Lignature provides a curated resource of ligand induced transcriptomic signatures for signaling inference

Abstract

Ligand-receptor interactions mediate intercellular communication, inducing transcriptional changes that regulate physiological and pathological processes. Ligand-induced transcriptomic signatures can be used to infer ligand activity; however, the absence of a comprehensive set of ligand-response signatures has limited their practical application in predicting ligand-receptor interactions. To bridge this gap, we develop Lignature, a curated database encompassing intracellular transcriptomic signatures for 362 human ligands, significantly expanding the repertoire of ligands with available intracellular response signatures such as CytoSig and ImmuneDictionary. Lignature compiles signatures from published transcriptomic datasets, generating both gene- and pathway-based signatures for each ligand. We apply Lignature to prioritize ligand-associated transcriptional activity in controlled in vitro experiments and real-world single-cell sequencing datasets. Across these settings, Lignature consistently improves the prioritization of experimentally supported ligands compared with existing approaches. We additionally develop a regression-based framework to model combinatorial regulation by multiple ligands. These results establish Lignature as a robust platform for ligand signaling inference, providing a powerful tool to explore ligand-receptor interactions across diverse experimental and physiological contexts.