Inferring a spatial code of cell-cell interactions across a whole animal body.
Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However,...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010715 |
| _version_ | 1811185773441974272 |
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| author | Erick Armingol Abbas Ghaddar Chintan J Joshi Hratch Baghdassarian Isaac Shamie Jason Chan Hsuan-Lin Her Samuel Berhanu Anushka Dar Fabiola Rodriguez-Armstrong Olivia Yang Eyleen J O'Rourke Nathan E Lewis |
| author_facet | Erick Armingol Abbas Ghaddar Chintan J Joshi Hratch Baghdassarian Isaac Shamie Jason Chan Hsuan-Lin Her Samuel Berhanu Anushka Dar Fabiola Rodriguez-Armstrong Olivia Yang Eyleen J O'Rourke Nathan E Lewis |
| author_sort | Erick Armingol |
| collection | DOAJ |
| description | Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body. |
| first_indexed | 2024-04-11T13:34:58Z |
| format | Article |
| id | doaj.art-fbf28cea235048f493ac6e76bf2459d1 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T13:34:58Z |
| publishDate | 2022-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-fbf28cea235048f493ac6e76bf2459d12022-12-22T04:21:34ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-11-011811e101071510.1371/journal.pcbi.1010715Inferring a spatial code of cell-cell interactions across a whole animal body.Erick ArmingolAbbas GhaddarChintan J JoshiHratch BaghdassarianIsaac ShamieJason ChanHsuan-Lin HerSamuel BerhanuAnushka DarFabiola Rodriguez-ArmstrongOlivia YangEyleen J O'RourkeNathan E LewisCell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.https://doi.org/10.1371/journal.pcbi.1010715 |
| spellingShingle | Erick Armingol Abbas Ghaddar Chintan J Joshi Hratch Baghdassarian Isaac Shamie Jason Chan Hsuan-Lin Her Samuel Berhanu Anushka Dar Fabiola Rodriguez-Armstrong Olivia Yang Eyleen J O'Rourke Nathan E Lewis Inferring a spatial code of cell-cell interactions across a whole animal body. PLoS Computational Biology |
| title | Inferring a spatial code of cell-cell interactions across a whole animal body. |
| title_full | Inferring a spatial code of cell-cell interactions across a whole animal body. |
| title_fullStr | Inferring a spatial code of cell-cell interactions across a whole animal body. |
| title_full_unstemmed | Inferring a spatial code of cell-cell interactions across a whole animal body. |
| title_short | Inferring a spatial code of cell-cell interactions across a whole animal body. |
| title_sort | inferring a spatial code of cell cell interactions across a whole animal body |
| url | https://doi.org/10.1371/journal.pcbi.1010715 |
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