[Paper Review] CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data

Kevin Troulé, Robert Petryszak+7 more|arXiv (Cornell University)|Nov 8, 2023

Single-cell and spatial transcriptomics33 citations

TL;DR

CellPhoneDB v5 updates the toolkit to infer cell-cell communication from single-cell multiomics, expanding ligand-receptor interactions and adding multimodal analyses for better prioritization and visualization.

ABSTRACT

Cell-cell communication is essential for tissue development, regeneration and function, and its disruption can lead to diseases and developmental abnormalities. The revolution of single-cell genomics technologies offers unprecedented insights into cellular identities, opening new avenues to resolve the intricate cellular interactions present in tissue niches. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with a set of computational and statistical methods to integrate them with single-cell genomics data. Importantly, CellPhoneDB captures the multimeric nature of molecular complexes, thus representing cell-cell communication biology faithfully. Here we present CellPhoneDB v5, an updated version of the tool, which offers several new features. Firstly, the repository has been expanded by one-third with the addition of new interactions. These encompass interactions mediated by non-protein ligands such as endocrine hormones and GPCR ligands. Secondly, it includes a differentially expression-based methodology for more tailored interaction queries. Thirdly, it incorporates novel computational methods to prioritise specific cell-cell interactions, leveraging other single-cell modalities, such as spatial information or TF activities (i.e. CellSign module). Finally, we provide CellPhoneDBViz, a module to interactively visualise and share results amongst users. Altogether, CellPhoneDB v5 elevates the precision of cell-cell communication inference, ushering in new perspectives to comprehend tissue biology in both healthy and pathological states.

Motivation & Objective

• Expand the interaction repository by about one-third to include non-protein ligands such as endocrine hormones and GPCR ligands.
• Implement a differentially expressed-based query framework for more tailored interaction inference.
• Incorporate novel prioritization methods that leverage other single-cell modalities (spatial information and TF activities).
• Provide CellPhoneDBViz for interactive visualization and sharing of results.

Proposed method

• Curated repository of bona fide ligand-receptor interactions that accounts for multimeric protein complexes.
• Computational and statistical methods to integrate curated interactions with single-cell genomics data.
• Differential expression-based query approaches for targeted interaction discovery.
• Multimodal prioritization techniques using spatial data and transcription factor activities (CellSign module).
• Interactive visualization and sharing via the CellPhoneDBViz module.

Experimental results

Research questions

• RQ1 Can the expanded interaction repository improve accuracy and coverage of inferred cell-cell communication?
• RQ2 How does incorporating multiomics modalities (spatial info, TF activities) affect prioritization of cell-cell interactions?
• RQ3 Do differential expression-based queries yield more tailored and relevant interaction predictions?
• RQ4 How effective is the visualization module (CellPhoneDBViz) for exploring and sharing results?

Key findings

• The repository is expanded by about one-third to include interactions mediated by non-protein ligands such as endocrine hormones and GPCR ligands.
• A differentially expression-based methodology enables more tailored interaction queries.
• Novel computational methods prioritize specific cell-cell interactions using spatial information or transcription factor activities via the CellSign module.
• CellPhoneDBViz provides interactive visualization and sharing of results.
• Overall, the approach elevates the precision of cell-cell communication inference in tissue biology contexts.