Method Development for Single-Cell Multi-omics

单细胞多组学方法开发

• 批准号: 10671589
• 负责人: Yaping Liu
• 金额: $ 13.68万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10671589
• 关键词: 3-Dimensional; Academic Training; Address; Aliquot; Binding; Biological Assay; Cells; Complex; Computing Methodologies; Cultured Cells; DNA Methylation; Development; Disease; Drug Targeting; Early Intervention; Epigenetic Process; Gene Activation; Gene Expression; Gene Expression Regulation; Genetic; Genetic Variation; Genome; Genomics; Goals; Intervention; Knowledge; Maps; Measurement; Measures; Modification; Pathologic; Phenotype; Protocols documentation; Public Health; Role; Sampling; Techniques; Technology; Tissues; Variant; cell type; design; epigenomics; experimental study; gene repression; genetic variant; histone modification; individual variation; infancy; method development; multidisciplinary; multiple omics; predictive modeling; single cell sequencing; single-cell RNA sequencing; transcription factor; transcriptomics

Abstract. Epigenetic modifications, including DNA methylation, histone modifications, and three-dimensional (3D) genome topology, combine with genetic content to determine mammalian transcriptional factor (TF) binding and thus, gene regulation. Gene activation or repression potential, however, cannot be entirely predicted by looking at a single measurement. Accurate predictive models require multiple measurements to be measured simultaneously. At present, we are limited by the number of simultaneous measurements that we can perform in a single cell. In addition, the interactions between different epigenetic marks and their effects on gene expression are revealed either in homogenous cultured cells or bulk tissues that average the readout. The study of interactions between different cell-type-specific epigenetic marks and gene expression in heterogeneous tissues at the single cell level is still in its infancy. Progress made during the last decade addressed the heterogeneity of individual cells in terms of gene expression and epigenetic marks at different primary mammalian tissues using single-cell sequencing techniques, such as single-cell RNA-seq. Recently, we and others developed several technologies to simultaneously capture multiple measurements in the same assay (multi-omics techniques) and extended them to the single-cell level. However, current single-cell multi-omics technology can only capture a couple of measurements, which limits our ability to fully understand the integration of epigenetic marks, genomics, and their effects on gene expression. Importantly, adding an additional “omics” assay in the existing experimental protocol is not a simple combination of two existing assays. On the contrary, the add-on assay will often require the re-design of the whole experimental protocol and/or the development of a new computational method. Adding additional “omics” assay to the same experiment or single cell will significantly expand our current knowledge on gene regulation. This is not achievable by joining separate mono-omics experiments in aliquots of the same sample. Given these challenges, significance, and my unique multidisciplinary academic training, my long-term goal is to develop high-throughput experimental assays and computational methods to understand gene regulation by integrating the multi-omics information from the same assay or single-cells. In this proposal, we will develop a combined experimental assay and computational approach to characterize multiple high-quality cell-type-specific epigenomic and transcriptomic maps in the same assay and single cells. We will further develop an integrated assay to characterize the regulatory role of genetic variants on gene expression through multiple intermediate epigenomic activities in the same single cells. These approaches will eventually allow us to address the fundamental questions for the interpretations of genetic variants and therefore bridge the gaps between genetic and phenotypic variations across different healthy and pathological conditions.

抽象的。 表观遗传修饰，包括DNA甲基化、组蛋白修饰和三维(3D)基因组 拓扑学，结合遗传含量来确定哺乳动物转录因子(TF)的结合，因此， 基因调控。然而，基因激活或抑制潜力不能完全通过观察一个 一次测量。准确的预测模型需要同时测量多个测量值。 目前，我们受到在单个细胞中可以进行的同时测量的数量的限制。在……里面 此外，还揭示了不同表观遗传标记之间的相互作用及其对基因表达的影响。 无论是在均质培养细胞中，还是在读数平均的大块组织中。相互作用的研究 不同细胞类型的不同表观遗传标记与单细胞水平上异质组织中的基因表达 目前仍处于初级阶段。在过去十年中取得的进展解决了单个细胞的异质性 利用单细胞技术研究不同原始哺乳动物组织中的基因表达和表观遗传标记 测序技术，如单细胞RNA-seq.最近，我们和其他人开发了几项技术 在同一化验中同时捕获多个测量结果(多组学技术)并扩展 达到单细胞水平。然而，目前的单细胞多组学技术只能捕获几个 测量，这限制了我们充分理解表观遗传标记、基因组学和 它们对基因表达的影响。重要的是，在现有的实验中增加了额外的“组学”分析 协议不是两种现有检测方法的简单组合。相反，附加分析通常需要 重新设计整个实验协议和/或开发新的计算方法。添加 对同一实验或单个细胞进行额外的“组学”分析将显著扩展我们目前对 基因调控。这不是通过在相同的等量中加入单独的单体组学实验来实现的 样本。鉴于这些挑战和意义，以及我独特的多学科学术培训，我的长期 目标是发展高通量的实验分析和计算方法来理解基因。 通过整合来自同一化验或单细胞的多组学信息进行调控。在这项提案中，我们 将开发一种实验分析和计算相结合的方法来表征多个高质量 同一检测和单个细胞中特定细胞类型的表观基因组和转录图谱。我们将进一步发展 一种综合分析方法来表征基因变异对基因表达的调节作用 同一单个细胞中的中间表观基因组活性。这些方法最终将使我们能够解决 解释遗传变异的基本问题，因此弥合了 不同健康和病理条件下的遗传和表型差异。