Biophysical applications of single-cell sequencing to measure frequencies and speeds

Abstract

This thesis deals with a number of diverse biological topics. They range from the study of the formation of the early body plan during mammalian development, to the characteristics of the DNA replication machinery and its interaction with the transcription machinery. The connecting theme is the methodologies employed to study these diverse questions. Doing this not in living animals but in vitro makes it possible to reduce a substantial amount of variability. Because we have control over the external conditions of our models, it is possible to apply sensitive perturbations to elucidate mechanisms of action. Additionally, in vitro models enable the application of advanced tools that are hard or impossible to apply otherwise. In this thesis, by applying and developing sequencing assays to in vitro systems, we quantitatively measure various physical cellular phenomena. In chapter 2 we characterize gastruloids, which are an in vitro model of mammalian development. Using scRNA-seq and Tomo-seq we find a neuro-mesodermal differentiation trajectory that is organized along the main body-axis. We then perform time-lapse imaging and quantify the dynamic behavior of the somitogenesis clock. Finally, by optimizing the culturing protocol we find that the somitogenesis machinery also produces patterned somites. In chapter 3 we describe a novel single-cell sequencing method that enables measuring DNA synthesis named scEdU-seq. We then go on to quantify DNA replication speeds by labeling the same cell twice and measuring the distance between successive labeling windows using a custom analysis workflow. Finally, we apply the method to describe the effect of transcription-induced DNA damage on the DNA replication speed. In chapter 4 we describe a multimodel single-cell sequencing method that allows the quantification of both nascent DNA and the transcriptome from the same cell, named sc-T-EdU-seq. We show that this method accurately quantifies nascent DNA which allows us to infer cell-cycle progression and to obtain cell-cycle resolved gene expression patterns. We then present proof-of-principle analysis that allow us to quantify cellular strategies to avoid TRCs, including strategies based on genome organization, cell state, and local avoidance.