Conclusion
We used a novel approach to analyze OA synovium, identifying patient-specific inflammatory clusters. Our findings indicate that phenotyping synovial inflammation may provide new insights into OA patient heterogeneity and biomarker development.
Methods
Cell composition and soluble protein production were measured by flow cytometry and multiplex enzyme-linked immunosorbent assay in synovium collected from OA patients undergoing knee replacement surgery (n = 35).
Objective
Synovial membrane inflammation is common in osteoarthritis (OA) and increases cartilage injury. However, synovial fluid and histology studies suggest that OA inflammatory responses are not homogeneous. Greater understanding of these responses may provide new insights into OA disease mechanisms. We undertook this study to develop a novel multiparameter approach to phenotype synovial responses in knee OA.
Results
Testing disaggregation conditions showed that aggressive digestion improved synovial cell yield and mesenchymal staining by flow cytometry, but it negatively impacted CD4+ T cell and CD56+ natural killer cell staining. Less aggressive digestion preserved these markers and showed highly variable T cell infiltration (range 0-43%; n = 32). Correlation analysis identified mesenchymal subpopulations associated with different nonmesenchymal populations, including macrophages and T cells (CD45+CD11b+HLA-DR+ myeloid cells with PDPN+CD73+CD90-CD34- mesenchymal cells [r = 0.65, P < 0.0001]; and CD45+CD3+ T cells with PDPN+CD73+CD90+CD34+ mesenchymal cells [r = 0.50, P = 0.003]). Interleukin-6 (IL-6) measured by flow cytometry strongly correlated with IL-6 released by ex vivo culture of synovial tissue (r = 0.59, P = 0.0012) and was highest in mesenchymal cells coexpressing CD90 and CD34. IL-6, IL-8, complement factor D, and IL-10 release correlated positively with tissue cellularity (P = 0.0042, P = 0.018, P = 0.0012, and P = 0.038, respectively). Additionally, increased CD8+ T cell numbers correlated with retinol binding protein 4 (P = 0.033). Finally, combining flow cytometry and multiplex data identified patient clusters with different types of inflammatory responses.