Finding spatially variable ligand-receptor interactions with functional support from downstream genes
Abstract Spatial transcriptomics has emerged as a groundbreaking tool for the study of intercellular ligand-receptor interactions (LRIs) that exhibit spatial variability. To identify spatially variable LRIs with activation evidence, we present SPIDER, which constructs cell-cell interaction interface...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62988-0 |
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| _version_ | 1849226169532022784 |
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| author | Shiying Li Ruohan Wang Sitong Liu Shuai Cheng Li |
| author_facet | Shiying Li Ruohan Wang Sitong Liu Shuai Cheng Li |
| author_sort | Shiying Li |
| collection | DOAJ |
| description | Abstract Spatial transcriptomics has emerged as a groundbreaking tool for the study of intercellular ligand-receptor interactions (LRIs) that exhibit spatial variability. To identify spatially variable LRIs with activation evidence, we present SPIDER, which constructs cell-cell interaction interfaces constrained by cellular interaction capacity, and profiles and identifies spatially variable interaction (SVI) signals with support from downstream transcript factors via multiple probabilistic models. SPIDER demonstrates superior performance regarding accuracy, specificity, and spatial variance relative to existing methods. Experiments of simulations and real datasets in bulk and single-cell resolutions validate SPIDER-identified SVIs by spatial autocorrelation and correlation with downstream target genes, and reveal their consistency across multiple biological replicates. Particularly, distinct SVIs on mouse datasets reveal the potential in representing regional and inter-cell type interactions. SPIDER groups SVIs with similar spatial distributions into SVI patterns that are supported by strong Pearson correlations on spot annotations, generating interaction-based sub-clusters within cell-type regions, and deriving enriched pathways. |
| format | Article |
| id | doaj-art-e3162b92ff6c49eaba031dd3d4597bb6 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e3162b92ff6c49eaba031dd3d4597bb62025-08-24T11:37:35ZengNature PortfolioNature Communications2041-17232025-08-0116112210.1038/s41467-025-62988-0Finding spatially variable ligand-receptor interactions with functional support from downstream genesShiying Li0Ruohan Wang1Sitong Liu2Shuai Cheng Li3Department of Computer Science, City University of Hong KongDepartment of Computer Science, City University of Hong KongDepartment of Computer Science, City University of Hong KongDepartment of Computer Science, City University of Hong KongAbstract Spatial transcriptomics has emerged as a groundbreaking tool for the study of intercellular ligand-receptor interactions (LRIs) that exhibit spatial variability. To identify spatially variable LRIs with activation evidence, we present SPIDER, which constructs cell-cell interaction interfaces constrained by cellular interaction capacity, and profiles and identifies spatially variable interaction (SVI) signals with support from downstream transcript factors via multiple probabilistic models. SPIDER demonstrates superior performance regarding accuracy, specificity, and spatial variance relative to existing methods. Experiments of simulations and real datasets in bulk and single-cell resolutions validate SPIDER-identified SVIs by spatial autocorrelation and correlation with downstream target genes, and reveal their consistency across multiple biological replicates. Particularly, distinct SVIs on mouse datasets reveal the potential in representing regional and inter-cell type interactions. SPIDER groups SVIs with similar spatial distributions into SVI patterns that are supported by strong Pearson correlations on spot annotations, generating interaction-based sub-clusters within cell-type regions, and deriving enriched pathways.https://doi.org/10.1038/s41467-025-62988-0 |
| spellingShingle | Shiying Li Ruohan Wang Sitong Liu Shuai Cheng Li Finding spatially variable ligand-receptor interactions with functional support from downstream genes Nature Communications |
| title | Finding spatially variable ligand-receptor interactions with functional support from downstream genes |
| title_full | Finding spatially variable ligand-receptor interactions with functional support from downstream genes |
| title_fullStr | Finding spatially variable ligand-receptor interactions with functional support from downstream genes |
| title_full_unstemmed | Finding spatially variable ligand-receptor interactions with functional support from downstream genes |
| title_short | Finding spatially variable ligand-receptor interactions with functional support from downstream genes |
| title_sort | finding spatially variable ligand receptor interactions with functional support from downstream genes |
| url | https://doi.org/10.1038/s41467-025-62988-0 |
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