Image guided construction of a common coordinate framework for spatial transcriptome data
Abstract Spatial transcriptomics is a powerful technology for high-resolution mapping of gene expression in tissue samples, enabling a molecular level understanding of tissue architecture. The acquisition entails dissecting and profiling micron-thick tissue slices, with multiple slices often needed...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-01862-x |
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| author | Peter Lais Shawn Mishra Kun Xiong Huanan Shi Gurinder Singh Atwal Yu Bai |
| author_facet | Peter Lais Shawn Mishra Kun Xiong Huanan Shi Gurinder Singh Atwal Yu Bai |
| author_sort | Peter Lais |
| collection | DOAJ |
| description | Abstract Spatial transcriptomics is a powerful technology for high-resolution mapping of gene expression in tissue samples, enabling a molecular level understanding of tissue architecture. The acquisition entails dissecting and profiling micron-thick tissue slices, with multiple slices often needed for a comprehensive study. However, the lack of a common coordinate framework (CCF) among slices, due to slicing and displacement variations, can hinder data analysis, making data comparison and integration challenging, and potentially compromising analysis accuracy. Here we present a deep learning algorithm STaCker that unifies the coordinates of transcriptomic slices via an image registration process. STaCker derives a composite image representation by integrating tissue image and gene expression that are transformed to be resilient to noise and batch effects. STaCker overcomes the training data scarcity by training exclusively on diverse synthetic data. Its performance on various benchmarking datasets shows a significant increase in spatial concordance in aligned slices, surpassing existing methods. STaCker also successfully harmonizes multiple real spatial transcriptome datasets acquired from various platforms. These results indicate that STaCker is a valuable computational tool for constructing a CCF with spatial transcriptome data. |
| format | Article |
| id | doaj-art-ffa3cb514f3e4a96854afe424954dba1 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ffa3cb514f3e4a96854afe424954dba12025-08-20T02:34:07ZengNature PortfolioScientific Reports2045-23222025-05-0115112010.1038/s41598-025-01862-xImage guided construction of a common coordinate framework for spatial transcriptome dataPeter Lais0Shawn Mishra1Kun Xiong2Huanan Shi3Gurinder Singh Atwal4Yu Bai5New York University Grossman School of MedicineRegeneron Pharmaceuticals, Inc.Regeneron Pharmaceuticals, Inc.Regeneron Pharmaceuticals, Inc.Flagship PioneeringRegeneron Pharmaceuticals, Inc.Abstract Spatial transcriptomics is a powerful technology for high-resolution mapping of gene expression in tissue samples, enabling a molecular level understanding of tissue architecture. The acquisition entails dissecting and profiling micron-thick tissue slices, with multiple slices often needed for a comprehensive study. However, the lack of a common coordinate framework (CCF) among slices, due to slicing and displacement variations, can hinder data analysis, making data comparison and integration challenging, and potentially compromising analysis accuracy. Here we present a deep learning algorithm STaCker that unifies the coordinates of transcriptomic slices via an image registration process. STaCker derives a composite image representation by integrating tissue image and gene expression that are transformed to be resilient to noise and batch effects. STaCker overcomes the training data scarcity by training exclusively on diverse synthetic data. Its performance on various benchmarking datasets shows a significant increase in spatial concordance in aligned slices, surpassing existing methods. STaCker also successfully harmonizes multiple real spatial transcriptome datasets acquired from various platforms. These results indicate that STaCker is a valuable computational tool for constructing a CCF with spatial transcriptome data.https://doi.org/10.1038/s41598-025-01862-x |
| spellingShingle | Peter Lais Shawn Mishra Kun Xiong Huanan Shi Gurinder Singh Atwal Yu Bai Image guided construction of a common coordinate framework for spatial transcriptome data Scientific Reports |
| title | Image guided construction of a common coordinate framework for spatial transcriptome data |
| title_full | Image guided construction of a common coordinate framework for spatial transcriptome data |
| title_fullStr | Image guided construction of a common coordinate framework for spatial transcriptome data |
| title_full_unstemmed | Image guided construction of a common coordinate framework for spatial transcriptome data |
| title_short | Image guided construction of a common coordinate framework for spatial transcriptome data |
| title_sort | image guided construction of a common coordinate framework for spatial transcriptome data |
| url | https://doi.org/10.1038/s41598-025-01862-x |
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