Dysregulated ligand-receptor interactions from single-cell transcriptomics

Motivation: Intracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand-receptor interactions. Although computational methods have been developed to infer cell type-specific ligand-receptor interactions from one single-cell transcriptomics profile, there is lack of approaches considering ligand and receptor simultaneously to identifying dysregulated interactions across conditions from multiple single-cell profiles. Results: We developed scLR, a statistical method for examining dysregulated ligand-receptor interactions between two conditions. scLR models the distribution of the product of ligands and receptors expressions and accounts for inter-sample variances and small sample sizes. scLR achieved high sensitivity and specificity in simulation studies. scLR revealed important cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection, and activated TGF-beta signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis.

Automated summary

This study developed a statistical method for examining dysregulated ligand-receptor interactions in different conditions, and successfully applied it to the study of important diseases such as COVID-19 infection and pulmonary fibrosis.