Quantitative profiling of lifespan-dependent cell-cell communication potential reveals dynamic ligand-receptor...

Cell-to-cell communication (CCC) is a tightly regulated process essential for tissue development and homeostasis, but can become dysregulated during ageing. While CCC is inherently complex and remains incompletely characterised, advances in single-cell RNA sequencing (scRNA-seq) have enabled large-scale, unbiased inference of intercellular interactions which offers broad-spectrum information that complements traditional protein-based assays. Unlike these targeted assays, transcriptomic approaches enable systematic inference and exploration of both known and potentially novel ligand-receptor (LR) interactions. In this study, we applied LIgand-receptor ANalysis frAmework (LIANA), which integrates multiple inference methods to derive consensus CCC predictions, to scRNA-seq data for four mouse organs (liver, lung, heart, and kidney), spanning key life stages: post-natal development, adulthood and ageing. Our analysis revealed dynamic, organ-specific CCC patterns characterised by both gains and losses of LR interactions over time, reflecting lifespan-dependent shifts in transcriptome-inferred intercellular communication potential. To quantify these shifts, we developed a two-phase comparative framework and introduced the Shrink and Expand (SE) score to capture directional changes in inferred LR interaction sets between any two biological states. Applying this framework generated a curated dataset of LR pairs and their predicted changes, capturing the repertoire of putative interactions across organs and states and enabling robust, interpretable comparisons of organ-specific and coinciding patterns of change across multiple organs. For instance, CD44 and ITGB1 were found to undergo highly dynamic changes across timepoints and organs, suggesting that they may act as central nodes in predicted age-dependent communication changes. This generalisable approach supports quantitative comparisons of inferred CCC across diverse states, including development, ageing, disease, or treatment conditions, and provides a resource for prioritising candidate interactions for drug target discovery for further experimental validation while exploring context-specific shifts in predicted intercellular communication.