Identifying the heterogeneity of pulmonary mesenchymal cell lineages via single cell RNA-seq analysis
M. Guoa, Lipps, Dakotaa,c, J.A. Whitsetta and Y. Xua,b*
aDivision of Neonatology, Perinatal and Pulmonary Biology, bDepartment of Biomedical Informatics, Cincinnati Children's Hospital Medical Center; cUniversity of Cincinnati, College of Engineering and Applied Science
Rationale: Understanding the lineage and differentiation state of each cell is fundamentally important for the ultimate delineation of organ formation and function. While lineage relationships among epithelial cells are becoming increasingly understood in the developing mouse lung, the identities and lineage relationships among mesenchymal cells remain poorly defined. The present study sought to develop and utilize single cell approach to identify ontogenetic changes in mesenchymal subtypes during fetal lung maturation.Methods: We developed SLICE, an algorithm utilizing single-cell RNA-seq (scRNA-seq) to quantitatively measure cellular differentiation states based on single cell entropy and predict cell differentiation lineages via the construction of entropy directed cell trajectories (Guo et al. 2017, Nucleic Acids Res). We applied SLICE to scRNA-seq from mouse lung at embryonic day 16.5, 18.5 and postnatal day 1 to identify lung mesenchymal cell types and to reveal the lineage relationships among lung mesenchymal cell sub types. Results:scRNA-seq analysis identified a diversity of mesenchymal cell types. A two-branched differentiation pathway of fibroblastic subtypes was predicted using SLICE. Temporal patterns and key regulators of each cell type were identified for both branches. Conclusion: Data provide a working model defining the lineages of major pulmonary mesenchymal cells during lung development and provide expression signatures to benchmark with fibroblast subtypes associated with lung repair and pathology.
Supported by CCHMC GAP fund and U01HL110964-Lungmap