Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

• Published in: Nucleic acids research Vol. 51; no. 10; pp. 4726 - 4744
• Main Authors: Villemin, Jean-Philippe, Bassaganyas, Laia, Pourquier, Didier, Boissière, Florence, Cabello-Aguilar, Simon, Crapez, Evelyne, Tanos, Rita, Cornillot, Emmanuel, Turtoi, Andrei, Colinge, Jacques
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 09-06-2023
• Subjects: Biochemistry, Molecular Biology; Computational Biology; Genomics; Life Sciences
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkad352

Abstract
The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.
Graphical Abstract
Graphical Abstract
BulkSignalR is a user-friendly R library that implements statistical models and specific algorithms to infer ligand-receptor interactions from bulk or spatial transcriptomic data. It is freely available.