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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.948508/full |
| _version_ | 1817996441816137728 |
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| author | Xinyi Wang Axel A. Almet Axel A. Almet Qing Nie Qing Nie Qing Nie |
| author_facet | Xinyi Wang Axel A. Almet Axel A. Almet Qing Nie Qing Nie Qing Nie |
| author_sort | Xinyi Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-14T02:22:19Z |
| format | Article |
| id | doaj.art-ba91cdce67e449e799a5ae9f0fc4bb77 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-14T02:22:19Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-ba91cdce67e449e799a5ae9f0fc4bb772022-12-22T02:18:00ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-08-011310.3389/fgene.2022.948508948508Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomicsXinyi Wang0Axel A. Almet 1Axel A. Almet 2Qing Nie3Qing Nie4Qing Nie5Department of Mathematics, University of California, Irvine, Irvine, CA, United StatesDepartment of Mathematics, University of California, Irvine, Irvine, CA, United StatesThe NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United StatesDepartment of Mathematics, University of California, Irvine, Irvine, CA, United StatesThe NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United StatesDepartment of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, United StatesCell–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.https://www.frontiersin.org/articles/10.3389/fgene.2022.948508/fullnetwork analysissingle-cellcell–cell interactionsdiversityCOVID-19 |
| spellingShingle | Xinyi Wang Axel A. Almet Axel A. Almet Qing Nie Qing Nie Qing Nie Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics Frontiers in Genetics network analysis single-cell cell–cell interactions diversity COVID-19 |
| 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 | network analysis single-cell cell–cell interactions diversity COVID-19 |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.948508/full |
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