CD4+ T celll differentiation explored through Markov process

Abstract

In this thesis I discuss a project in collaboration with Professor Sarah Teichmann’s group (MRC Laboratory of Molecular Biology and EMBL-EBI, Cambridge). We have been working on a cell transformation process of naive cells into Th2 cells (also in this case the data has been purposely derived in Teichmann’s lab). Here we investigate population data on cells at different stages of differentiation (FACS data) which contain a wealth of information on the microscopic nature of the events leading na ive cells becoming Th2 cells. Many differentiation processes occur hand-in-hand with a change in cell cycle status: this can be cell cycle arrest, as in the monocyte to macrophage transition, cell cycle entry, as for the pre-adipocyte to adipocyte differentiation, and entry and subsequent cell division, as in T helper (Th) cell differentiation. Th cell differentiation is the process where naive CD4+ T cells transition to effector lymphocytes and is central to mammalian adaptive immunity. After antigen stimulation of the T-cell receptor in the preence of specific cytokines, naive Th cells start dividing rapidly to reach a differentiated state, with the best understood being Th1, Th2, Th17 and pTregs. So far, several master regulators have been identified and there is considerable insight into their regulatory networks. While much is known in CD8+ (killer) T cells, the expansion of CD4+ (helper) T cells during an infection is less well understood at the cellular and molecular levels. How does the coupling between differentiation and the cell cycle occur in CD4+ T cells? Are the two processes indipendent and orthogonal or linked through molecules and hence intertwined? Does differentiation occur in a gradual manner as suggested by many studies, including a recent single-cell analysis of lungh epithelial development or in a cooperative switch-like manner? Here, we use a new approach to tackle these questions, which is to extract biologically intermediate states of differentiation from a single chronological time point. By sorting out separate cell populations from a single cell culture of asynchronized, dividing cells, we aimed to reduce the biological variability in cytokine exposure, confluence, etc. With this approach we minimize the biological noise in our data and focus entirely on the processes of cell division and differentiation. We used in-depth transcriptome profiling coupled with bioinformatics data analysis to identify three major cell states during Th2 differentiation. By counting cells in each cell generation using flow cytometry, we modelled the rates of death, division and differentiation using a discrete time Markov branching process. This model gives information about which kind of transitions are most likely to happen at single cell level; for example, it can say if an activated cell (still not differentiated) is most likely to give rise to differentiated cells through a duplication or in a direct way. This revealed a higher cell division rate for differentiated cells compared with proliferating, activated cells. We validate those finding by DNA staining and by single-cell live imaging of T h2 cells. These in vitro data supported the idea of a fine-tuned relationship between cell cycle speed and differentiation status in CD4+T cells.