PHD2018-05

Deciphering the activation states of plasmacytoid dendritic cells, their dynamical relationships and their molecular regulation

Scientific background

Functional heterogeneity exists within each cell type, such as the restriction of cytokine production to only a fraction of activated immune cells. The underlying mechanisms largely remain to be identified. Single cell transcriptomic and epigenetic profiling revealed intra-type heterogeneity in immune cells already at ground state. Its biological meaning remains puzzling. Here, we will study a striking example of this phenomenon: the control of type I interferon (IFN-I) production by mouse and human plasmacytoid dendritic cells (pDC). Indeed, pDC IFN-I production is tightly regulated in time and in space, including in our mouse model of viral infection. Interestingly, this regulation is altered in breast tumors. To understand the molecular regulation of IFN-I production by pDC, we will investigate their functional heterogeneity, by combining various high-throughput single cell measurements, their integrated bioinformatics analysis and mathematical modelling.

PhD Objectives

We aim at characterizing the activation states of pDC, their relationships and their functional specialization, to advance our understanding of the physiological functions of these cells and their molecular regulation. Specifically, we want to decipher how specific functions of individual pDC, including IFN-I production, are controlled molecularly, likely by the integration of a combination of exogenous instructive signals, from the tissue microenvironment including infected/tumor cells, and from cell-intrinsic stochastic events.

Proposed approach 

Experimental: We use a reporter mouse expressing a fluorescent protein under the control of the Ifnb1 promoter, for ex vivo enrichment of IFN-I-producing pDCs by flow cytometry prior to transcriptomic and epigenetic single cell profiling (scRNAseq & scATACseq). Computational analysis of scRNAseq experiments identified distinct splenic pDC clusters/activation states during viral infection. Pseudo-temporal analyses are ongoing to model pDC activation trajectories. Kinetics experiments need to be performed to correlate pseudo-time with real time. Combined single cell transcriptomic and phenotypic profiling (CITE-seq) and epigenomic data (ATAC-seq) remain to be generated and integrated, as well as data in other pathophysiological contexts. To infer how pDC activation is controlled molecularly, mathematical predictive dynamic models will be established using different formalisms, based on the regulatory circuits differentially expressed between pDC activation states, and refined by iterative confrontation to experimental testing.

PhD student’s expected profile

Our interdisciplinary project is at the interface of immunology, computational biology and mathematics. It integrates various types of approaches, including wet lab experimentations, computational analyses and mathematical modeling of the regulatory networks differentially expressed between pDC activation states, to infer how pDC activation and functions are molecularly regulated. We seek a candidate with solid bases in computational biology, including experience in RNA-seq data analysis if possible, and a strong interest in data integration and mathematical modeling. Expertise or interest in performing wet lab molecular biology experiments would be a plus.

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