Spatial integration of multi-omics single-cell data with SIMO
Abstract Technical limitations in spatial and single-cell omics sequencing pose challenges for capturing and describing multimodal information at the spatial scale. To address this, we develop SIMO, a computational method designed for the Spatial Integration of Multi-Omics datasets through probabili...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56523-4 |
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| _version_ | 1832571506668863488 |
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| author | Penghui Yang Kaiyu Jin Yue Yao Lijun Jin Xin Shao Chengyu Li Xiaoyan Lu Xiaohui Fan |
| author_facet | Penghui Yang Kaiyu Jin Yue Yao Lijun Jin Xin Shao Chengyu Li Xiaoyan Lu Xiaohui Fan |
| author_sort | Penghui Yang |
| collection | DOAJ |
| description | Abstract Technical limitations in spatial and single-cell omics sequencing pose challenges for capturing and describing multimodal information at the spatial scale. To address this, we develop SIMO, a computational method designed for the Spatial Integration of Multi-Omics datasets through probabilistic alignment. Unlike previous tools, SIMO not only integrates spatial transcriptomics with single-cell RNA-seq but expands beyond, enabling integration across multiple single-cell modalities, such as chromatin accessibility and DNA methylation, which have not been co-profiled spatially before. We benchmark SIMO on simulated datasets, demonstrating its high accuracy and robustness. Further application on biological datasets reveals SIMO’s ability to detect topological patterns of cells and their regulatory modes across multiple omics layers. Through comprehensive analysis of real-world data, SIMO uncovers multimodal spatial heterogeneity, offering deeper insights into the spatial organization and regulation of biological molecules. These findings position SIMO as a powerful tool for advancing spatial biology by revealing previously inaccessible multimodal insights. |
| format | Article |
| id | doaj-art-3e86d47dc70c4df9815bd40a31a85db6 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3e86d47dc70c4df9815bd40a31a85db62025-02-02T12:32:12ZengNature PortfolioNature Communications2041-17232025-02-0116111510.1038/s41467-025-56523-4Spatial integration of multi-omics single-cell data with SIMOPenghui Yang0Kaiyu Jin1Yue Yao2Lijun Jin3Xin Shao4Chengyu Li5Xiaoyan Lu6Xiaohui Fan7College of Pharmaceutical Sciences, Zhejiang UniversityCollege of Pharmaceutical Sciences, Zhejiang UniversityCollege of Pharmaceutical Sciences, Zhejiang UniversityCollege of Pharmaceutical Sciences, Zhejiang UniversityCollege of Pharmaceutical Sciences, Zhejiang UniversityCollege of Pharmaceutical Sciences, Zhejiang UniversityCollege of Pharmaceutical Sciences, Zhejiang UniversityCollege of Pharmaceutical Sciences, Zhejiang UniversityAbstract Technical limitations in spatial and single-cell omics sequencing pose challenges for capturing and describing multimodal information at the spatial scale. To address this, we develop SIMO, a computational method designed for the Spatial Integration of Multi-Omics datasets through probabilistic alignment. Unlike previous tools, SIMO not only integrates spatial transcriptomics with single-cell RNA-seq but expands beyond, enabling integration across multiple single-cell modalities, such as chromatin accessibility and DNA methylation, which have not been co-profiled spatially before. We benchmark SIMO on simulated datasets, demonstrating its high accuracy and robustness. Further application on biological datasets reveals SIMO’s ability to detect topological patterns of cells and their regulatory modes across multiple omics layers. Through comprehensive analysis of real-world data, SIMO uncovers multimodal spatial heterogeneity, offering deeper insights into the spatial organization and regulation of biological molecules. These findings position SIMO as a powerful tool for advancing spatial biology by revealing previously inaccessible multimodal insights.https://doi.org/10.1038/s41467-025-56523-4 |
| spellingShingle | Penghui Yang Kaiyu Jin Yue Yao Lijun Jin Xin Shao Chengyu Li Xiaoyan Lu Xiaohui Fan Spatial integration of multi-omics single-cell data with SIMO Nature Communications |
| title | Spatial integration of multi-omics single-cell data with SIMO |
| title_full | Spatial integration of multi-omics single-cell data with SIMO |
| title_fullStr | Spatial integration of multi-omics single-cell data with SIMO |
| title_full_unstemmed | Spatial integration of multi-omics single-cell data with SIMO |
| title_short | Spatial integration of multi-omics single-cell data with SIMO |
| title_sort | spatial integration of multi omics single cell data with simo |
| url | https://doi.org/10.1038/s41467-025-56523-4 |
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