Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Coordinated cell-cell communication is vital for tissue development and homeostasis.
• Single-cell transcriptomics (scRNA-seq) offers high-resolution insights into cellular heterogeneity.
• Loss of spatial information in scRNA-seq limits understanding of localized cell signaling.

Purpose of the Study:

• To review computational tools for inferring cell-cell communication using single-cell and spatial transcriptomics.
• To highlight biological insights gained from these tools in skin biology.
• To discuss future research directions in intercellular communication analysis.

Main Methods:

• Analysis of gene expression data from single-cell transcriptomics.
• Integration of spatial information using spatial transcriptomics.
• Application of computational tools for cell-cell communication inference.

Main Results:

• Emergent computational tools leverage spatial transcriptomics for more accurate cell-cell communication inference.
• These approaches have uncovered novel intercellular communication mechanisms in various tissues, including the skin.
• Application to skin development, homeostasis, disease, and aging provides significant biological insights.

Conclusions:

• Integrating spatial information is critical for understanding tissue organization and function through cell-cell communication.
• Computational tools are advancing the systematic exploration of intercellular signaling.
• The skin serves as a valuable model for studying complex cellular interactions and communication pathways.