Decoding the molecular basis of cellular identity in the human brain

解码人脑细胞身份的分子基础

• 批准号: 10306356
• 负责人: Michael Clark Oldham
• 金额: $ 40.11万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-12-05 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10306356
• 关键词: Address; Adult; Animal Model; Astrocytes; BRAIN initiative; Brain; Brain Diseases; Brain region; Cell Extracts; Cell Nucleus; Cell physiology; Cells; Cellular biology; Complement; Complementary DNA; Consensus; Data; Data Set; Disease; Disease model; Dissociation; Endothelial Cells; Ependymal Cell; Gene Expression; Gene Expression Profile; Genes; Genetic; Genetic Transcription; Goals; Heterogeneity; Homologous Gene; Human; In Vitro; Individual; Lead; Linear Regressions; Mathematics; Methods; Microglia; Modeling; Molecular; Mus; Neuroglia; Neurons; Occupations; Oligodendroglia; Outcome; Pathologic; Pathology; Pattern; Population Heterogeneity; Process; Property; Reagent; Recurrence; Reproducibility; Research; Resistance; Resources; Sampling; Sampling Biases; Source; Taxonomy; Testing; Tissue Sample; Tissues; Transcript; Transcription Process; Variant; base; biological systems; cell type; design; differential expression; experience; genome-wide; in silico; innovation; insight; mathematical model; novel; novel marker; novel therapeutics; predictive modeling; programs; regional difference; spatiotemporal; tool; transcriptome; transcriptome sequencing

Project Summary Understanding the molecular basis of cellular identity in the human brain is a major goal of the BRAIN Initiative and essential for clarifying the cellular origins of diverse brain disorders. This project will use novel, ‘top-down’ analytical strategies that complement single-cell and single-nucleus methods to define the core transcriptional identities of major cell types in the adult human brain while obviating the need to purify or isolate cells. Our central hypothesis is that covariation between the abundance of cell types and transcripts can be estimated through integrative gene coexpression analysis of intact tissue samples, and this information can be used to construct quantitative cell type definitions, perform mathematical modeling of gene expression, and identify cell type-specific transcriptional differences between biological systems. In Aim 1, we will integrate cell type- specific gene coexpression modules from >60 datasets and >7000 neurotypical adult human brain samples to determine consensus transcriptional profiles of major cell types. These profiles will suggest which major cell types primarily express genes that have been implicated in human brain disorders, identify novel biomarkers, and help to establish the 'ground truth' for assessing the validity of human cell types derived in vitro for disease modeling. We will also leverage highly recurrent gene coexpression relationships to estimate cellular abun- dance and develop predictive models of gene expression in adult human brain samples. These models will im- prove reproducibility through concrete predictions that can be tested in new human brain transcriptomes (in- cluding those from pathological samples) as they become available; they will also lead to new analytical strate- gies that go beyond differential expression analysis to reveal subtle transcriptional perturbations associated with pathology. In Aim 2, we will replicate the goals of Aim 1 in mice and implement a comprehensive effort to identify binary (on/off) expression differences in major cell types between human and mouse brains. In Aim 3, we will assess the extent of regional variation in the transcriptional identities of major CNS cell types in the adult human brain. Expected outcomes include consensus transcriptional profiles of astrocytes, oligodendro- cytes, microglia, neurons, ependymal cells, and endothelial cells; rigorous mathematical models that can accu- rately predict expression levels for thousands of genes in de novo human brain transcriptomes; and new tools and reagents for studying CNS cell types and subtypes. This project is innovative because it challenges the status quo that cells must be physically isolated to study their molecular properties; it also introduces a novel concept and metric called gene expression fidelity for defining cellular identity. Our studies will have a positive impact by providing an unprecedented resource for identifying transcriptional processes that distinguish cell types among human brain regions, species, and disease states, and will contribute directly to our long-term goal of constructing a comprehensive cellular taxonomy of the human CNS from molecular data.

项目摘要 了解人脑中细胞身份的分子基础是大脑倡议的主要目标 对于阐明潜水员脑疾病的细胞起源至关重要。该项目将使用小说“自上而下” 与单细胞和单核方法相互补充的分析策略来定义核心转录 成人人脑中主要细胞类型的身份，同时消除了纯化或分离细胞的需求。我们的 中心假设是可以估计细胞类型的抽象和转录本之间的协方差 通过完整的组织样品的综合基因共表达分析，该信息可用于 构建定量细胞类型的定义，对基因表达进行数学建模并识别细胞 生物系统之间的特定类型转录差异。在AIM 1中，我们将整合细胞类型 - 来自> 60个数据集和> 7000个神经型成人人脑样本的特定基因共表达模块 确定主要细胞类型的共识转录曲线。这些轮廓将暗示哪个主要单元格 类型在人脑疾病中实施的主要表达基因，识别新型生物标志物， 并有助于建立“地面真相”，以评估体外疾病中人类细胞类型的有效性 造型。我们还将利用高度复发的基因共表达关系来估计细胞abun- 在成人人类脑样本中跳舞和开发基因表达的预测模型。这些模型将不 通过可以在新的人脑转录组中测试的具体预测来证明可重复性 从病理性样本中持有它们，因为它们可用；它们还将导致新的分析策略 - 超出差分表达分析以揭示与微妙的转录扰动相关的gies 有病理学。在AIM 2中，我们将复制小鼠目标1的目标，并实施全面的努力 确定人和小鼠大脑之间主要细胞类型的二进制（开/关）表达差异。在AIM 3中， 我们将评估主要中枢神经系统细胞类型的转录身份的区域变化程度 成人人脑。预期的结果包括星形胶质细胞的共识转录曲线 细胞，小胶质细胞，神经元，膜状细胞和内皮细胞；严格的数学模型可以促进 在人类脑转录组中的数千个基因的表达水平已预测；和新工具 以及用于研究CNS细胞类型和亚型的试剂。该项目具有创新性，因为它挑战了 必须将细胞物理隔离以研究其分子特性的状态；它也介绍了一本小说 概念和公制称为基因表达保真度以定义细胞身份。我们的研究将有积极的 通过提供前所未有的资源来识别区分细胞的转录过程来产生影响 人脑，物种和疾病状态之间的类型，将直接贡献我们的长期 从分子数据中构建人类中枢神经系统的全面细胞分类法的目标。

项目成果

Generation of functional human oligodendrocytes from dermal fibroblasts by direct lineage conversion.

• DOI: 10.1242/dev.199723
• 发表时间: 2022-10-15
• 期刊: Development (Cambridge, England)

Bulk and mosaic deletions of Egfr reveal regionally defined gliogenesis in the developing mouse forebrain.

• DOI: 10.1016/j.isci.2023.106242
• 发表时间: 2023-03-17
• 期刊: ISCIENCE
• 影响因子: 5.8
• 作者: Zhang, Xuying;Xiao, Guanxi;Johnson, Caroline;Cai, Yuheng;Horowitz, Zachary K.;Mennicke, Christine;Coffey, Robert;Haider, Mansoor;Threadgill, David;Eliscu, Rebecca;Oldham, Michael C.;Greenbaum, Alon;Ghashghaei, H. Troy
• 通讯作者: Ghashghaei, H. Troy

Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes.

• DOI: 10.1038/s41593-018-0216-z
• 发表时间: 2018-09
• 期刊: Nature neuroscience
• 影响因子: 25
• 作者: Kelley KW;Nakao-Inoue H;Molofsky AV;Oldham MC
• 通讯作者: Oldham MC