DEMORA: DEep spatial characterization of synovial MacrOphages in Rheumatoid Arthritis
Lead Research Organisation:
Queen Mary University of London
Department Name: William Harvey Research Institute
Abstract
Rheumatoid Arthritis (RA) is the most common autoimmune disease affecting the joints. The primary site of the disease process is the synovial tissue (ST), where macrophages play a central role in driving inflammation. In physiologic conditions, the synovial membrane consists of a thin lining and a relatively acellular sublining containing a few resident macrophages, but in RA, the ST changes considerably. Three different "pathotypes" have been described depending on the "quantity/quality" of the cellular infiltrate: lympho-myeloid, diffuse-myeloid, and pauci-immune. Although in different amounts, synovial tissue macrophages (STM) are present in all three pathotypes. In the last five years, single-cell molecular profiling of STM has revealed the existence of phenotypically and functionally distinct clusters, leading to the definition of a new STM taxonomy. Here, I hypothesize that different STM clusters shape the synovial tissue micro-environment in RA and influence tissue pathology and response to treatment. By applying to an existing unique bioresource of >800 ST, cutting-edge technology such as digital spatial profiling integrated with single-cell RNA-seq and in silico deconvolution, I aim to: determine the location/topographical distribution of STM clusters and the existence of specific "niche" (e.g., intra/peri-ectopic lymphoid structures, peri-vascular or nerve); identify STM clusters associated with each synovial pathotype and driving pathotypes transition; assess if specific STM clusters predict clinical response to anti-rheumatic drugs, how they change post-treatment, and which subsets emerge in patients refractory to multiple medications. This in-depth STM characterization in RA ST will enhance our understanding of the mechanisms sustaining chronic arthritis and non-response to treatments, integrate and improve predictive algorithms based on ST cellular/molecular signatures, and may suggest new therapeutic targets.
| Description | Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects the joints, causing persistent pain, bone and cartilage destruction, and disability. Although multiple treatments are available, around 40% of patients do not respond to individual therapies, and between 5% and 20% are refractory to all available treatments. The reasons behind this lack of response remain unknown, and there are currently no medical tests to predict treatment outcomes. In this context, macrophages, a type of immune cell, have been identified as potential regulators of treatment response. However, these cells are highly diverse and can exist in nine different subtypes in the synovial tissue (the primary site of inflammation in RA), each with specific functions in the body. In this study, we developed a computational tool to analyse genetic data and estimate the abundance of each macrophage subtype in the joints of RA patients. We used data from patients in a clinical trial called R4RA, where all participants had previously failed to respond to one treatment and were randomly assigned to receive either rituximab or tocilizumab. Our results show that patients who responded to treatment had higher levels of specific macrophage subtypes before starting therapy compared to those who were resistant to all treatments, suggesting that certain subtypes may be more prevalent in patients who are more likely to respond to treatment. Additionally, when comparing abundances before and after the treatment, some macrophage subtypes decreased in responders but remained unchanged in non-responders. This suggests that the resolution of inflammation may be driven by the selective depletion of specific macrophage subtypes, particularly those associated with tissue repair and immune regulation. We also found that rituximab and tocilizumab affect macrophages differently. Rituximab altered the abundances of four macrophage subtypes, while tocilizumab affected two other subtype. Overall, these findings highlight the complexity of macrophage modulation in response to targeted therapies and underscore the need for further investigation into the functional consequences of these changes, particularly to treatment response and long-term disease outcomes. Finally, we analysed how macrophage subtypes were linked to different molecular pathways in the immune system, which may provide insights into disease mechanisms and potential therapeutic targets. Our findings suggest that some macrophages are involved in interferon signalling, a key process in immune response, while others may be related to blood vessel integrity and immune cell migration. To summarise, these discoveries highlight the complexity of how macrophages respond to treatment and suggest that targeting specific macrophage subtypes could improve RA therapies. This research could help develop more personalised treatments by identifying which patients are more likely to respond to certain drugs. However, further studies are needed to fully understand these mechanisms and translate them into clinical applications. |
| Exploitation Route | The outcomes of this funding could be taken forward and utilised in several ways: 1) Patient classification using immunohistochemistry: Now that we have identified the macrophage subtypes involved in treatment response or resistance, our findings could be applied to synovial tissue biopsies. By using immunohistochemical staining, patients could be classified based on a macrophage subtype abundance score. 2) Predicting treatment response with machine learning: Bulk RNA-seq data could be used in machine learning models to predict which treatment would be most effective for each patient. This approach could help personalise RA treatment strategies and improve patient outcomes. 3) Development of an accessible web tool: a web-based tool could be created where users simply input bulk RNA-seq data from a patient, and the system predicts where they are likely to respond to a given treatment or if they are refractory to all available options. This would provide practical and accessible resource for clinicians to guide treatment decisions. By implementing these approaches, our findings could help refine patient stratification, improve treatment selection, and ultimately enhance clinical care for RA patients. |
| Sectors | Pharmaceuticals and Medical Biotechnology |