Systems biology meets chronic inflammation
While research has provided an enormous body of knowledge about the regulatory mechanisms behind chronic inflammation, it is difficult to assess the contribution of each individual process with current biological methods. In particular, what are the critical components and conditions triggering a change towards chronic inflammation, despite the multi-faceted mechanisms that promote immune tolerance? Furthermore, can we develop a rationale for improved strategies of therapeutic intervention? Currently available drugs targeting immune cell communication (so-called targeted or “biological” therapies), such as TNF-alpha blockers, often show limited effectiveness and considerable adverse effects.
The goal of our group is to develop an interdisciplinary framework for dissecting and rationalizing intercellular communication networks, to investigate the effects of perturbations and thus pave the road for optimization of targeted therapies in the future.
Quantifying immune cell interaction networks
The mammalian immune response depends on the interaction and collaboration of many highly individual cells. Further, cells themselves are regulated by complex intracellular networks such as signal transduction and gene regulation, resulting in two-fold regulation by a “network of networks”. Response-time modeling, a mathematical concept we previously established (Thurley et al., Cell Systems 2018), allows analysis of cell-cell communication circuits by directly integrating measurable kinetic data.
Currently, we are using that approach for dissecting immune-cell decision making at the onset of chronic inflammation, in the context of well-established in vivo model systems studied by our collaborators.
Analyzing paracrine cell-cell communication in 3D
Cell-cell communication by diffusible ligands generates spatial signaling gradients, with far-reaching consequences for immune-cell decision making in compartments such as secondary lymphoid organs. In a 3D model of IL-2 signaling among Th cells, we previously found that substantial cytokine gradients can arise even in the case of limited cellular heterogeneity, and such cytokine gradients are critical for paracrine signaling efficacy (Thurley et al., PLoS Comp Biol 2015).
Using a new, highly efficient simulation approach, we currently investigate spatial signaling dynamics systematically, and we work on mapping our model simulations to high-content histology data from collaborating groups.
Quantitative analysis of high-dimensional data sets
In recently published work together with the Radbruch group at the DRFZ, we analyzed the antigen-specific T cell receptor repertoire after measles re-vaccination (Cendon et al. 2020, preprint). We found that the repertoire at day 14 after vaccination contained a substantial portion of “circulating, persistent” and “mobilized, persistent” clones, indicating the importance of tissue-resident T cells for immunological memory. Other ongoing data analysis projects include the study of single-cell and kinetic gene-expression data on Th cell differentiation, and of gene-expression data from patient-derived T and B cells.