stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs
Abstract Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, w...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60575-x |
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| _version_ | 1850111776120635392 |
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| author | Chunman Zuo Junjie Xia Yupeng Xu Ying Xu Pingting Gao Jing Zhang Yan Wang Luonan Chen |
| author_facet | Chunman Zuo Junjie Xia Yupeng Xu Ying Xu Pingting Gao Jing Zhang Yan Wang Luonan Chen |
| author_sort | Chunman Zuo |
| collection | DOAJ |
| description | Abstract Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, we propose stClinic, a dynamic graph model that integrates SMSMO and phenotype data to uncover clinically relevant niches. stClinic aggregates information from evolving neighboring nodes with similar-profiles across slices, aided by a Mixture-of-Gaussians prior on latent features. Furthermore, stClinic directly links niches to clinical manifestations by characterizing each slice with attention-based geometric statistical measures, relative to the population. In cancer studies, stClinic uses survival time to assess niche malignancy, identifying aggressive niches enriched with tumor-associated macrophages, alongside favorable prognostic niches abundant in B and plasma cells. Additionally, stClinic identifies a niche abundant in SPP1+ MTRNR2L12+ myeloid cells and cancer-associated fibroblasts driving colorectal cancer cell adaptation and invasion in healthy liver tissue. These findings are supported by independent functional and clinical data. Notably, stClinic excels in label annotation through zero-shot learning and facilitates multi-omics integration by relying on other tools for latent feature initialization. |
| format | Article |
| id | doaj-art-64fe67e19b0f4267993a1bcb67b9bbb5 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-64fe67e19b0f4267993a1bcb67b9bbb52025-08-20T02:37:33ZengNature PortfolioNature Communications2041-17232025-06-0116111810.1038/s41467-025-60575-xstClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphsChunman Zuo0Junjie Xia1Yupeng Xu2Ying Xu3Pingting Gao4Jing Zhang5Yan Wang6Luonan Chen7School of Life Sciences, Sun Yat-sen UniversityInstitute of Artificial Intelligence, Donghua UniversityInstitute of Artificial Intelligence, Donghua UniversitySystem Biology Lab for Metabolic Reprogramming, Department of Human Genetics and Cell Biology, School of Medicine, Southern University of Science and TechnologyShanghai Collaborative Innovation Center of Endoscopy, Fudan UniversityDepartment of Pathology, Changzheng Hospital, Secondary Military Medical UniversityCollege of Computer Science and Technology, Jilin UniversitySchool of Mathematical Sciences, Shanghai Jiao Tong UniversityAbstract Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, we propose stClinic, a dynamic graph model that integrates SMSMO and phenotype data to uncover clinically relevant niches. stClinic aggregates information from evolving neighboring nodes with similar-profiles across slices, aided by a Mixture-of-Gaussians prior on latent features. Furthermore, stClinic directly links niches to clinical manifestations by characterizing each slice with attention-based geometric statistical measures, relative to the population. In cancer studies, stClinic uses survival time to assess niche malignancy, identifying aggressive niches enriched with tumor-associated macrophages, alongside favorable prognostic niches abundant in B and plasma cells. Additionally, stClinic identifies a niche abundant in SPP1+ MTRNR2L12+ myeloid cells and cancer-associated fibroblasts driving colorectal cancer cell adaptation and invasion in healthy liver tissue. These findings are supported by independent functional and clinical data. Notably, stClinic excels in label annotation through zero-shot learning and facilitates multi-omics integration by relying on other tools for latent feature initialization.https://doi.org/10.1038/s41467-025-60575-x |
| spellingShingle | Chunman Zuo Junjie Xia Yupeng Xu Ying Xu Pingting Gao Jing Zhang Yan Wang Luonan Chen stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs Nature Communications |
| title | stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs |
| title_full | stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs |
| title_fullStr | stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs |
| title_full_unstemmed | stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs |
| title_short | stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs |
| title_sort | stclinic dissects clinically relevant niches by integrating spatial multi slice multi omics data in dynamic graphs |
| url | https://doi.org/10.1038/s41467-025-60575-x |
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