SpaIM: single-cell spatial transcriptomics imputation via style transfer
Abstract Spatial transcriptomics (ST) technologies have transformed our understanding of cellular organization but are limited by sparse signals and restricted gene coverage. To address these challenges, we introduce SpaIM, a style transfer learning model that leverages single-cell RNA sequencing (s...
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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/s41467-025-63185-9 |
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| author | Bo Li Ziyang Tang Aishwarya Budhkar Xiang Liu Tonglin Zhang Baijian Yang Jing Su Qianqian Song |
| author_facet | Bo Li Ziyang Tang Aishwarya Budhkar Xiang Liu Tonglin Zhang Baijian Yang Jing Su Qianqian Song |
| author_sort | Bo Li |
| collection | DOAJ |
| description | Abstract Spatial transcriptomics (ST) technologies have transformed our understanding of cellular organization but are limited by sparse signals and restricted gene coverage. To address these challenges, we introduce SpaIM, a style transfer learning model that leverages single-cell RNA sequencing (scRNA-seq) data to predict unmeasured gene expressions in ST profiles. By disentangling shared content and modality-specific styles, SpaIM effectively integrates scRNA-seq’s rich gene expression with the spatial context of ST. Evaluated across 53 datasets spanning sequencing- and imaging-based spatial technologies in various tissue types, SpaIM consistently outperforms 12 state-of-the-art methods in improving gene coverage and expression accuracy. Furthermore, SpaIM enhances downstream analyses, including ligand-receptor interaction inference, spatial domain characterization, and differential gene expression analysis. Released as open-source software, SpaIM expands accessibility and utility in ST research. Overall, SpaIM represents a robust and generalizable framework for enriching ST data with single-cell information, enabling deeper insights into tissue architecture and cellular function. |
| format | Article |
| id | doaj-art-9f8b3a990502487298f87bbf7a4b304f |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9f8b3a990502487298f87bbf7a4b304f2025-08-24T11:38:51ZengNature PortfolioNature Communications2041-17232025-08-0116111310.1038/s41467-025-63185-9SpaIM: single-cell spatial transcriptomics imputation via style transferBo Li0Ziyang Tang1Aishwarya Budhkar2Xiang Liu3Tonglin Zhang4Baijian Yang5Jing Su6Qianqian Song7Department of Computer and Information Science, University of Macau, TaipaDepartment of Computer and Information Technology, Purdue UniversityDepartment of Biostatistics and Health Data Science, Indiana University School of MedicineDepartment of Biostatistics and Health Data Science, Indiana University School of MedicineDepartment of Statistics, Purdue UniversityDepartment of Computer and Information Technology, Purdue UniversityDepartment of Biostatistics and Health Data Science, Indiana University School of MedicineDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of FloridaAbstract Spatial transcriptomics (ST) technologies have transformed our understanding of cellular organization but are limited by sparse signals and restricted gene coverage. To address these challenges, we introduce SpaIM, a style transfer learning model that leverages single-cell RNA sequencing (scRNA-seq) data to predict unmeasured gene expressions in ST profiles. By disentangling shared content and modality-specific styles, SpaIM effectively integrates scRNA-seq’s rich gene expression with the spatial context of ST. Evaluated across 53 datasets spanning sequencing- and imaging-based spatial technologies in various tissue types, SpaIM consistently outperforms 12 state-of-the-art methods in improving gene coverage and expression accuracy. Furthermore, SpaIM enhances downstream analyses, including ligand-receptor interaction inference, spatial domain characterization, and differential gene expression analysis. Released as open-source software, SpaIM expands accessibility and utility in ST research. Overall, SpaIM represents a robust and generalizable framework for enriching ST data with single-cell information, enabling deeper insights into tissue architecture and cellular function.https://doi.org/10.1038/s41467-025-63185-9 |
| spellingShingle | Bo Li Ziyang Tang Aishwarya Budhkar Xiang Liu Tonglin Zhang Baijian Yang Jing Su Qianqian Song SpaIM: single-cell spatial transcriptomics imputation via style transfer Nature Communications |
| title | SpaIM: single-cell spatial transcriptomics imputation via style transfer |
| title_full | SpaIM: single-cell spatial transcriptomics imputation via style transfer |
| title_fullStr | SpaIM: single-cell spatial transcriptomics imputation via style transfer |
| title_full_unstemmed | SpaIM: single-cell spatial transcriptomics imputation via style transfer |
| title_short | SpaIM: single-cell spatial transcriptomics imputation via style transfer |
| title_sort | spaim single cell spatial transcriptomics imputation via style transfer |
| url | https://doi.org/10.1038/s41467-025-63185-9 |
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