Abstract
Broadly speaking, this thesis combines two subjects: advances of single cell sequencing technolgies, and the application of single cell sequencing technologies in a cancer setting. Since the first single cell sequencing technologies were established, the field has been revolutionized, now allowing the processing of thousands of single cells for either
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the sequencing of RNA, DNA or even epigenetic marks. In this context chapter 2 and chapter 3 describe advances of the sequencing technologies themselves. Over the last few years single cell sequencing techniques are increasingle used to answer biological questions. Chapters 4, 5 and 6 describe projects in which single cell sequencing was used to answer biological questions. Chapter 1 provides an overview of the development of single cell mRNA sequencing and the algorithms that have been developed to perform lineage tracing based on single cell transcriptomics data. It then describes recent advances in genetic lineage tracing and finishes by describing some very recent techniques that allow the combination of transcriptomic based and genetic lineage tracing from the same single cell. This combination has the potential to give a much deeper understanding of both pathologies and developmental systems. Chapter 2 describes an algorithm that can be used to deconvolve technical variability from single cell mRNA sequencing data. It uses the efficiency of detecting exogenous mRNA species to model technical variation and then uses these models to remove the technical variation from the data. Chapter 3 describes a technique through which it is possible to sequence both the mRNA and the genomic DNA from the same single cell. Chapter 4 uses single cell mRNA sequencing to investigate the tumor heterogeneity of breast tumors. Here, we find that within most breast tumors all major molecular subtypes of breast cancers will be present. Treatment decisions are often based on these molecular classification of tumors into subtypes, therefore this knowledge may hold promise to further optimize cancer treatment. Chapter 5 describes the clonal dynamics of a colon carcinoma model system. Here we show that selectional pressure is much higher in early stage colon cancer organoids than in late stage colon cancer organoids. Furthermore, we show that Copy Number Variation profiles are much more heterogeneous in late stage colon cancer organoids. Chapter 6 describes the molecular events leading up to the development of the first hematopoietic stem cells in the mouse embryo. Chapter 7 contains a discussion of the work presented in this thesis.
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