Statistical methods for analysis of single-cell variability

 描述（由申请人提供）：健康和患病组织均由多种细胞类型组成，其相互作用是其功能的基础。即使在给定的细胞类型中，细胞 不同的转录状态，由于外部影响，该细胞的历史，或stocastic事件。这种异质性的影响在癌症治疗的背景下尤其显著，其中表型不同的亚克隆群体的存在助长复发和对治疗的抗性。作为与Catherine Wu（DFCI）实验室正在进行的合作的一部分，我们正在应用单细胞基因组测定来研究白血病细胞中的亚群动态。检查来自慢性淋巴细胞白血病（CLL）患者的样品，我们发现显著的转录和表观遗传异质性（朗道，Cancer Cell in press），并且旨在表征与治疗抗性相关的转录亚群，并建立它们与更好地研究的遗传亚克隆的关系。 虽然单细胞测定提供了 尽管它们是解剖异质组织的直接手段，但它们的应用目前受到缺乏用于其分析的敏感统计工具的限制。在这里，我们提出了新的统计方法的开发和应用，用于从常规和空间分辨的单细胞转录组测量中识别和表征生物学上不同的细胞亚群。基于我们对单细胞转录组数据进行统计建模的方法（Kharchenko，Nature Methods 2014），我们提出：1）使用基于敏感模型的因子分析来捕获转录变异性的结构; 2）实现一个框架来探索所有统计学上的差异。 细胞群体内异质性的重要方面，使得能够集中分析生物学相关异质性; 3）开发整合方法以在单细胞水平上比对转录和遗传肿瘤亚群; 4）将联合收割机误差模型与统计小波分析和马尔可夫随机场方法相结合，以从空间分辨的RNA-seq数据中识别异质性的空间模式，并对肿瘤和正常组织中的组织微结构进行建模。 鉴于明确的临床需要，以提高我们的肿瘤内异质性的癌症及其对治疗反应和耐药性的影响的理解，我们专注于这些方法的应用，在白血病患者的肿瘤亚克隆群体的分析。使用单细胞基因组测定，我们将检查在连续时间点从接受治疗的CLL患者中收集的样本，其中匹配的批量DNA和RNA测序数据已作为Wu实验室单独努力的一部分收集。应用所提出的方法，我们将1）表征亚克隆群体内和亚克隆群体之间的转录异质性，2）寻找与亚克隆扩增速率和对外周血中靶向B细胞受体途径的疗法的抗性相关的功能特征; 3）应用空间分辨RNA-seq方法来研究耐药白血病细胞的集中淋巴结储库。我们希望这些研究能为与治疗相关的细胞特征提供有价值的见解 阻力，并产生广泛适用的计算工具。

 DESCRIPTION (provided by applicant): Both healthy and diseased tissues are composed of multiple cell types whose interplay underpins their functions. Even within a given cell type, cells differ in their transcriptional state due to external influences, the history of that cell, or stocastic events. The impact of such heterogeneity is particularly notable in the context of cancer therapy, where presence of phenotypically distinct subclonal populations fuels relapse and resistance to treatment. As part of an ongoing collaboration with the laboratory of Catherine Wu (DFCI), we are applying single-cell genomic assays to investigate subpopulation dynamics in leukemia cells. Examining samples from patients with chronic lymphocytic leukemia (CLL), we find notable transcriptional and epigenetic heterogeneity (Landau, Cancer Cell in press), and are aiming to characterize transcriptional subpopulations associated with therapeutic resistance and establish their relationship to better-studied genetic subclones. While single-cell assays provide direct means to dissect heterogeneous tissues, their application is currently limited by the lack of sensitive statistical tools for their analysis. Here we propose the development and application of novel statistical methods for the identification and characterization of biologically distinct subsets of cells from regular and spatially-resolved single-cell transcriptome measurements. Building on our approach for statistical modeling of single-cell transcriptome data (Kharchenko, Nature Methods 2014) we propose to: 1) use sensitive model-based factor analysis to capture the structure of transcriptional variability; 2) implement a framework to explore all statistically significant aspects of heterogeneity within cell population, enabling a focused analysis of biologically relevant heterogeneity; 3) develop integrative approach to align transcriptional and genetic tumor subpopulations on a single-cell level; 4) combine error models with statistical wavelet analysis and Markov Random Field methods to identify spatial patterns of heterogeneity from spatially- resolved RNA-seq data, and model tissue microarchitecture in tumor and normal tissue. Given the clear clinical need to advance our understanding of intra-tumor heterogeneity in cancer and its impact on therapy response and resistance, we focus the application of these methods on the analysis of subclonal populations in tumors of leukemia patients. Using single-cell genomic assays, we will examine samples collected at serial time points from CLL patients undergoing therapy, for which matched bulk DNA and RNA sequencing data have been collected as part of a separate effort by the Wu lab. Applying the proposed methods, we will 1) characterize transcriptional heterogeneity within and between the subclonal populations, 2) search for functional features linked to subclonal expansion rates and resistance to therapies targeting B-cell receptor pathway in peripheral blood; 3) apply spatially-resolved RNA-seq methods to investigate focused lymph node reservoirs of drug-resistant leukemic cells. We expect these studies to provide valuable insights into cell characteristics associated with therapy resistance, and yield widely applicable computational tools.