Fibroblast heterogeneity has long been recognized in mouse and human lungs, homeostasis, and disease states. However, there is no common consensus on fibroblast subtypes, lineages, biological properties, signaling, and plasticity, which severely hampers our understanding of the mechanisms of fibrosis. To comprehensively classify fibroblast populations in the lung using an unbiased approach, single-cell RNA sequencing was performed with mesenchymal preparations from either uninjured or bleomycin-treated mouse lungs. Single-cell transcriptome analyses classified and defined six mesenchymal cell types in normal lung and seven in fibrotic lung. Furthermore, delineation of their differentiation trajectory was achieved by a machine learning method. This collection of single-cell transcriptomes and the distinct classification of fibroblast subsets provide a new resource for understanding the fibroblast landscape and the roles of fibroblasts in fibrotic diseases. Display omitted •Distinct MC subtypes were defined by single-cell transcriptome analysis•Lipofibroblasts were identified•Fibrotic Pdgfrb high MC subtype emerges post-injury•Integrative analysis of MC trajectories was constructed by machine learning Xie et al. have analyzed mesenchymal cell subpopulations at single-cell resolution and have demonstrated known subtypes and a newly emerging subtype during pulmonary fibrosis in mouse lung.