Tracing cell communication programs across conditions at single cell resolution with CCC-RISE

Abstract

Cell-cell communication (CCC) mediates coordinated cellular activities that vary dynamically across time, location, and biological context. While various tools exist to infer CCC, they typically aggregate data according to pre-defined cell types, obscuring critical single-cell heterogeneity. Furthermore, because signaling pathways and cell populations operate in a coordinated manner, an integrative analytical approach is essential. To address these challenges, we developed CCC-RISE, an extension of the tensor-based method Reduction and Insight in Single-cell Exploration (RISE). CCC-RISE identifies integrative patterns of single-cell variation by deconvolving communication into interpretable modules defined by unique sender cells, receiver cells, ligands, and condition associations. We applied this framework to a COVID-19 cohort with varying disease severity and a lung transplant cohort with acute allograft dysfunction. In both contexts, CCC-RISE successfully identified disease-relevant communication programs and traced them to specific cellular subpopulations, often crossing conventional cell-type boundaries. This approach offers a robust pipeline enabling the identification of disease-relevant signaling subpopulations that are invisible to aggregate methods. HighlightsO_LICCC-RISE enables integrative analysis of cell-cell communication across multiple conditions at single-cell resolution C_LIO_LICCC-RISE deconvolves signaling patterns into modules defined by their sender cells, receiver cells, LR pairs, and experimental conditions/samples C_LIO_LIAnalysis at single-cell resolution uncovers signaling activity within and across conventional cell types C_LI