Mining cell-cell signaling in single-cell transcriptomics atlases.

Area of Science:

• * Molecular Biology
• * Genomics
• * Bioinformatics

Background:

• * Single-cell RNA sequencing (scRNA-seq) has generated vast datasets (atlases) across various biological systems.
• * These atlases contain implicit information about intercellular communication, vital for understanding multicellular organisms.
• * Extracting this signaling information is key to deciphering complex biological processes.

Purpose of the Study:

• * To review current computational strategies for identifying cell-cell signaling events from scRNA-seq data.
• * To highlight successful applications of these methods in discovering novel biological functions.
• * To provide a roadmap for future research in single-cell signaling analysis.

Main Methods:

• * Systematic literature review of computational tools and algorithms for cell-cell communication inference from scRNA-seq.
• * Analysis of case studies demonstrating the application of these methods.
• * Discussion of the strengths and limitations of existing approaches.

Main Results:

• * Multiple computational frameworks exist for predicting ligand-receptor interactions between cells using scRNA-seq data.
• * These methods have successfully identified known and novel signaling pathways in various tissues and disease states.
• * Functional validation of predicted signals often leads to new discoveries in developmental biology, immunology, and cancer research.

Conclusions:

• * Mining cell-cell signaling from scRNA-seq atlases is a powerful approach to understanding multicellular functions.
• * Continued development of robust computational methods and experimental validation is crucial.
• * This field holds significant promise for advancing biological discovery and therapeutic development.