Machine learning-based in vivo assessment of the role of the synovial microenvironment in the pathogenesis of inflammatory arthritis
The synovial tissue represents a unique microenvironment that has emerged as a critical factor shaping the course of rheumatoid arthritis (RA). Specifically, resident synovial immune cells such as macrophages exert not only pro-inflammatory, but also important tissue protective functions and thereby actively control onset and resolution of inflammation. Macrophages critically rely on reciprocal interactions with their local microenvironmental niche, but how the state of the microenvironment actually translates into changes of the inflammatory threshold within the synovial tissue remains unclear. Based on promising preliminary in vivo results, we will establish a novel intravital imaging platform of small peripheral mouse joints, which will allow us to directly access an intact synovial compartment and visualize the spatiotemporal dynamics of synovial immune cells in health and during onset of arthritis. As an interdisciplinary approach, we will develop an accompanying machine learning-powered quantitative analysis pipeline that will allow us to investigate the structure-function relationships of the synovial microenvironmental niche in situ. Here, we will identify methods suited for the analysis of these structures and develop novel, data-driven approaches that allow us to capture the temporal as well as the functional dynamics quantitatively. Our aim is to understand the direct role of stromal crosstalk and spatial biology in the functionality of synovial macrophages. This will help to better explain disease susceptibility at the level of tissue homeostasis and will pave the way to novel therapeutic approaches that do not only treat established arthritis, but prevent it from developing in the first place.