Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis
Abstract Recent progress in multiplexed tissue imaging is deepening our understanding of tumor microenvironments related to treatment response and disease progression. However, analyzing whole-slide images with millions of cells remains computationally challenging, and few methods provide a principl...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61821-y |
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| author | Xiyu Peng James W. Smithy Mohammad Yosofvand Caroline E. Kostrzewa MaryLena Bleile Fiona D. Ehrich Jasme Lee Michael A. Postow Margaret K. Callahan Katherine S. Panageas Ronglai Shen |
| author_facet | Xiyu Peng James W. Smithy Mohammad Yosofvand Caroline E. Kostrzewa MaryLena Bleile Fiona D. Ehrich Jasme Lee Michael A. Postow Margaret K. Callahan Katherine S. Panageas Ronglai Shen |
| author_sort | Xiyu Peng |
| collection | DOAJ |
| description | Abstract Recent progress in multiplexed tissue imaging is deepening our understanding of tumor microenvironments related to treatment response and disease progression. However, analyzing whole-slide images with millions of cells remains computationally challenging, and few methods provide a principled approach for integrative analysis across images. Here, we introduce SpatialTopic, a spatial topic model designed to decode high-level spatial tissue architecture from multiplexed images. By integrating both cell type and spatial information, SpatialTopic identifies recurrent spatial patterns, or “topics,” that reflect biologically meaningful tissue structures. We benchmarked SpatialTopic across diverse single-cell spatial transcriptomic and proteomic imaging platforms spanning multiple tissue types. We show that SpatialTopic is highly scalable to large-scale images, along with high precision and interpretability. It consistently identifies biologically and clinically significant spatial topics, such as tertiary lymphoid structures, and tracks spatial changes over disease progression. Its computational efficiency and broad applicability will enhance the analysis of large-scale imaging datasets. |
| format | Article |
| id | doaj-art-2ba9fa616ce64d018a804c2adda770fa |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-2ba9fa616ce64d018a804c2adda770fa2025-08-20T03:43:27ZengNature PortfolioNature Communications2041-17232025-07-0116111510.1038/s41467-025-61821-yScalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysisXiyu Peng0James W. Smithy1Mohammad Yosofvand2Caroline E. Kostrzewa3MaryLena Bleile4Fiona D. Ehrich5Jasme Lee6Michael A. Postow7Margaret K. Callahan8Katherine S. Panageas9Ronglai Shen10Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterDepartment of Medicine, Memorial Sloan Kettering Cancer CenterDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterDepartment of Medicine, Memorial Sloan Kettering Cancer CenterNeag Comprehensive Cancer Center, UConn HealthDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterDepartment of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterAbstract Recent progress in multiplexed tissue imaging is deepening our understanding of tumor microenvironments related to treatment response and disease progression. However, analyzing whole-slide images with millions of cells remains computationally challenging, and few methods provide a principled approach for integrative analysis across images. Here, we introduce SpatialTopic, a spatial topic model designed to decode high-level spatial tissue architecture from multiplexed images. By integrating both cell type and spatial information, SpatialTopic identifies recurrent spatial patterns, or “topics,” that reflect biologically meaningful tissue structures. We benchmarked SpatialTopic across diverse single-cell spatial transcriptomic and proteomic imaging platforms spanning multiple tissue types. We show that SpatialTopic is highly scalable to large-scale images, along with high precision and interpretability. It consistently identifies biologically and clinically significant spatial topics, such as tertiary lymphoid structures, and tracks spatial changes over disease progression. Its computational efficiency and broad applicability will enhance the analysis of large-scale imaging datasets.https://doi.org/10.1038/s41467-025-61821-y |
| spellingShingle | Xiyu Peng James W. Smithy Mohammad Yosofvand Caroline E. Kostrzewa MaryLena Bleile Fiona D. Ehrich Jasme Lee Michael A. Postow Margaret K. Callahan Katherine S. Panageas Ronglai Shen Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis Nature Communications |
| title | Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis |
| title_full | Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis |
| title_fullStr | Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis |
| title_full_unstemmed | Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis |
| title_short | Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis |
| title_sort | scalable topic modelling decodes spatial tissue architecture for large scale multiplexed imaging analysis |
| url | https://doi.org/10.1038/s41467-025-61821-y |
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