Fibroblasts are a dynamic cell type that achieve selective differentiated states to mediate acute wound healing and long-term tissue remodeling with scarring. With myocardial infarction injury, cardiomyocytes are replaced by secreted extracellular matrix proteins produced by proliferating and differentiating fibroblasts. Here, we employed 3 different mouse lineage-tracing models and stage-specific gene profiling to phenotypically analyze and classify resident cardiac fibroblast dynamics during myocardial infarction injury and stable scar formation. Fibroblasts were activated and highly proliferative, reaching a maximum rate within 2 to 4 days after infarction injury, at which point they expanded 3.5-fold and were maintained long term. By 3 to 7 days, these cells differentiated into myofibroblasts that secreted abundant extracellular matrix proteins and expressed smooth muscle α-actin to structurally support the necrotic area. By 7 to 10 days, myofibroblasts lost proliferative ability and smooth muscle α-actin expression as the collagen-containing extracellular matrix and scar fully matured. However, these same lineage-traced initial fibroblasts persisted within the scar, achieving a new molecular and stable differentiated state referred to as a matrifibrocyte, which was also observed in the scars of human hearts. These cells express common and unique extracellular matrix and tendon genes that are more specialized to support the mature scar.

Inhibiting FN polymerization or cardiac fibroblast gene expression attenuates pathological properties of MFs in vitro and ameliorates adverse cardiac remodeling and fibrosis in an in vivo model of heart failure. Interfering with FN polymerization may be a new therapeutic strategy for treating cardiac fibrosis and heart failure.

Highlights d DoubletDecon uses deconvolution to identify and remove doublets in scRNA-seq data d Retention of doublets can confound data analysis and cell population identification d DoubletDecon limits erroneous removal of transitional and progenitor cells d The algorithm identifies unique doublets relative to alternative approaches