Resolving developmental dynamics using single-cell sequencing data

Abstract

How an organism grows and develops is one of the fundamental questions in biology. A deeper understanding of developmental programmes can help to answer those questions and also generate new insights into disease and potential treatments thereof – pathways active during development often reappear in a disrupted form in disease. Over the past decade, single-cell sequencing has become one of the key technologies to generate high resolution in vivo snapshots to study developmental trajectories. After giving an overview of the current state of single cell technologies and computational methods, I continue with my work on cell cycle in mouse embryonic stem cells. Pairing fluorescence reporters with single-cell transcriptomic data I describe various ways for cell cycle inference, identify cell cycle regulatory dynamics and study the impact of genetic knockouts and other biological systems on these dynamics. The third chapter covers my computational work generating single cell atlas of developing mouse forebrain. Most importantly, a lineage tree inference allows for an unprecedented description of the switch from neurogenesis to gliogenesis identifying primed cell states and branching points. I continue with an application of the forebrain atlas comparing brain development and brain cancer. After identifying the recapitulation of developmental trajectories in a mouse model of glioblastoma I extend the analysis further onto human data. Lastly, I present a single cell multiomics atlas of mouse skin development using transcriptomics and chromatin accessibility. This multimodal approach allows for a better description of cell states during skin development and homeostasis. I propose the existence of a slow and fast differentiating lineage during skin homeostasis and a distinct switch between cell states over those trajectories as well as improve the current understanding of the growth dynamics in the various skin compartments over development.