MetaLigand: A database for predicting non-peptide ligand mediated cell-cell communication

Abstract

Non-peptide ligands (NPLs), including lipids, amino acids, carbohydrates, and non-peptide neurotransmitters and hormones, play a critical role in ligand-receptor-mediated cell-cell communication, driving diverse physiological and pathological processes. To facilitate the study of NPL-dependent intercellular interactions, we introduce MetaLigand, an R-based and web-accessible tool designed to infer NPL production and predict NPL-receptor interactions using transcriptomic data. MetaLigand compiles data for 233 NPLs, including their biosynthetic enzymes, transporter genes, and receptor genes, through a combination of automated pipelines and manual curation from comprehensive databases. The tool integrates both de novo and salvage synthesis pathways, incorporating multiple biosynthetic steps and transport mechanisms to improve prediction accuracy. Comparisons with existing tools demonstrate MetaLigands superior ability to account for complex biogenesis pathways and model NPL abundance across diverse tissues and cell types. Furthermore, analysis of single-nucleus RNA-seq datasets from age-related macular degeneration samples revealed that distinct retinal cell types exhibit unique NPL profiles and participate in specific NPL-mediated pathological cell-cell interactions. Finally, MetaLigand supports single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data, enabling the visualization of predicted NPL production levels and heterogeneity at single-cell resolution.