Statistical methods for analysis of single-cell variability

Harvard Medical School, Boston MA

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Abstract

? 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.