Accumulation of Exogenous Activated TGF-β in the Superficial Zone of Articular Cartilage

It was recently demonstrated that mechanical shearing of synovial fluid (SF), induced during joint motion, rapidly activates latent transforming growth factor β (TGF-β). This discovery raised the possibility of a physiological process consisting of latent TGF-β supply to SF, activation via shearing, and transport of TGF-β into the cartilage matrix. Therefore, the two primary objectives of this investigation were to characterize the secretion rate of latent TGF-β into SF, and the transport of active TGF-β across the articular surface and into the cartilage layer. Experiments on tissue explants demonstrate that high levels of latent TGF-β1 are secreted from both the synovium and all three articular cartilage zones (superficial, middle, and deep), suggesting that these tissues are capable of continuously replenishing latent TGF-β to SF. Furthermore, upon exposure of cartilage to active TGF-β1, the peptide accumulates in the superficial zone (SZ) due to the presence of an overwhelming concentration of nonspecific TGF-β binding sites in the extracellular matrix. Although this response leads to high levels of active TGF-β in the SZ, the active peptide is unable to penetrate deeper into the middle and deep zones of cartilage. These results provide strong evidence for a sequential physiologic mechanism through which SZ chondrocytes gain access to active TGF-β: the synovium and articular cartilage secrete latent TGF-β into the SF and, upon activation, TGF-β transports back into the cartilage layer, binding exclusively to the SZ

Accumulation of Exogenous Activated TGF-β in the Superficial Zone of Articular Cartilage

It was recently demonstrated that mechanical shearing of synovial fluid (SF), induced during joint motion, rapidly activates latent transforming growth factor β (TGF-β). This discovery raised the possibility of a physiological process consisting of latent TGF-β supply to SF, activation via shearing, and transport of TGF-β into the cartilage matrix. Therefore, the two primary objectives of this investigation were to characterize the secretion rate of latent TGF-β into SF, and the transport of active TGF-β across the articular surface and into the cartilage layer. Experiments on tissue explants demonstrate that high levels of latent TGF-β1 are secreted from both the synovium and all three articular cartilage zones (superficial, middle, and deep), suggesting that these tissues are capable of continuously replenishing latent TGF-β to SF. Furthermore, upon exposure of cartilage to active TGF-β1, the peptide accumulates in the superficial zone (SZ) due to the presence of an overwhelming concentration of nonspecific TGF-β binding sites in the extracellular matrix. Although this response leads to high levels of active TGF-β in the SZ, the active peptide is unable to penetrate deeper into the middle and deep zones of cartilage. These results provide strong evidence for a sequential physiologic mechanism through which SZ chondrocytes gain access to active TGF-β: the synovium and articular cartilage secrete latent TGF-β into the SF and, upon activation, TGF-β transports back into the cartilage layer, binding exclusively to the SZ

Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning.

Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease