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: 10579845
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579845
• 关键词: 3-Dimensional; ATAC-seq; Address; Adult; Algorithms; Anatomy; Atlases; Autopsy; Base Pairing; Binding Sites; Biological Assay; Biological Process; Brain; Cell Differentiation process; Cell physiology; Cells; Cellular Assay; Censuses; Central Nervous System; Chickens; Chromatin; Code; Collection; Compensation; 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; Human; Individual; Learning; Link; Machine Learning; Maps; Measurement; Mental disorders; Methods; Modeling; Molecular; Mus; Mutation; Nucleotides; Outcome; Pathway interactions; Pattern; Performance; Population; Pregnancy; Proteins; Regulator Genes; Regulatory Element; Reporter; Resolution; Risk; Sampling; Signal Transduction; Source; Source Code; Specificity; Structure; Technology; Testing; Theoretical model; Time; Tissue Sample; Tissues; Transgenic Mice; Untranslated RNA; Validation; Variant; Weight; autism spectrum disorder; 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专门化的重要决定因素，而且 调节元件中的核苷酸变化会增加精神疾病的风险。因此，我们 假设这些衰弱的疾病部分是由改变基因表达和基因表达的遗传变异驱动的 扰乱脑组织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方法 通过将变量映射到特定于和共享的调控元素来检验这一假设的方法 细胞群。这个策略有两个主要障碍，我们的项目针对这两个障碍提出了建模解决方案。 首先，尽管单细胞ATAC测序(scatac-seq)是目前最好的检测方法，但它存在低 分辨率，意味着一个开放的染色质区域可能被给定细胞中的零个或几个读取所支持。这 这使得识别连贯的细胞群体变得困难。提出了一种半监督聚类的网络模型 利用来自更高覆盖率的整体组织实验和单细胞的信息的scatac-seq中的细胞 RNA测序(scRNA-seq)，如果有的话。将这一模型应用于简编的预期结果 来自公共存储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)发现开放染色质为 富含与精神障碍相关的基因变异。仅有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 实施计算效率高的机器学习框架，以预测特定的监管 单细胞开放染色质区域的功能来自我们的网络模型和其他方法。基因调控 增强剂尤其适合这种方法，因为高通量的小鼠转基因和 大规模的平行报告分析已经为监督学习产生了足够多的有效增强剂。我们的 框架将很容易应用于其他调节功能，例如在染色质捕获中隔离边界 数据。通过开发一种压缩的、但又灵活的海量批量和单单元数据汇编的特征， 我们将使用计算密集型预测算法实现快速迭代，并将其应用于单细胞开放 染色质区域。我们的方法还将包括从数据丰富的(例如，死后或 小鼠大脑)到数据匮乏的环境(例如，人类妊娠晚期的大脑)。我们预计会出现预期的监管因素 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结合在一起 利用批量数据和单元格数据的互补优势来解决推动 发育中和成人大脑中细胞的精细多样性，以定位非编码的贡献 精神疾病。