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

Area of Science:

• Biochemistry
• Genomics
• Computational Biology

Background:

• Non-peptide ligands (NPLs) are crucial for cell-cell communication in physiological and pathological contexts.
• Understanding NPL-mediated interactions is vital for studying diverse biological processes.
• Existing tools lack comprehensive analysis of NPL biogenesis and abundance.

Purpose of the Study:

• To develop MetaLigand, an R-based, web-accessible tool for inferring NPL production and predicting NPL-receptor interactions.
• To provide a comprehensive resource for studying NPL-dependent intercellular communication using transcriptomic data.
• To enhance the accuracy of NPL abundance modeling across different tissues and cell types.

Main Methods:

• Compiled data for 233 NPLs, including biosynthetic enzymes, transporters, and receptors.
• Integrated de novo and salvage synthesis pathways with multiple biosynthetic and transport steps.
• Validated MetaLigand's performance against existing tools using transcriptomic data.
• Analyzed single-nucleus RNA sequencing data from age-related macular degeneration samples.

Main Results:

• MetaLigand demonstrates superior accuracy in modeling NPL abundance and accounting for complex biogenesis pathways.
• Identified unique NPL profiles and specific pathological cell-cell interactions in retinal cell types.
• Successfully inferred NPL production and receptor interactions from transcriptomic data.
• Revealed distinct NPL-mediated interactions in age-related macular degeneration.

Conclusions:

• MetaLigand is a powerful tool for dissecting NPL-mediated intercellular communication.
• The tool supports single-cell RNA sequencing and spatial transcriptomics for high-resolution analysis.
• MetaLigand facilitates the discovery of novel NPL-receptor interactions and their roles in disease.