Charting spatial ligand-target activity using Renoir

Abstract

The advancement of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics has made it possible to infer interactions amongst heterogeneous cells and their surrounding cellular environments. Existing methods assist in the analysis of ligand-receptor interactions by either adding spatial information to the currently available scRNA-seq data or utilizing spot-level or high-resolution spatial transcriptomics data. However, till date, there is a lack of methods capable of mapping ligand-target interactions across a spatial topology with specific cell type composition, with the potential to shed further light on the niche-specific relationship between ligands and their downstream targets. Here we present Renoir for charting the ligand-target activities across a spatial topology and delineating spatial communication niches harboring specific ligand-target activities and cell type composition. Renoir can also spatially map pathway-level aggregate activity of ligand-target gene sets and identify domain-specific activities between ligands and targets. We applied Renoir to three spatial datasets ranging from development to disease to demonstrate its effectiveness in inferring cellular niches with distinct ligand-target interactions, spatially mapping hallmark pathway activities, ranking ligand activity across spatial niches, and visualising overall cell type-specific, ligand-target interactions in spatial niches.

LIANA+: an all-in-one cell-cell communication framework

“…Typically, these methods infer relationships between proteins 12,13 or cell types (cellular neighbourhoods) 14,15 . Spatially-informed methods differ in the scale at which interactions are inferred, as some infer relationships globally, summarising them across slides as a whole 12,13,16 , while others do so locally at the individual cell or spot locations within a slide [17][18][19][20] .…”