Cell-Cell Communication Networks in Tissue: Toward Quantitatively Linking Structure with Function.

Area of Science:

• Cellular and Molecular Biology
• Computational Biology
• Systems Biology

Background:

• Multiplexed tissue imaging, intravital microscopy, and single-cell RNA sequencing (scRNAseq) provide detailed molecular data on mammalian tissues.
• Current challenges lie in translating this data into a dynamic understanding of cell-cell communication networks.
• Integrating diverse experimental data for network-level analysis is complex.

Purpose of the Study:

• To review progress in understanding cell-cell communication networks within tissues.
• To highlight the value of computational models for network-level functions.
• To identify future directions for integrating experimental and computational approaches.

Main Methods:

• Review of recent advancements in studying cell-cell communication in tissues.
• Analysis of ligand-receptor interactions in the tumor microenvironment.
• Discussion of computational modeling strategies for network analysis.

Main Results:

• Ligand-receptor interactions play key roles in immunosuppressive and mitogenic signaling within the tumor microenvironment.
• Computational models offer a framework for understanding niche-dependent actions, homeostasis, and multi-scale coordination.
• Progress has been made in dissecting signaling pathways using advanced imaging and sequencing techniques.

Conclusions:

• Computational models are essential for a comprehensive understanding of dynamic cell-cell communication networks.
• Bridging high-level statistical approaches with low-level physicochemical mechanisms presents a significant future research direction.
• Further integration of experimental data through modeling will advance the study of tissue biology.