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New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data
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 o...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Higher Education Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719148/ https://www.ncbi.nlm.nih.gov/pubmed/32435978 http://dx.doi.org/10.1007/s13238-020-00727-5 |
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author | Shao, Xin Lu, Xiaoyan Liao, Jie Chen, Huajun Fan, Xiaohui |
author_facet | Shao, Xin Lu, Xiaoyan Liao, Jie Chen, Huajun Fan, Xiaohui |
author_sort | Shao, Xin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7719148 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Higher Education Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77191482020-12-07 New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data Shao, Xin Lu, Xiaoyan Liao, Jie Chen, Huajun Fan, Xiaohui Protein Cell Review 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. Higher Education Press 2020-05-21 2020-12 /pmc/articles/PMC7719148/ /pubmed/32435978 http://dx.doi.org/10.1007/s13238-020-00727-5 Text en © 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/. |
spellingShingle | Review Shao, Xin Lu, Xiaoyan Liao, Jie Chen, Huajun Fan, Xiaohui New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data |
title | New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data |
title_full | New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data |
title_fullStr | New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data |
title_full_unstemmed | New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data |
title_short | New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data |
title_sort | new avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719148/ https://www.ncbi.nlm.nih.gov/pubmed/32435978 http://dx.doi.org/10.1007/s13238-020-00727-5 |
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