DiSiR: a software framework to identify ligand-receptor interactions at subunit level from single-cell RNA-sequencing data

Abstract

Most of cell-cell interactions and crosstalks are mediated by ligand-receptor interactions. The advent of single-cell RNA-sequencing (scRNA-seq) techniques has enabled characterizing tissue heterogeneity at single-cell level. Over the past recent years, several methods have been developed to study ligand-receptor interactions at cell type level using scRNA-seq data. However, there is still no easy way to query the activity of a specific user-defined signaling pathway in a targeted way or map the interactions of the same subunit with different ligands as part of different receptor complexes. Here, we present DiSiR, a fast and easy-to-use permutation-based software framework to investigate how individual cells are interacting with each other by analyzing signaling pathways of multi-subunit ligand-activated receptors from scRNA-seq data, not only for available curated databases of ligand-receptor interactions, but also for interactions that are not listed in these databases. We show that, when utilized to infer melanoma disease map on a gold-standard dataset, DiSiR outperforms other well-known permutation-based methods, e.g., CellPhoneDB and ICELLNET. To demonstrate DiSiRs utility in exploring data and generating biologically relevant hypotheses, we apply it to COVID lung and rheumatoid arthritis (RA) synovium scRNA-seq data and highlight potential differences between inflammatory pathways at cell type level for control vs. disease samples.