Decipher: A computational pipeline to extract context-specific mechanistic insights from single-cell profiles

Abstract

AbstractThe advent of single-cell profiling technologies has revolutionized our understanding of the cellular and molecular states that underpin disease states and response to therapies. However, current computational tools cannot recover both known and novel mechanistic insights at distinct layers of biological regulation in an unbiased manner. Here, we presentDecipher, a novel computational pipeline that builds integrated cell signalling networks from single-cell profiles in a context-specific, data-driven manner and identifies the key cellular and molecular events that drive disease. We benchmarkedDecipheragainst existing tools and found it could recover known, experimentally determined cytokine signalling pathways with high accuracy, whilst maintaining the flexibility to detect novel pathways and context-specific effects. Notably,Decipherreports a manageable set of interactions that can be aggregated into global cell-to-cell signalling maps that are interpretable. In addition, we utilisedDecipherto unveil the cellular and molecular mechanisms driving a novel population of inflammatory monocytes enriched with interferon stimulated genes that is markedly increased in frequency following secondary immunization with the Pfizer-BioNTech COVID-19 mRNA vaccine. Taken together, our findings demonstrate thatDeciphercan decode signalling pathways and report on ligand-receptor mediated transcription factor – target gene networks that underlie processes in homeostasis and disease, as well as cellular responses to therapies. We presentDecipheras an invaluable new tool for the discovery of novel therapeutic targets and the development of new medicines.