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Dissection of intercellular communication using the transcriptome-based framework ICELLNET

Cell-to-cell communication can be inferred from ligand–receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We dev...

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Autores principales: Noël, Floriane, Massenet-Regad, Lucile, Carmi-Levy, Irit, Cappuccio, Antonio, Grandclaudon, Maximilien, Trichot, Coline, Kieffer, Yann, Mechta-Grigoriou, Fatima, Soumelis, Vassili
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889941/
https://www.ncbi.nlm.nih.gov/pubmed/33597528
http://dx.doi.org/10.1038/s41467-021-21244-x
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author Noël, Floriane
Massenet-Regad, Lucile
Carmi-Levy, Irit
Cappuccio, Antonio
Grandclaudon, Maximilien
Trichot, Coline
Kieffer, Yann
Mechta-Grigoriou, Fatima
Soumelis, Vassili
author_facet Noël, Floriane
Massenet-Regad, Lucile
Carmi-Levy, Irit
Cappuccio, Antonio
Grandclaudon, Maximilien
Trichot, Coline
Kieffer, Yann
Mechta-Grigoriou, Fatima
Soumelis, Vassili
author_sort Noël, Floriane
collection PubMed
description Cell-to-cell communication can be inferred from ligand–receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We develop ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand–receptor interactions accounting for multiple subunits expression; 2) quantification of communication scores; 3) the possibility to connect a cell population of interest with 31 reference human cell types; and 4) three visualization modes to facilitate biological interpretation. We apply ICELLNET to three datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveals autocrine IL-10 control of human dendritic cell communication with up to 12 cell types. Four of them (T cells, keratinocytes, neutrophils, pDC) are further tested and experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles.
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spelling pubmed-78899412021-03-03 Dissection of intercellular communication using the transcriptome-based framework ICELLNET Noël, Floriane Massenet-Regad, Lucile Carmi-Levy, Irit Cappuccio, Antonio Grandclaudon, Maximilien Trichot, Coline Kieffer, Yann Mechta-Grigoriou, Fatima Soumelis, Vassili Nat Commun Article Cell-to-cell communication can be inferred from ligand–receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We develop ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand–receptor interactions accounting for multiple subunits expression; 2) quantification of communication scores; 3) the possibility to connect a cell population of interest with 31 reference human cell types; and 4) three visualization modes to facilitate biological interpretation. We apply ICELLNET to three datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveals autocrine IL-10 control of human dendritic cell communication with up to 12 cell types. Four of them (T cells, keratinocytes, neutrophils, pDC) are further tested and experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles. Nature Publishing Group UK 2021-02-17 /pmc/articles/PMC7889941/ /pubmed/33597528 http://dx.doi.org/10.1038/s41467-021-21244-x Text en © The Author(s) 2021 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Noël, Floriane
Massenet-Regad, Lucile
Carmi-Levy, Irit
Cappuccio, Antonio
Grandclaudon, Maximilien
Trichot, Coline
Kieffer, Yann
Mechta-Grigoriou, Fatima
Soumelis, Vassili
Dissection of intercellular communication using the transcriptome-based framework ICELLNET
title Dissection of intercellular communication using the transcriptome-based framework ICELLNET
title_full Dissection of intercellular communication using the transcriptome-based framework ICELLNET
title_fullStr Dissection of intercellular communication using the transcriptome-based framework ICELLNET
title_full_unstemmed Dissection of intercellular communication using the transcriptome-based framework ICELLNET
title_short Dissection of intercellular communication using the transcriptome-based framework ICELLNET
title_sort dissection of intercellular communication using the transcriptome-based framework icellnet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889941/
https://www.ncbi.nlm.nih.gov/pubmed/33597528
http://dx.doi.org/10.1038/s41467-021-21244-x
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