浪潮，但没有一种疾病改善疗法存在。我们对中枢神经系统的了解，以及合理干预疾病的基础，将通过产生定量分子表型，本质上是人类神经元、星形胶质细胞和少突胶质细胞的细胞特征，这些细胞特征来自健康人，以及患有运动神经元疾病、亨廷顿氏病和帕金森病的患者。中枢神经系统是如此独特，以至于研究非神经元细胞并不能提供太多帮助。尽管有这种迫切的需求，但人类脑细胞的不可获取性意味着研究它们是不可能的，直到最近发现细胞重编程和诱导多能干细胞技术。在此，我们建议成立NeuroLINCS联盟来实现这些目标。我们精心挑选了这个团队，引进了iPSC技术、疾病建模、转录组学、表观基因组学、代谢组学、全基因组测序、蛋白质组学、高含量、高通量纵向单细胞分析、其他基于细胞的分析、生物信息学、统计学和计算生物学等方面的关键专业知识。此外，我们正在与谷歌合作，引入机器学习方面的专业知识，并将跨平台的签名集成到对扰动响应的高度预测模型中。总之，我们期望在不同条件下开发一系列人类脑细胞类型的细胞特征，这些细胞特征应该广泛适用于LINCs社区。我们还期望产生创新的软件工具和方法，使签名生成过程更便宜、更快、更可靠。除了在NeuroLINCS中代表的独特的专业知识组合之外，另一个显着的特征是其成员之间相互合作的长期记录。这种合作精神将在NeuroLINCS中通过其重要的、多方面的社区外展项目得到体现。这将包括具体和详细的计划，使NeuroLINCS产生的数据和工具可供社区使用，与其他LINCS站点进行交互，并为DCIC和大规模传播知识和资源的前景做好准备。

DESCRIPTION (provided by applicant): There is a critical need to define the state and predict the behavior of human brain cells in health and disease. The number of different cell types in the CNS remains undefined, and despite a demographically ordained wave of neurodegenerative diseases, not a single disease-modifying therapy exists. Our knowledge of the CNS and the foundation for intervening rationally in disease would be dramatically advanced by generating quantitative molecular phenotypes essentially cell signatures of human neurons, astrocytes and oligodendrocytes from healthy people and from patients with motor neuron disease, Huntington's disease, and Parkinson's disease. The CNS is so unique that studying non-neuronal cells does not provide much assistance. Despite this desperate need, the inaccessibility of human brain cells meant studying them would have been impossible until the recent discovery of cellular reprogramming and induced pluripotent stem cell technology. Here we propose to form the NeuroLINCS consortium to accomplish these goals. We have handpicked the team to bring in critical expertise in iPSC technology, disease modeling, transcriptomics, epigenomics, metabolomics, whole genome sequencing, proteomics, high content, high throughput longitudinal single cell analysis, other cell-based assays, bioinformatics, statistics and computational biology. In addition, we are collaborating with Google to bring in special expertise in machine learning and the integration of signatures across platforms into highly predictive models of responses to perturbagens. Together, we expect to develop cell signatures of an array of human brain cell types under different conditions that should be broadly applicable to the LINCs community. We also anticipate generating innovative software tools and approaches that will make the signature generating process cheaper, faster, and more reliable. Besides the unique combination of expertise represented within NeuroLINCS, another distinguishing feature is the long track record that its members have of collaborating with each other. That collaborative spirit will be expressed in NeuroLINCS through its significant and multifaceted community outreach programs. These will involve specific and detailed plans to make the data and tools that NeuroLINCS generates available to the community, to interact with other LINCS sites, and to prepare for DCIC and the prospect of disseminating knowledge and resources at scale.