[论文解读] 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 transcriptomics被引用 33

一句话总结

CellPhoneDB v5 更新工具包，以从单细胞多组学推断细胞间通信，扩展配体-受体相互作用并新增多模态分析，以实现更好的优先级排序和可视化。

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.

研究动机与目标

• 通过扩展相互作用库约三分之一，以包含非蛋白质配体，例如内分泌激素和GPCR 配体。
• 实现基于差异表达的查询框架，以获得更定制化的相互作用推断。
• 结合其他单细胞模态（空间信息和转录因子活性）来利用新颖的优先排序方法。
• 提供 CellPhoneDBViz 以实现结果的交互式可视化和共享。

提出的方法

• 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.

实验结果

研究问题

• 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?

主要发现

• 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.