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Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics
Cell–cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465179/ https://www.ncbi.nlm.nih.gov/pubmed/36105110 http://dx.doi.org/10.3389/fgene.2022.948508 |
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author | Wang, Xinyi Almet , Axel A. Nie, Qing |
author_facet | Wang, Xinyi Almet , Axel A. Nie, Qing |
author_sort | Wang, Xinyi |
collection | PubMed |
description | Cell–cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focus on direct comparisons between individual CCI networks of individual samples from patients, rather than “group-level” comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease statuses, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease statuses. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network level. By applying these analysis to a large-scale single-cell RNA-sequencing dataset of coronavirus disease 2019 (COVID-19), we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19. |
format | Online Article Text |
id | pubmed-9465179 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94651792022-09-13 Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics Wang, Xinyi Almet , Axel A. Nie, Qing Front Genet Genetics Cell–cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focus on direct comparisons between individual CCI networks of individual samples from patients, rather than “group-level” comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease statuses, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease statuses. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network level. By applying these analysis to a large-scale single-cell RNA-sequencing dataset of coronavirus disease 2019 (COVID-19), we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19. Frontiers Media S.A. 2022-08-29 /pmc/articles/PMC9465179/ /pubmed/36105110 http://dx.doi.org/10.3389/fgene.2022.948508 Text en Copyright © 2022 Wang, Almet and Nie. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wang, Xinyi Almet , Axel A. Nie, Qing Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics |
title | Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics |
title_full | Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics |
title_fullStr | Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics |
title_full_unstemmed | Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics |
title_short | Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics |
title_sort | analyzing network diversity of cell–cell interactions in covid-19 using single-cell transcriptomics |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465179/ https://www.ncbi.nlm.nih.gov/pubmed/36105110 http://dx.doi.org/10.3389/fgene.2022.948508 |
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