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      The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics

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          Abstract

          Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data.

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          Hallmarks of Cancer: The Next Generation

          Douglas Hanahan, Robert A. Weinberg (2011)
          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Inference and analysis of cell-cell communication using CellChat

            Suoqin Jin, Christian Guerrero-Juarez, Lihua Zhang … (2021)
            Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
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              A Mathematical Theory of Communication

              C. Shannon (1948)
              Article 0 comments Cited 1165 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9010218
                Journal ID (pubmed-jr-id): 1211
                Journal ID (nlm-ta): Semin Cancer Biol
                Journal ID (iso-abbrev): Semin Cancer Biol
                Title: Seminars in cancer biology
                ISSN (Print): 1044-579X
                ISSN (Electronic): 1096-3650
                Publication date Nihms-submitted: 1 November 2023
                Publication date (Print): October 2023
                Publication date (Electronic): 15 July 2023
                Publication date PMC-release: 06 November 2023
                Volume: 95
                Pages: 42-51
                Affiliations
                [a ]Department of Mathematics, University of California, Irvine, Irvine, CA, United States
                [b ]The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United States
                [c ]Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, United States
                Author notes
                [* ]Corresponding authors at: Department of Mathematics, University of California, Irvine, Irvine, CA, United States, aalmet@ 123456uci.edu (A.A. Almet), qnie@ 123456math.uci.edu (Q. Nie).
                Article
                Manuscript ID: NIHMS1937557
                DOI: 10.1016/j.semcancer.2023.07.001
                PMC ID: 10627116
                PubMed ID: 37454878
                SO-VID: 43254899-5939-4680-b1d5-9bec442a4227
                License:

                This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

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                Subject: Article

                Keywords: single-cell rna-sequencing,spatial transcriptomics,cell-cell interactions,cancer
                Data availability:
                Keywords: single-cell rna-sequencing, spatial transcriptomics, cell-cell interactions, cancer

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