State transition and intercellular communication of synovial fibroblasts in response to chronic and acute shoulder injuries unveiled by single-cell transcriptomic analyses

We aimed to investigate the heterogeneity of synovial fibroblasts and their potential to undergo cell state transitions at the resolution of single cells.

We employed the single-cell RNA sequencing (scRNA-seq) approach to comprehensively map the cellular landscape of the shoulder synovium in individuals with chronic rotator cuff tears (RCTs) and acute proximal humerus fractures (PHFs). Utilizing unbiased clustering analysis, we successfully identified distinct subpopulations of fibroblasts within the synovial environment. We utilized Monocle 3 to delineate the trajectory of synovial fibroblast state transition. And we used CellPhone DB v2.1.0 to predict cell-cell communication patterns within the synovial microenvironment.

We identified eight main cell clusters in the shoulder synovium. Unbiased clustering analysis identified four synovial fibroblast subpopulations, with diverse biological functions associated with protein secretion, ECM remodeling, inflammation regulation and cell division. Lining, mesenchymal, pro-inflammatory and proliferative fibroblasts subsets were identified. Combining the results from StemID and characteristic gene features, mesenchymal fibroblasts exhibited characteristics of fibroblast progenitor cells. The trajectory of synovial fibroblast state transition showed a transition from mesenchymal to pro-inflammatory and lining phenotypes. In addition, the cross talk between fibroblast subclusters increased in degenerative shoulder diseases compared to acute trauma.

We successfully generated the scRNA-seq transcriptomic atlas of the shoulder synovium, which provides a comprehensive understanding of the heterogeneity of synovial fibroblasts, their potential to undergo state transitions, and their intercellular communication in the context of chronic degenerative and acute traumatic shoulder diseases.