Quantitative single-cell interactomes in normal and virus-infected mouse lungs

Mammalian organs consist of diverse, intermixed cell types that signal to each other via ligand-receptor interactions - an interactome - to ensure development, homeostasis, and injury-repair. Dissecting such intercellular interactions is facilitated by rapidly growing single-cell RNA-seq (scRNA-seq) data; however, existing computational methods are often not sufficiently quantitative nor readily adaptable by bench scientists without advanced programming skills. Here we describe a quantitative intuitive algorithm, coupled with an optimized experimental protocol, to construct and compare interactomes in control and Sendai virus-infected mouse lungs. A minimum of 90 cells per cell type compensates for the known gene dropout issue in scRNA-seq and achieves comparable sensitivity to bulk RNA-seq. Cell lineage normalization after cell sorting allows cost-efficient representation of cell types of interest. A numeric representation of ligand-receptor interactions identifies, as outliers, known and potentially new interactions as well as changes upon viral infection. Our experimental and computational approaches can be generalized to other organs and human samples. Summary statementAn intuitive method to construct quantitative ligand-receptor interactomes using single-cell RNA-seq data and its application to normal and Sendai virus-infected mouse lungs.