Molecular Profiling of Schizophrenia

项目摘要 复杂的疾病，如精神分裂症（SCZ），是由多因素遗传和环境 引起分子网络多效性变化的扰动，导致疾病。我们的目标 一个项目是将我们的CommonMind Consortium（CMC）数据生成提升到一个新的水平，专注于细胞类型 特定数据生成，包括转录组学、表观基因组学、蛋白质组学和综合分析， SCZ中涉及的关键区域，以提高我们识别和细化SCZ遗传风险因素的能力。我们 将在我们的死后CMC SCZ中表征细胞类型特异性转录组和表观基因组组分 和对照队列以及新对照尸检标本中。在来自CMC的50例SCZ病例和50例对照中，我们 将1.）进行RNA，测序，2.）ATACseq和3.）从谷氨酸能池中分离的细胞核中的Hi-C 神经元、GABA能神经元和分离自前额皮质的少突胶质细胞。相同样品中 我们通过液相色谱（LC）选择性反应表征突触后密度蛋白的蛋白质 监测（SRM）-质谱仪（MS）定量蛋白质组学分析。为了揭露有多少 不同类型的细胞存在于对照个体的不同大脑区域中，我们将使用纳米流体技术， 来自新鲜尸检、从未冷冻或固定的对照标本的条形码（Drop-seq）。综合分析将 将结合联合收割机遗传学、基因表达、表观基因组学和蛋白质组学数据来鉴定新的SCZ 基因.最后，我们将继续维护和升级我们的社区工作空间， 传播和公开评估来自CMC的数据、分析和结果。我们将继续 通过Sage Bionetworks Synapse平台向研究社区提供所有数据。有一个 人类对转录和表观遗传景观的更多细胞类型特异性信息的深刻需求 脑，特别是神经元和神经胶质细胞，并将此信息与人类SCZ遗传学整合。我们有 汇集了关键的关键人员，样本资源，技术知识和分析策略， 能够为该领域提供有用的地图，以及开始解开SCZ生物学。

Project Summary Complex diseases such as schizophrenia (SCZ) result from multifactorial genetic and environmental perturbations that cause pleiotropic changes in molecular networks, resulting in disease. The goal of our project is to move our CommonMind Consortium (CMC) data generation to the next level, focusing on cell-type specific data generation including transcriptomics, epigenomics, proteomics, and integrated analyses, from critical regions implicated in SCZ to improve our ability to identify and refine genetic risk factors for SCZ. We will characterize cell-type specific transcriptome and epigenome components in our post-mortem CMC SCZ and control cohort and in new control autopsy specimens. In 50 SCZ cases and 50 controls from the CMC, we will 1.) perform RNA, sequencing, 2.) ATACseq, and 3.) Hi-C in nuclei isolated from pools of glutamatergic neurons, GABAergic neurons, and oligodendrocytes isolated from the prefrontal cortex. In the same samples we characterize proteins of post-synaptic density proteins by liquid chromatography (LC)-selective reaction monitoring (SRM)-mass spectrometer (MS) quantitative proteomic analyses. In order to uncover how many distinct types of cells are present in various brain regions of control individuals we will use nanofluidics and bar-coding (Drop-seq) from freshly autopsied, never frozen or fixed control specimens. Integrative analyses will be pursued that will combine genetic, gene expression, epigenomic and proteomic data to identify novel SCZ genes. Finally, we will continue to maintain and upgrade our community workspace that provides for the rapid dissemination and open evaluation of data, analyses, and outcomes derived from the CMC. We will continue to make all data available to the research community through the Sage Bionetworks Synapse Platform. There is a deep need to more cell-type specific information on the transcriptional and epigenetic landscape in the human brain, and in particular in neurons and glia and to integrate this information with human SCZ genetics. We have assembled the critical key personnel, sample resources, technological know-how, and analytic strategies to be able to provide both useful maps for the field, as well as begin to unravel SCZ biology.