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      New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data

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          Abstract

          For multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.

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          Most cited references85

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          Massively parallel digital transcriptional profiling of single cells

          Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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            Spatial reconstruction of single-cell gene expression

            Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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              Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

              Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
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                Author and article information

                Contributors
                fanxh@zju.edu.cn
                Journal
                Protein Cell
                Protein Cell
                Protein & Cell
                Higher Education Press (Beijing )
                1674-800X
                1674-8018
                21 May 2020
                21 May 2020
                December 2020
                : 11
                : 12
                : 866-880
                Affiliations
                [1 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, College of Pharmaceutical Sciences, , Zhejiang University, ; Hangzhou, 310058 China
                [2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, College of Computer Science and Technology, , Zhejiang University, ; Hangzhou, 310027 China
                [3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, The First Affiliated Hospital, School of Medicine, , Zhejiang University, ; Hangzhou, 310003 China
                [4 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, The Save Sight Institute, Faculty of Medicine and Health, , The University of Sydney, ; Sydney, NSW 2000 Australia
                Author information
                http://orcid.org/0000-0002-6336-3007
                Article
                727
                10.1007/s13238-020-00727-5
                7719148
                32435978
                d79e7156-41bb-4f0f-aa6a-75090a2fccc7
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 February 2020
                : 12 April 2020
                Categories
                Review
                Custom metadata
                © The Author(s) 2020

                cell-cell communication,single-cell rna sequencing,physical contact-dependent communication,chemical signal-dependent communication,ligand-receptor interaction,network biology

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