Single Cell RNA Sequencing Meta-Analysis of Mouse Tendon Cells

Introduction: Tendons, which connect muscle to bone, often undergo chronic overuse or acute tear injuries. After injury, low vascularity and cellularity lead to compromised mechanical properties and fibrosis in the healing tendon. Despite the vast clinical burden, the specific cells responsible for tendon fibrosis are not well understood. We conducted a comprehensive single-cell transcriptomic meta-analysis from publicly available studies to characterize the functional relevance of tenocyte and fibroblast cell types in tendon injury.

Methods: We analyzed single cell RNA sequencing (scRNA-seq) datasets of all publicly available mouse tendon samples. Standard quality control metrics were applied, and R package Seurat version 5 was used for analysis. To integrate and analyze these datasets, Seurat, Harmony, scVI, and FastMNN were used. After integration, transcriptionally defined clusters were identified, and cells were labelled. Cells identified as fibroblasts or tenocytes were analyzed for subclusters, and plots were split to separately visualize injured and uninjured conditions.

Results: Among the 41 datasets screened, 9 mouse studies met the necessary inclusion criteria. 137,889 cells were analyzed and sorted into 15 transcriptionally defined clusters. Subsequently, 47,673 cells were identified as fibroblasts or tenocytes and were subclustered, resulting in 4 clusters. Notably, the cell subpopulation containing fibroblasts increased in injured tendon samples when compared to uninjured tendons.

Conclusion: These data illustrate that it is computationally possible to distinguish fibroblasts and tenocytes. In the future, this data can be combined with tendon fibrosis data to identify therapeutic targets for tendon regeneration.