Immunophysics

Our tissues are constantly exposed to various stresses — such as pathogens or dead cells and debris — which can cause unnecessary inflammation. To quickly resolve such incidents and keep tissues in homeostasis, Resident Tissue Macrophages (RTMs) reside in essentially every tissue in the body where they constantly monitor their environment. This so-called sampling is associated with characteristic morphological shape changes providing crucial information about the activation state of RTMs and how they ensure tissue homeostasis. Together with our collaborators (group of Stefan Uderhardt, Uniklinikum Erlangen), we built an advanced image analysis pipeline to assess the morphodynamics of RTMs from high-resolution intra-vital imaging data. We found that RTMs in steady state span a surprisingly broad naïve morphospace, within which they shift — but rarely leave — upon stimulation. Strikingly, the analysis pipeline detected the detrimental effects of ageing on RTMs and demonstrated how addition of a specific cytokine alleviates such effects and could potentially restore tissue homeostasis. When focusing on individual cell protrusions, we discovered that their periodic sampling motion covers a surprisingly small space. Astonishingly, protrusions of the same cell show a division of labour in terms of their sampling radius, size and lifetime, hinting at different tasks and functions they need to fulfil.

Autoimmune diseases and similar autoinflammatory diseases (AIIDs) are a group of disorders caused by immune system dysregulation, in which the immune system recognizes host molecules as pathogenic and develops an immune response against the host itself. One prominent example of such disorders is psoriasis. It is an immune-driven skin disorder characterized by the appearance of erythematous scaly plaques on the skin surface. As with other AIIDs, there is no universal cure for psoriasis, but combined therapeutic approaches can mitigate the symptoms for some time. At the same time, there is still no clear understanding of the disease onset mechanisms, which makes it an exciting and complex problem involving a tangled network of different immune cell types from both adaptive and innate immunity, various molecular signals such as cytokines, chemokines, antimicrobial peptides, and proteases, as well as skin cells. The process may even extend further to the nervous system, involving nerve cells and neuropeptides.

Together with our collaborators Prof. G.C.L. Wong (UCLA, also Rosalind-Franklin Scientist in Residence at MPZPM) and Prof. R. Gallo (UCSD) we want to untangle this challenging signaling network of interacting cells and molecules and describe it using mathematical and computational models. By applying dynamical systems theory, numerical calculations, statistical analysis, we are able to identify the most and least influential interactions, classify and reproduce known clinical states, and even simulate potential therapies within the model. We are using state of the art physics informed machine learning approaches to integrate biomedical data into our models and thus combine theory and experiment.