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

项目总结 单细胞基因组学的出现增强了我们研究异质细胞群体的能力(1)以追踪 细胞分化的时间进程和识别驱动因素，(2)识别潜在的新的细胞状态/类型 无人监督的方式，以及(3)识别与疾病结果相关的细胞群。最近，我们 其他人已经开发了单细胞多组学技术，使测量多种形式的 单个细胞同时存在，包括转录组、甲基组、表观基因组和表面标记 蛋白质。这些检测为更多地研究单细胞状态提供了前所未有的机会。 通过为这些化验开发必要的计算方法，我们可以获得更多 准确和深入地描述细胞状态，并获得对这些关系的机械性见解 在染色质状态、蛋白质组状态和单个细胞内的转录状态之间。然而， 研究多组学数据的可解释计算方法非常缺乏。为了弥补这一差距 并推动该领域向前发展，我们正在提议开发计算方法，将(1) 以无监督的方式表征多组环境中单个细胞的状态，(2)表征 通过鉴定转录因子结合活性和(3)鉴定多个 单细胞的结构变异。 首先，我们将开发基于可解释的主题建模的方法来表征单个细胞，基于 多体读数。以我们过去的成功为基础，使用主题模型准确地进行分类和表征 单细胞群体，我们将为多组学分析开发新的主题建模方法，以便 实现对细胞状态的更深层次的剖析，这将导致对 通过多组分析测量的多种形式，例如转录和表观基因组状态。第二，我们 提出发展一种单细胞转录因子足迹(TF)方法。传递函数的计算检测 足迹可以识别决定细胞状态的重要驱动因素的活跃转录因子的图景 和身份。我们将开发一种前所未有的方法来鉴定活跃的转录因子 单细胞分辨，并研究甲基组和转铁蛋白结合之间的联系。最后，我们将发展 利用新的多组体在单细胞分辨率下识别不同类型的结构变异的方法 化验是由我的合作者开发的。这种方法论将增强我们研究 不同细胞群体的结构差异。总体而言，拟议的一套计算方法 将允许生成和分析多组数据的研究人员的广泛受众对多组数据进行注释 以前所未有的深度和前所未有的方式在不同的细胞群中测量细胞状态 单元格分辨率。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.