Modeling cell differentiation dynamics from single cell genomics data (360G-Wellcome-203828_Z_16_A)

We aim to understand in detail the dynamics of how white blood cells (specifically T helper cells) react to infections by multiplying rapidly and at the same time adapting their cell state to fight the infection. In particular, we focus on a mouse model of malaria where T helper cells differentiate into two subtypes: Th1 and Tfh. By quantitatively profiling the T helper cell population at different time points during a malaria infection, we expect to improve our understanding of the mechanisms which are responsible for the cell proliferation and specialisation. We study the cell population by detecting RNA expression, surface markers and cell divisions at the single cell level. The RNA expression will provide clues as to genes which are driving this process, and we will test a subset of genes using CRISPR knock outs. In addition to a better knowledge of the immune system, we hope to develop new mathematical and computational methods that will be widely applicable to modeling cell proliferation and differentiation data in diverse biological contexts.