Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
YDEVS Software Development, Valencia, Spain.
Nat Protoc. 2020 Apr;15(4):1484-1506. doi: 10.1038/s41596-020-0292-x. Epub 2020 Feb 26.
Cell-cell communication mediated by ligand-receptor complexes is critical to coordinating diverse biological processes, such as development, differentiation and inflammation. To investigate how the context-dependent crosstalk of different cell types enables physiological processes to proceed, we developed CellPhoneDB, a novel repository of ligands, receptors and their interactions. In contrast to other repositories, our database takes into account the subunit architecture of both ligands and receptors, representing heteromeric complexes accurately. We integrated our resource with a statistical framework that predicts enriched cellular interactions between two cell types from single-cell transcriptomics data. Here, we outline the structure and content of our repository, provide procedures for inferring cell-cell communication networks from single-cell RNA sequencing data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v.2.0 is an updated version of our resource that incorporates additional functionalities to enable users to introduce new interacting molecules and reduces the time and resources needed to interrogate large datasets. CellPhoneDB v.2.0 is publicly available, both as code and as a user-friendly web interface; it can be used by both experts and researchers with little experience in computational genomics. In our protocol, we demonstrate how to evaluate meaningful biological interactions with CellPhoneDB v.2.0 using published datasets. This protocol typically takes ~2 h to complete, from installation to statistical analysis and visualization, for a dataset of ~10 GB, 10,000 cells and 19 cell types, and using five threads.
细胞间通讯由配体-受体复合物介导,对于协调多种生物学过程至关重要,如发育、分化和炎症。为了研究不同细胞类型的上下文相关串扰如何使生理过程得以进行,我们开发了 CellPhoneDB,这是一个新型的配体、受体及其相互作用的数据库。与其他数据库不同,我们的数据库考虑了配体和受体的亚基结构,准确地表示异源复合物。我们将我们的资源与一个统计框架相结合,该框架可以根据单细胞转录组学数据预测两种细胞类型之间丰富的细胞相互作用。在这里,我们概述了我们的存储库的结构和内容,提供了从单细胞 RNA 测序数据推断细胞间通讯网络的过程,并提供了一个实用的逐步指南,以帮助实施该方案。CellPhoneDB v.2.0 是我们资源的更新版本,它增加了额外的功能,使用户能够引入新的相互作用分子,并减少了查询大型数据集所需的时间和资源。CellPhoneDB v.2.0 可供代码和用户友好的网络界面使用,既可供计算基因组学方面的专家使用,也可供经验较少的研究人员使用。在我们的方案中,我们展示了如何使用发布的数据集评估 CellPhoneDB v.2.0 中的有意义的生物学相互作用。对于一个包含约 10GB、10000 个细胞和 19 种细胞类型的数据集,使用五个线程,从安装到统计分析和可视化,这个方案通常需要大约 2 小时才能完成。