Integrative characterization of cell state via modeling of multi-omics data

项目摘要 单细胞基因组学的出现增强了我们研究异质细胞群的能力（1）， （2）在细胞分化的时间过程中识别潜在的新细胞状态/类型， 无监督的方式，和（3）识别与疾病结果相关的细胞群。最近，我们 和其他人已经开发了单细胞多组学技术，使得能够测量多种形式的 包括转录组、甲基化组、表观基因组和表面标记 proteins.这些检测方法为更多地研究单细胞状态提供了前所未有的机会。 全面;通过为这些测定开发必要的计算方法，我们可以获得更多 细胞状态的准确和更深入的表征，并获得对关系的机械见解 染色质状态、蛋白质组和转录组状态之间的关系。然而，在这方面， 严重缺乏可解释的计算方法来研究多组学数据。为了弥补这一差距 并推动该领域向前发展，我们建议开发计算方法，将（1） 以无监督的方式表征多组设置中单个细胞的状态，（2）表征 通过鉴定转录因子结合活性来确定单细胞的调控景观，以及（3）鉴定多个 单细胞的结构变化。 首先，我们将开发可解释的基于主题建模的方法，用于基于 多组读出基于我们过去成功的主题模型， 单细胞群体，我们将开发新的多组学分析的主题建模方法， 实现对细胞状态的更深入剖析，这将导致对细胞之间联系的潜在洞察。 通过多组学测定测量的多种模式，例如转录组学和表观基因组学状态。二是 提出发展单细胞转录因子足迹法（TF）。TF的计算检测 足迹可以识别决定细胞状态的重要驱动因素的活性转录因子的景观 和身份。我们将发展一种方法，以前所未有的速度鉴定活性转录因子。 单细胞分辨率，并研究甲基化和TF结合之间的联系。最后，我们将开发 通过利用新的多组学方法以单细胞分辨率鉴定不同类型的结构变异的方法 由我的合作者开发的检测方法。这种方法将提高我们研究的异质性， 不同细胞群体的结构差异。总的来说，所提出的一套计算方法 将允许广泛的研究人员谁产生和分析多组学数据注释的多- 在异质细胞群体中以深刻和前所未有的方式模态测量细胞状态， 单细胞分辨率。此外，我们的可解释方法将产生可检验的假设，以更好地 了解细胞状态。

PROJECT SUMMARY Advent of singe-cell genomics has enhanced our ability to study heterogeneous cell populations (1) to track the time course of cellular differentiation and identify drivers, (2) to identify potentially novel cell states/types in an unsupervised manner, and (3) to identify cell populations that are linked disease outcomes. More recently, we and others have developed single-cell multiomics technologies that enable measuring of multiple modalities of a single cell at the same time, including the transcriptome, the methylome, the epigenome and surface marker proteins. These assays offer unprecedented opportunities to study the state of single cells more comprehensively; by developing the necessary computational methods for these assays, we can obtain more accurate and deeper characterization of cell states and obtain mechanistic insights into the relationships between state of chromatin, the proteome and the transcriptomic states within an individual cell. However, there is a dramatic lack of interpretable computational methods to study multiomics data. To address this gap and propel the field forward, we are proposing to develop computational methodologies that will (1) characterize the state of a single cell in a multiomic setting in an unsupervised manner, (2) characterize the regulatory landscape of single-cells by identifying transcription factor binding activity and (3) identify multiple structural variations at single-cells. First, we will develop interpretable topic-modeling based methods for characterizing single cells based on multiomic readout. Building on our past success with topic models to accurately cluster and characterize single-cell populations, we will develop novel topic modeling approaches for multiomics assays in order to achieve a much deeper profiling of the state of a cell, which will lead to potential insights into the links between multiple modalities measured by a multiomic assay, such as transcriptomic and epigenomic state. Second, we propose to develop a single-cell transcription factor footprinting (TF) methods. Computational detection of TF footprints can identify the landscape of active transcription factors that determine important drivers of cell state and identity. We will develop the methodology to identify active transcription factors at an unprecedented single-cell resolution and to investigate links between the methylome and TF binding. Lastly, we will develop methods to identify different types of structural variations at single cell resolution by leveraging novel multiomic assays developed by my collaborators. This methodology will enhance our ability to study the heterogeneity of structural variations in different cell populations. Overall, the proposed suite of computational methodologies will allow a broad audience of researchers who generate and analyze multiomic data to annotate the multi- modally measured cell states in heterogeneous cell populations in a deep and unprecedented manner at single-cell resolution. Furthermore, our interpretable methods will yield testable hypotheses to better understand the cell state.