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Author Spotlight: Integrating Single-Cell Transcriptomics with Organoid Cultures for Advanced Research and Therapeutic Insights

Author Spotlight: Integrating Single-Cell Transcriptomics with Organoid Cultures for Advanced Research and Therapeutic Insights

Published on: June 28, 2024

SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics.

Simon Cabello-Aguilar^1,2,3, Mélissa Alame^1,2,3,4, Fabien Kon-Sun-Tack^1,2,3

¹Institut de Recherche en Cancérologie de Montpellier, Inserm, F-34298 Montpellier, France.

Nucleic Acids Research

|March 21, 2020

Summary

This study introduces SingleCellSignalR, an R package for inferring ligand-receptor interactions from single-cell data. It offers a novel scoring method to confidently predict cellular communication networks.

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Area of Science:

Computational biology
Systems biology
Genomics

Background:

Single-cell transcriptomics enables the study of cellular networks.
Inferring ligand-receptor (LR) interactions is crucial for understanding intercellular communication.

Purpose of the Study:

To introduce a new, curated LR database and a novel regularized scoring method for LR interaction inference.
To assess the confidence in predicted LR interactions and compare the performance of the new scoring scheme against existing methods.
To provide an open-access R package, SingleCellSignalR, for accessible analysis of cellular communication.

Main Methods:

Development of a curated ligand-receptor database.
Implementation of a novel regularized scoring method for LR interaction inference.
Validation of the scoring method's performance in controlling false positives and outperforming other schemes.
Application of the SingleCellSignalR R package to analyze mouse epidermis data.

Main Results:

The novel regularized score demonstrates superior performance in predicting LR interactions compared to existing methods.
The SingleCellSignalR package provides a unique network view of intercellular interactions and relates receptors to intracellular pathways.
Analysis of mouse epidermis data revealed an oriented communication structure from external to basal layers.

Conclusions:

SingleCellSignalR offers a robust and accessible tool for inferring high-confidence ligand-receptor interactions from single-cell transcriptomic data.
The developed scoring method enhances the reliability of predicted cellular communication networks.
The findings highlight the utility of SingleCellSignalR in uncovering complex intercellular communication patterns, as demonstrated in the mouse epidermis example.