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Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200–CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

Background: Clonal T cell populations are frequently detected in patients with rheumatic diseases. The relevance of this finding is often uncertain, as the clinical spectrum can range from being asymptomatic to T cell leukemia. Former studies suggested that certain anti-rheumatic drugs might influence the course of the clonal T cell populations. Methods: A prospective long-term follow up study was performed including patients with rheumatic diseases and clonal T cell populations. Clinical features, adverse events, especially infections and cytopenias, and immunosuppressive medication were assessed. T cell populations were characterized by polymerase chain reaction, flow cytometry and stimulated cell cultures. Results: 28 Patients with rheumatoid arthritis, spondyloarthritis, or giant cell arteritis were prospectively followed for up to 7.6 years. Severe infections or cytopenias (10.7% autoimmune neutropenias) were rare. The clonal T cell populations mostly persisted over time, the tumor burden decreased in the long-term. The cytokine secretion in stimulated T cell cultures did not differ in the subgroup of RA patients with versus without clonal T cells. Conclusion: Clonal T cell populations in patients with rheumatic diseases are common, but are rarely harmful. Feared neutropenia, infections or progression into T cell leukemia could not be detected in the long-term in our cohort.

Background: The development and potential of artificial intelligence (AI) is remarkable. Its application in all medical disciplines, including rheumatology, is attracting attention. To what extent AI is already used in clinical routine in rheumatology is unknown. In addition, the perceived barriers, potentials, and expectations regarding AI by rheumatologists have not yet been studied.Objectives: To examine the current usage and perceived barriers and facilitators of AI, including large language models (LLMs), among rheumatologists.Design: National, observational, non-interventional, and cross-sectional web-based study.Methods: A web-based survey was developed by the Working Group Young Rheumatology (AGJR) of the German Society for Rheumatology. The survey was distributed at the Congress of the German Society for Rheumatology and via social media, QR code, and email from August 30 until November 4, 2023.Results: Responses from 172 rheumatologists (55% female; mean age 43 years) were analyzed. The majority stated that they did not previously use AI (73%) in their daily practice. Eighty-eight percent of rheumatologists rated their AI knowledge as low to intermediate and 84% would welcome dedicated training on LLMs. The majority of rheumatologists anticipated AI implementation to improve patient care (60%) and reduce daily workload (62%). Especially for diagnosis (73%), writing medical reports (70%), and data analysis (70%), rheumatologists reported a potential positive benefit of AI. Main AI concerns addressed the responsibility for medical decisions (64%) and data security (58%).Conclusion: Overall, the results indicate that rheumatologists currently have little AI knowledge and make very little use of AI in clinical routine. However, the majority of rheumatologists anticipate positive AI effects and would welcome increased AI implementation and dedicated training programs.