Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis

Understanding how cells communicate remains a central challenge in biology. While ligand-receptor interactions are known to mediate cell-cell signaling, prior studies have lacked a centralized, manually curated database to explore these interactions systematically. Existing resources often lack the specificity or integration of functional annotations needed to decode intercellular communication networks. This gap motivated the development of a platform that could consolidate known ligand-receptor pairs and provide tools for analyzing their roles in cell-cell communication. Single-cell RNA sequencing has enabled the detection of cell-type-specific expression patterns, but interpreting these data requires robust interaction databases. Prior work has identified ligand-receptor pairs from literature, but these datasets are often fragmented or lack integration with computational tools. The absence of a unified platform for querying and visualizing these interactions has limited progress in this field. Researchers have proposed various methods to infer intercellular communication, but these approaches often rely on incomplete or uncurated interaction data. This uncertainty has driven the need for a high-confidence database that can support both basic research and algorithm development in single-cell studies. The lack of a comprehensive, well-organized resource has hindered the ability to study the functional effects of cell-cell communication. Thus, a curated platform with integration of small molecule interactions and CoV-related data could significantly advance the field.

The aim of this study was to create a manually curated database of ligand-receptor interactions to support research on intercellular communication. The researchers sought to provide a centralized platform that includes both protein-protein and small molecule interactions. They intended to integrate this database with a web-based tool for analyzing scRNA-seq data. The study aimed to address the lack of a comprehensive resource for studying cell-cell communication networks. The researchers also wanted to include interactions relevant to coronavirus infections, particularly SARS-CoV-2. The goal was to develop a platform that could facilitate both experimental and computational studies in this area. The team aimed to ensure the database is user-friendly and accessible for querying and visualization. They also intended to make the resource publicly available to promote further research and algorithm development in single-cell genomics.

The researchers compiled a database of literature-supported ligand-receptor interactions. They manually curated interactions from published studies to ensure accuracy and reliability. The database includes both human and mouse ligand-receptor pairs. The team also included small molecule-related interactions in the dataset. They developed a webserver to allow users to query and visualize these interactions. The platform integrates with single-cell RNA sequencing data for analysis of intercellular communication. The researchers tested the database with known interactions involving coronavirus proteins. They ensured the platform is accessible and user-friendly for researchers in the field. The methods involved manual curation, integration of multiple data sources, and development of a web-based interface.

The database includes over 3,700 human and 3,200 mouse ligand-receptor interactions. More than 400 small molecule-related interactions were also included in the dataset. The platform provides a web-based interface for querying and visualizing these interactions. Over 16 ligand-receptor pairs related to coronavirus-human interactions were documented. Twelve of these interactions are specific to SARS-CoV-2 infection. The database is publicly accessible at http://www.rna-society.org/cellinker/. The researchers demonstrated the platform's utility in analyzing scRNA-seq data. The results show that Cellinker supports both basic research and algorithm development in single-cell studies.

The authors propose that Cellinker provides a valuable resource for studying intercellular communication. They suggest that the platform could promote research in this area and support the development of new algorithms for scRNA-seq analysis. The database includes a large number of manually curated interactions, which the authors believe enhances its reliability. The inclusion of coronavirus-related interactions is highlighted as a key feature of the platform. The researchers emphasize the user-friendly interface as a benefit for researchers. They suggest that the database could facilitate both experimental and computational studies. The authors believe the availability of this platform will advance the field of cell-cell communication research. They conclude that Cellinker is a practical and convenient tool for decoding intercellular communication networks.