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Charting spatial ligand-target activity using Renoir
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DOI：10.1038/s41467-026-72388-7.png)
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Abstract
En 中文
The advancement of single-cell RNA sequencing and spatial transcriptomics has enabled the inference of cellular interactions in a tissue microenvironment. Despite advances in cell-cell interaction inference, methods capable of mapping the influence of ligands on downstream target genes across spatial niches harboring specific cell type composition, crucial for resolving niche-specific relationship between ligands and their downstream targets are still lacking. Here, we present Renoir for charting the ligand-target activities across a spatial topology, delineating spatial communication niches harboring specific ligand-target activities and spatially mapping pathway-level activity of genesets. Across spatial datasets with varying resolution (spot to single-cell) ranging from development to disease, Renoir infers cellular niches with distinct ligand-target interactions, spatially maps pathway activities, and identifies context-specific cell-cell interactions, including hepatocyte-macrophage interactions in fetal liver and interactions between onco-fetal and bipotent cells in hepatocellular carcinoma. Renoir uncovers biological insights and therapeutically-relevant cellular crosstalk from spatial transcriptomics data. Methods that can map the influence of ligands on target genes across spatial niches are still necessary to improve our understanding of tissue microenvironments. Here, the authors develop Renoir, a method to infer ligand-target activities and map them to spatial niches in spatial transcriptomics data, with applications in fetal liver and hepatocellular carcinoma cells.
Keywords:
Renoir
spatial transcriptomics
ligand-target interactions
cellular niches
pathway activity
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