cytoNet: Spatiotemporal network analysis of cell communities.
We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009846&type=printable |
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| _version_ | 1849693160199946240 |
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| author | Arun S Mahadevan Byron L Long Chenyue W Hu David T Ryan Nicolas E Grandel George L Britton Marisol Bustos Maria A Gonzalez Porras Katerina Stojkova Andrew Ligeralde Hyeonwi Son John Shannonhouse Jacob T Robinson Aryeh Warmflash Eric M Brey Yu Shin Kim Amina A Qutub |
| author_facet | Arun S Mahadevan Byron L Long Chenyue W Hu David T Ryan Nicolas E Grandel George L Britton Marisol Bustos Maria A Gonzalez Porras Katerina Stojkova Andrew Ligeralde Hyeonwi Son John Shannonhouse Jacob T Robinson Aryeh Warmflash Eric M Brey Yu Shin Kim Amina A Qutub |
| author_sort | Arun S Mahadevan |
| collection | DOAJ |
| description | We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains. |
| format | Article |
| id | doaj-art-68be8c3e4b864f43830fbc95545cafd3 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-68be8c3e4b864f43830fbc95545cafd32025-08-20T03:20:30ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-06-01186e100984610.1371/journal.pcbi.1009846cytoNet: Spatiotemporal network analysis of cell communities.Arun S MahadevanByron L LongChenyue W HuDavid T RyanNicolas E GrandelGeorge L BrittonMarisol BustosMaria A Gonzalez PorrasKaterina StojkovaAndrew LigeraldeHyeonwi SonJohn ShannonhouseJacob T RobinsonAryeh WarmflashEric M BreyYu Shin KimAmina A QutubWe introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009846&type=printable |
| spellingShingle | Arun S Mahadevan Byron L Long Chenyue W Hu David T Ryan Nicolas E Grandel George L Britton Marisol Bustos Maria A Gonzalez Porras Katerina Stojkova Andrew Ligeralde Hyeonwi Son John Shannonhouse Jacob T Robinson Aryeh Warmflash Eric M Brey Yu Shin Kim Amina A Qutub cytoNet: Spatiotemporal network analysis of cell communities. PLoS Computational Biology |
| title | cytoNet: Spatiotemporal network analysis of cell communities. |
| title_full | cytoNet: Spatiotemporal network analysis of cell communities. |
| title_fullStr | cytoNet: Spatiotemporal network analysis of cell communities. |
| title_full_unstemmed | cytoNet: Spatiotemporal network analysis of cell communities. |
| title_short | cytoNet: Spatiotemporal network analysis of cell communities. |
| title_sort | cytonet spatiotemporal network analysis of cell communities |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009846&type=printable |
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