LB1021 Single-cell dynamics of fibroblast states during skin wound healing in mice

Wound healing is a highly dynamic process over spatial and temporal scales. Key to wound outcomes are fibroblasts, whose precise functions in tissue repair only partially understood. We constructed an atlas of mouse skin using several published single-cell RNA-seq (scRNA-seq) datasets. Using scRNA-seq data integration across time, we identified key cell types states that both shared as well unique specific post-wounding timepoints. reveal fibroblasts dramatically alter after wounding compared unwounded also remain time healing. These transcriptional dynamics not paralleled other major wound-resident types. Furthermore, by “supervised clustering” training-interpretable machine learning models infer these state largely driven selected gene categories, essential for fibroblasts. Finally, RNA velocity pseudotime analysis suggest fibroblast during small abridged version large dynamics. Taken together, our findings transcriptomic landscape