Quantitative analysis of early follicular helper T cell development using spatiotemporal modeling and in vivo mouse models

• Applicants: Professor Dr. Andreas Hutloff; Professor Dr. Kevin Thurley
• Subject Area: Immunology ; Bioinformatics and Theoretical Biology
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 460181884

T follicular helper (Tfh) cells are the CD4+ T cell subset providing help for B cells during the germinal center reaction, and therefore are an attractive therapeutic target, for instance in the context of antibody-mediated autoimmune diseases. Despite the central biological role of this T cell subset, Tfh cell differentiation is still incompletely understood. This is largely related to the fact, that in contrast to other CD4+ T cell subsets like Th1, Th2 and Th17, Tfh cells cannot be generated in vitro, suggesting that development of this Th subset requires the complex environment of secondary lymphoid organs (SLO). Within SLO, accumulating evidence suggests the occurrence of spatial cytokine gradients among immune cell populations, at least in the well-studied case of IL-2. Previous mathematical modeling studies by us and others predicted local IL-2 concentration differences over 1 order of magnitude and more. Recent in vivo studies demonstrated that microanatomical regions of high and low IL-2 within SLO are crucial for the fate decision between Tfh and Th1 development. Nevertheless, gradients of many other cytokines and their dynamic interplay, as well as biological consequences for Th cell differentiation, remain largely an uncharted area. Here, we will use an interdisciplinary combination of single-cell experiments, high-resolution quantitative histology, and spatio-temporal modeling, to derive a quantitative “cartography” of Th cell signaling and differentiation dynamics in the very early phases of Tfh cell differentiation. Specifically, we will use a mouse model that allows analyzing antigen-specific T cells and lymphoid tissue directly ex vivo at several early time points after immunization. Data-driven modeling will allow systematic characterization of the parameter range and will thus derive predictions regarding the effect of perturbations such as cytokine blocking antibodies. In turn, such model predictions can be further assessed within the project by direct assessment of tissue samples, in a closed-loop “iterative cycle of systems biology”. Taken together, our study will gain functional, quantitative insights into the tissue architecture of early decision-making processes during Tfh cell differentiation, and into spatio-temporal cell-cell communication signaling dynamics in general.

• DFG Programme: Research Grants