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<p>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.</p>

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Charting spatial ligand-target activity using Renoir

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Narein Rao,
  • Tanush Kumar,
  • Dina Kazemi,
  • Shaozhi Hou,
  • Atefeh Khakpoor,
  • Merrin Mary Eapen,
  • Rhea Pai,
  • Liang Qiao,
  • Archita Mishra,
  • Florent Ginhoux,
  • Jerry Kok Yen Chan,
  • Jacob George,
  • Ankur Sharma,
  • Hamim Zafar

摘要

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.