Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis
Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spati...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Research |
| Online Access: | https://spj.science.org/doi/10.34133/research.0568 |
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| _version_ | 1841525739954896896 |
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| author | Yongxin Ge Jiake Leng Ziyang Tang Kanran Wang Kaicheng U Sophia Meixuan Zhang Sen Han Yiyan Zhang Jinxi Xiang Sen Yang Xiang Liu Yi Song Xiyue Wang Yuchen Li Junhan Zhao |
| author_facet | Yongxin Ge Jiake Leng Ziyang Tang Kanran Wang Kaicheng U Sophia Meixuan Zhang Sen Han Yiyan Zhang Jinxi Xiang Sen Yang Xiang Liu Yi Song Xiyue Wang Yuchen Li Junhan Zhao |
| author_sort | Yongxin Ge |
| collection | DOAJ |
| description | Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling. GIST employs histopathology foundation models pretrained on millions of histology images to enhance feature extraction and a hybrid graph transformer model to integrate them with transcriptome features. Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. GIST provides a generalizable framework for integrating histology with spatial transcriptome analysis, revealing novel insights into spatial organization and functional dynamics. |
| format | Article |
| id | doaj-art-eb9c0cd266c743019d588f03959d7847 |
| institution | Kabale University |
| issn | 2639-5274 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Research |
| spelling | doaj-art-eb9c0cd266c743019d588f03959d78472025-01-17T08:00:36ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742025-01-01810.34133/research.0568Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile AnalysisYongxin Ge0Jiake Leng1Ziyang Tang2Kanran Wang3Kaicheng U4Sophia Meixuan Zhang5Sen Han6Yiyan Zhang7Jinxi Xiang8Sen Yang9Xiang Liu10Yi Song11Xiyue Wang12Yuchen Li13Junhan Zhao14School of Big Data and Software Engineering, Chongqing University, Chongqing, China.School of Big Data and Software Engineering, Chongqing University, Chongqing, China.Department of Computer and Information Technology, Purdue University, West Lafayette, IN, USA.Radiation Oncology Center, Chongqing University Cancer Hospital, Chongqing, China.Tri-Institutional Computational Biology & Medicine, Weill Cornell Medicine, New York, NY, USA.College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA.Division of Genetics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.Department of Neurosurgery, Chongqing University Three Gorges Hospital, Chongqing, China.Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling. GIST employs histopathology foundation models pretrained on millions of histology images to enhance feature extraction and a hybrid graph transformer model to integrate them with transcriptome features. Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. GIST provides a generalizable framework for integrating histology with spatial transcriptome analysis, revealing novel insights into spatial organization and functional dynamics.https://spj.science.org/doi/10.34133/research.0568 |
| spellingShingle | Yongxin Ge Jiake Leng Ziyang Tang Kanran Wang Kaicheng U Sophia Meixuan Zhang Sen Han Yiyan Zhang Jinxi Xiang Sen Yang Xiang Liu Yi Song Xiyue Wang Yuchen Li Junhan Zhao Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis Research |
| title | Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis |
| title_full | Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis |
| title_fullStr | Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis |
| title_full_unstemmed | Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis |
| title_short | Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis |
| title_sort | deep learning enabled integration of histology and transcriptomics for tissue spatial profile analysis |
| url | https://spj.science.org/doi/10.34133/research.0568 |
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