Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics
Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolom...
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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: | BME Frontiers |
| Online Access: | https://spj.science.org/doi/10.34133/bmef.0084 |
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| author | Changxiang Huan Jinze Li Yingxue Li Shasha Zhao Qi Yang Zhiqi Zhang Chuanyu Li Shuli Li Zhen Guo Jia Yao Wei Zhang Lianqun Zhou |
| author_facet | Changxiang Huan Jinze Li Yingxue Li Shasha Zhao Qi Yang Zhiqi Zhang Chuanyu Li Shuli Li Zhen Guo Jia Yao Wei Zhang Lianqun Zhou |
| author_sort | Changxiang Huan |
| collection | DOAJ |
| description | Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels. Spatial monoomics technology has evolved from monoomics to spatial multiomics. Moreover, the spatial resolution, high-throughput detection capability, capture efficiency, and compatibility with various sample types of omics technology have considerably advanced. Despite the technological advances in this field, data analysis frameworks have stagnated. Current challenges include incomplete spatial monoomics data analysis pipeline, overly complex data analysis tasks, and few established spatial multiomics data analysis strategies. In this review, we systematically summarize recent developments of various spatial monoomics techniques and improvements in related data analysis pipeline. On the basis of the spatial multiomics technology, we propose a data integration strategy with cross-platform, cross-slice, and cross-modality. We summarize the potential applications of spatial monoomics technology, aiming to provide researchers and clinicians with a better understanding of how such applications have advanced. Spatial multiomics technology is expected to substantially impact biology and precision medicine through measurements of cellular tissue structures and the extraction of biomolecular features. |
| format | Article |
| id | doaj-art-c846d52ac2a74322867dcff8fa8e0a96 |
| institution | Kabale University |
| issn | 2765-8031 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | BME Frontiers |
| spelling | doaj-art-c846d52ac2a74322867dcff8fa8e0a962025-01-13T08:06:42ZengAmerican Association for the Advancement of Science (AAAS)BME Frontiers2765-80312025-01-01610.34133/bmef.0084Spatially Resolved Multiomics: Data Analysis from Monoomics to MultiomicsChangxiang Huan0Jinze Li1Yingxue Li2Shasha Zhao3Qi Yang4Zhiqi Zhang5Chuanyu Li6Shuli Li7Zhen Guo8Jia Yao9Wei Zhang10Lianqun Zhou11CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels. Spatial monoomics technology has evolved from monoomics to spatial multiomics. Moreover, the spatial resolution, high-throughput detection capability, capture efficiency, and compatibility with various sample types of omics technology have considerably advanced. Despite the technological advances in this field, data analysis frameworks have stagnated. Current challenges include incomplete spatial monoomics data analysis pipeline, overly complex data analysis tasks, and few established spatial multiomics data analysis strategies. In this review, we systematically summarize recent developments of various spatial monoomics techniques and improvements in related data analysis pipeline. On the basis of the spatial multiomics technology, we propose a data integration strategy with cross-platform, cross-slice, and cross-modality. We summarize the potential applications of spatial monoomics technology, aiming to provide researchers and clinicians with a better understanding of how such applications have advanced. Spatial multiomics technology is expected to substantially impact biology and precision medicine through measurements of cellular tissue structures and the extraction of biomolecular features.https://spj.science.org/doi/10.34133/bmef.0084 |
| spellingShingle | Changxiang Huan Jinze Li Yingxue Li Shasha Zhao Qi Yang Zhiqi Zhang Chuanyu Li Shuli Li Zhen Guo Jia Yao Wei Zhang Lianqun Zhou Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics BME Frontiers |
| title | Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics |
| title_full | Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics |
| title_fullStr | Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics |
| title_full_unstemmed | Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics |
| title_short | Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics |
| title_sort | spatially resolved multiomics data analysis from monoomics to multiomics |
| url | https://spj.science.org/doi/10.34133/bmef.0084 |
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