NicheNet

NicheNet predicts ligand–receptor interactions and downstream target gene regulation by integrating gene expression data with prior knowledge of signaling pathways and gene regulatory networks to elucidate intercellular communication.

Key Features:

• Integration of Expression Data: Utilizes gene expression profiles from interacting cells to predict ligand-target interactions that may drive changes in gene expression.
• Incorporation of Prior Knowledge: Combines expression data with established information on signaling pathways and gene regulatory networks to inform and prioritize predictions.
• Application to Complex Microenvironments: Infers active ligands and their downstream gene regulatory effects in complex contexts such as tumor microenvironments and immune cell interactions.

Scientific Applications:

• Tumor microenvironment analysis: Applied to identify ligands and downstream regulatory effects that mediate cell–cell communication within tumor microenvironments.
• Tissue niche and macrophage interactions: Used to study interactions involving Kupffer cells and other liver cell types (stellate cells, hepatocytes, endothelial cells), including cytokine-mediated (TNF, IL-1) signaling that activates transcription factors involved in Kupffer cell differentiation.

Methodology:

Maps potential ligand–receptor interactions by analyzing gene expression from source (ligand-expressing) and target cells and integrates these data with known signaling pathways and gene regulatory networks to predict active ligands and their effects on target gene regulation.