Abstract
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 sequencing (scRNA-seq) data; however, existing computational methods are often not 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 sequencing. 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.