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Mapping structural rearrangements in single cells by template strand sequencing to explore inversions in the human genome Sanders, Ashley Diane

Abstract

Studies of genome heterogeneity and plasticity aim to resolve how genomic features underlie phenotypes and disease susceptibilities. Identifying genomic features that differ between individuals and cells can help uncover the functional variants that drive specific biological outcomes. For this, single cell studies are paramount, as characterizing the contribution of rare but functional cellular subpopulations is important for disease prognosis, management and progression. Until now, these studies have been challenged by our inability to map structural variants accurately and comprehensively. To overcome this, I employed the template strand sequencing method, Strand-seq, to preserve the organization and structure of individual homologues and visualize structural rearrangements in single cells. Using Strand-seq, I monitored homologue states in human genomes to quantify the degree of somatic rearrangements, and distinguished these from recurrent structural variants, such as inherited inversions. In so doing, I created an innovative tool to rapidly discover, map, and genotype structural polymorphisms with unprecedented resolution. Next, to facilitate systematic analyses of Strand-seq data, I developed novel bioinformatic software that locates putative genomic rearrangements in singles cells and identifies recurrent rearrangements across multiple cells. This provides an essential instrument for unbiased and non-targeted structural variant discovery in a high-throughput approach, helping to scale Strand-seq for population-based studies. Applying these tools, I explored the distribution and frequency of structural variation in a heterogeneous cell population to discover and genotype over 100 inversions in the human genome. I found significant structural heterogeneity resides in definable polymorphic domains and within complex and repetitive regions of our genome. Finally, I extended my strategy to comprehensively map the complete set of inversions in an individual’s genome and define their unique invertome. Comparing two invertomes, I found sets of inversions can be combined to make predictions about ancestry and health of an individual, and I characterized the architectural features of inversion breakpoints with base-pair resolution. Taken together, I describe a powerful new framework to study structural rearrangements and genomic heterogeneity in single cell samples, whether from individuals for population studies, or tissues for biomarker discovery.

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Attribution-NonCommercial-NoDerivs 2.5 Canada

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