Cell–cell communication networks in tissue: Toward quantitatively linking structure with function. Issue 27 (September 2021)

• Record Type: Journal Article
• Authors: Luthria, Gaurav; Lauffenburger, Douglas; Miller, Miles A.
• Is Part Of: Current opinion in systems biology. Issue 27(2021)
• Publication Date: 2021-09
• Subjects: Growth factor/cytokine/chemokine -- Receptor tyrosine kinase -- Multiplexed tissue imaging -- Spatial network inference -- Microenvironment -- Differential equations -- Gene expression; Systems biology -- Periodicals; 570
• DOI: 10.1016/j.coisb.2021.05.002
• Languages: English
• ISSNs: 2452-3100

Abstract

Abstract: Forefront techniques for molecular interrogation of mammalian tissues, such as multiplexed tissue imaging, intravital microscopy, and single-cell RNA sequencing (scRNAseq), can combine to quantify cell-type abundance, co-localization, and global levels of receptors and their ligands. Nonetheless, it remains challenging to translate these various quantities into a more comprehensive understanding of how cell–cell communication networks dynamically operate. Therefore, construction of computational models for network-level functions—including niche-dependent actions, homeostasis, and multiscale coordination—will be valuable for productively integrating the battery of experimental approaches. Here, we review recent progress in understanding cell–cell communication networks in tissue. Featured examples include ligand-receptor dissection of immunosuppressive and mitogenic signaling in the tumor microenvironment. As a future direction, we highlight an unmet potential to bridge high-level statistical approaches with low-level physicochemical mechanisms. Highlights: Tissue communication coordinates cellular decisions across multiple length and time scales. Combining multiplexed imaging with scRNAseq guides spatial communication models. Ligand-receptor tabulation generates static maps of intercellular communication. Quantitative in vivo imaging reveals microenvironment-dependent dynamics. Trade-offs between multiplexing and spatiotemporal resolution limit biological conclusions.