Decipher: a computational pipeline for context-specific cell-signalling analysis from single-cell profiles

Single-cell profiling has transformed our understanding of the cellular and molecular states underpinning disease. However, current computational tools struggle to connect cell-cell communication with downstream transcriptional responses while retaining context specificity and the capacity to identify novel signalling relationships. We present Decipher, a computational pipeline that builds and scores integrated cell-signalling networks from single-cell profiles in a context-specific, data-driven manner. Benchmarking showed Decipher recovers known cytokine signalling pathways while maintaining the flexibility to detect novel pathways and context-specific effects. We utilized Decipher to characterize the signals associated with an inflammatory monocyte population enriched for interferon-stimulated genes and markedly increased in frequency after secondary Pfizer-BioNTech COVID-19 vaccination. Finally, we employed Decipher to interrogate transcription factor activity profiles in mild versus severe COVID-19, finding that progression to severe disease was associated with a loss of interferon signalling transcription factors (IRF7, IRF9, STAT1, STAT2) and a gain of factors associated with inflammation and cellular stress responses (NFKB2, HIF1A, ATF3, ATF4). These results show that Decipher can decode signalling pathways and report on ligand-receptor mediated transcription factor-target gene networks underlying processes in homeostasis, disease, and cellular responses to therapies. We present Decipher as a tool for generating ranked, testable hypotheses about cell-cell communication to support early-stage prioritization of therapeutic targets.