Method Development for Single-Cell Multi-omics

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，与遗传内容联合收割机以确定哺乳动物转录因子（TF）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基因来预测。 单次测量准确的预测模型需要同时测量多个测量值。 目前，我们受到在单个小区中可以执行的同时测量的数量的限制。在 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 在均质培养的细胞中或在对读数进行平均的大量组织中。之间相互作用的研究 不同细胞类型特异性表观遗传标记和异质组织中单细胞水平的基因表达 还处于初期阶段在过去十年中取得的进展解决了个体细胞的异质性， 使用单细胞技术在不同原代哺乳动物组织中的基因表达和表观遗传标记术语 测序技术，如单细胞RNA-seq。最近，我们和其他人开发了几种技术 在同一测定中同时捕获多个测量（多组学技术）， 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 对基因表达的影响。重要的是，在现有的实验中增加额外的“组学”测定， 方案不是两种现有测定的简单组合。相反，附加测定通常需要 整个实验方案的重新设计和/或新的计算方法的开发。添加 对同一实验或单细胞进行额外的“组学”分析将显著扩展我们目前对 基因调控这是不能通过将单独的单组学实验加入相同的等分试样中来实现的。 sample.鉴于这些挑战，意义，以及我独特的多学科学术训练，我的长期 我们的目标是开发高通量的实验分析和计算方法，以了解基因 通过整合来自相同测定或单细胞的多组学信息来调节。在本提案中，我们 将开发一种结合实验分析和计算方法，以表征多种高质量 在同一测定和单细胞中的细胞类型特异性表观基因组和转录组图谱。我们将进一步发展 一种综合测定法，通过多种方法表征遗传变异体对基因表达的调节作用， 在相同的单细胞中具有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，从而弥合 在不同的健康和病理条件下的遗传和表型变异。

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.