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.

本研究旨在以单细胞分辨率探究滑膜成纤维细胞的异质性及其细胞状态转变潜能。我们采用单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）技术，全面绘制慢性肩袖撕裂（RCTs）与急性肱骨近端骨折（PHFs）患者肩部滑膜的细胞图谱。通过无偏聚类分析，我们成功在滑膜微环境中鉴定出不同的成纤维细胞亚群。我们利用Monocle 3解析了滑膜成纤维细胞的状态转变轨迹，并借助CellPhone DB v2.1.0预测了滑膜微环境内的细胞间通讯模式。本研究在肩部滑膜中共鉴定出8个主要细胞簇。无偏聚类分析进一步识别出4个滑膜成纤维细胞亚群，这些亚群分别与蛋白质分泌、细胞外基质（ECM）重塑、炎症调控及细胞分裂等多样生物学功能相关。所鉴定的成纤维细胞亚群包括衬层成纤维细胞、间充质成纤维细胞、促炎成纤维细胞与增殖性成纤维细胞。结合StemID分析结果与特征基因表达特征，间充质成纤维细胞表现出成纤维细胞祖细胞的特性。滑膜成纤维细胞的状态转变轨迹呈现为从间充质表型向促炎表型与衬层表型的转变过程。此外，与急性创伤病例相比，退行性肩部疾病患者的成纤维细胞亚群间的通讯交流更为活跃。本研究成功构建了肩部滑膜的scRNA-seq转录组图谱，为理解慢性退行性与急性创伤性肩部疾病背景下滑膜成纤维细胞的异质性、细胞状态转变潜能及细胞间通讯机制提供了全面的研究基础。