Intercellular Interactions’ Effect on Cell State Trajectories in Pulmonary Fibrosis

Pulmonary fibrosis is a chronic lung disease that is characterized by a progressive and irreversible destruction of lung architecture caused by accumulation of extracellular matrix and scarring. The healthy and diseased lung tissues consist of a wide variety of cell types with various cell states, from immune, stromal and epithelial lineages residing in close proximity, that communicate via physical interactions, exchange of metabolites and ligand–receptor signalling. Cell state, including its inflammatory and metabolic molecular programs, is a consequence of complexed intercellular signalling networks induced within and between immune and non-immune lineages.

A massive single-cell RNA-sequencing (scRNA-seq) data was generated in order to molecularly characterize the heterogeneity of cell states in the healthy and diseased lungs. In our research, we aim to develop a computational method that integrates scRNA-seq data of fibrotic and healthy lungs, and calculate the cell-cell interactions that are associated with unique trajectories of intracellular cell states. Our algorithm will map the whole-lung tissue intercellular signalling networks and use a computational deconvolution approach to characterize combinations of ligand-receptor interactions that are associated with the heterogeneity of cell states within each cell type. By this methodology, we will identify intercellular crosstalks that likely have an effect on specific intracellular cell states within a particular cell type in the pulmonary fibrotic lungs. The results of our algorithm’s application and the thorough analysis on lung scRNA-seq data are expected to provide novel insights regarding the molecular signalling driving unique and rare cell states in pulmonary fibrosis, and other lung pathologies.