Resolving single-cell brain regulatory elements with bulk data supervised models

用批量数据监督模型解决单细胞大脑调节元件

• 批准号: 10362579
• 负责人: KATHERINE S. POLLARD
• 金额: $ 60.66万
• 依托单位: J. DAVID GLADSTONE INSTITUTES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10362579
• 关键词: 3-Dimensional; Address; Adult; Algorithms; Anatomy; Atlases; Autopsy; Base Pairing; Binding Sites; Biological Assay; Biological Process; Brain; Cell Differentiation process; Cell physiology; Cells; Cellular Assay; Censuses; Chickens; Chromatin; Code; Collection; Complex; DNA Binding; DNA Sequence Alteration; Data; Diffusion; Disease; Drops; Elements; Enhancers; Epigenetic Process; Evaluation; Functional disorder; Gene Expression; Gene Expression Regulation; Genes; Genetic Risk; Genome; Genomics; Genotype-Tissue Expression Project; Goals; Graph; Hi-C; Human; Individual; Light; Link; Machine Learning; Maps; Measurement; Mental disorders; Methods; Modeling; Molecular; Mus; Mutation; Neuraxis; Nucleotides; Outcome; Pathway interactions; Pattern; Performance; Population; Pregnancy; Proteins; Regulator Genes; Regulatory Element; Reporter; Resolution; Risk; Sampling; Signal Transduction; Source; Source Code; Specificity; Structure; Supervision; Technology; Testing; Theoretical model; Time; Tissue Sample; Tissues; Transgenic Mice; Untranslated RNA; Validation; Variant; Weight; XCL1 gene; autism spectrum disorder; base; brain cell; brain health; brain tissue; cell type; cohort; differential expression; disorder risk; egg; epigenomics; equilibration disorder; experimental study; flexibility; functional genomics; gene function; genetic variant; genome sequencing; genome-wide; genomic data; human data; improved; in vitro Assay; in vivo; insight; machine learning framework; multiple data types; network models; open source; prediction algorithm; promoter; psychiatric genomics; public repository; single cell analysis; single-cell RNA sequencing; supervised learning; tool; transfer learning; web server; whole genome

Gene regulation is an important determinant of the complex specialization of cells in the human brain, and nucleotide changes within regulatory elements contribute to risk for psychiatric disorders. We therefore hypothesize that these debilitating diseases are driven in part by genetic variants that alter gene expression and disturb the balance and function of cell types in brain tissue. Single-cell open chromatin assays are a promising approach to testing this hypothesis by mapping variants to regulatory elements specific to and shared across cell populations. There are two major barriers to this strategy, for which our project proposes modeling solutions. First, despite being the best assay currently, single-cell ATAC-sequencing (scATAC-seq) suffers from low resolution, meaning that an open chromatin region may be supported by zero or few reads in a given cell. This makes it hard to identify coherent cell populations. We propose a network model for semi-supervised clustering of cells in scATAC-seq that leverages information from higher-coverage bulk tissue experiments and single-cell RNA-sequencing (scRNA-seq), if available. The expected outcomes from applying this model to compendia of brain data from public repositories and our collaborators are (i) identification of open chromatin regions that differentiate cell types and states, and (ii) discovery of resolved cell populations whose open chromatin is enriched for psychiatric disorder associated genetic variants. These results alone may not be enough to develop a mechanistic understanding of how variants impact brain function. To address this second challenge, we will implement a computationally efficient, machine-learning framework for predicting the specific regulatory functions of single-cell open chromatin regions from our network model and other approaches. Gene regulatory enhancers are particularly amenable to this approach, because high-throughput mouse transgenics and massively parallel reporter assays have generated enough validated enhancers for supervised learning. Our framework will be easy to apply to other regulatory functions, such as insulating boundaries in chromatin capture data. By developing a compressed, yet flexible, featurization of massive bulk and single-cell data compendia, we will enable rapid iteration with computationally intensive prediction algorithms to be applied to single-cell open chromatin regions. Our approach will also incorporate transfer learning from data-rich (e.g., postmortem or mouse brains) to data-poor settings (e.g., human late-gestation brains). We expect predicted regulatory elements to be more enriched for psychiatric disorder genetic risk, to provide mechanistic insight regarding how variants cause disease, and to be useful molecular tools. Together our two proposed computational approaches will leverage the complementary strengths of bulk and single-cell data to resolve regulatory elements that drive the exquisite diversity of cells in developing and adult brains towards mapping the non-coding contribution of psychiatric disease.

基因调控是人脑细胞复杂特化的重要决定因素， 调节元件内的核苷酸变化有助于精神疾病的风险。因此我们 假设这些使人衰弱的疾病部分是由改变基因表达的遗传变异驱动的， 扰乱脑组织中细胞类型的平衡和功能。单细胞开放染色质分析是一种很有前途的 一种通过将变异映射到特定于和共享的调控元件来检验这一假设的方法 细胞群这个策略有两个主要的障碍，我们的项目提出了建模解决方案。 首先，尽管是目前最好的测定，但单细胞ATAC测序（scATAC-seq）具有低的测序效率。 这意味着开放的染色质区域可以由给定细胞中的零个或几个读段支持。这 使得很难识别连贯的细胞群。我们提出了一个半监督聚类的网络模型 scATAC-seq中的细胞利用了来自更高覆盖率的大量组织实验和单细胞 RNA测序（scRNA-seq），如果可用。将这一模式应用于 来自公共知识库和我们的合作者的大脑数据是（i）识别开放的染色质区域， 区分细胞类型和状态，以及（ii）发现其开放染色质是 富含与精神疾病相关的遗传变异仅仅这些结果可能不足以发展 对变异如何影响大脑功能的机械理解。为了应对第二个挑战，我们将 实现一个计算效率高的机器学习框架，用于预测特定的监管 从我们的网络模型和其他方法的单细胞开放染色质区域的功能。基因调控 增强子特别适合这种方法，因为高通量小鼠转基因和 大规模平行报道分析已经产生了足够的用于监督学习的有效增强子。我们 框架将易于应用于其他调控功能，例如染色质捕获中的绝缘边界 数据通过开发一个压缩的，但灵活的，功能化的大量散装和单细胞数据纲要， 我们将使计算密集型预测算法的快速迭代能够应用于单细胞开放 染色质区域。我们的方法还将结合从数据丰富的迁移学习（例如，死后或 小鼠大脑）到数据贫乏的设置（例如，人类妊娠晚期大脑）。我们预计预测的调控元件 更丰富的精神疾病遗传风险，提供关于变异如何 导致疾病，并成为有用的分子工具。我们提出的两种计算方法将共同 利用批量数据和单细胞数据的互补优势，解决推动 发育中和成人大脑中细胞的精致多样性， 精神病