High-resolution mapping of single cells in spatial context
Abstract Spatially resolved transcriptomic technologies have emerged as pivotal tools for elucidating molecular regulation and cellular interplay within the intricate tissue microenvironment, but hampered by insufficient gene recovery or challenges in achieving intact single-cell resolution. Here, w...
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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/s41467-025-61667-4 |
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| author | Jincan Ke Jian Xu Jia Liu Yumeng Yang Chenkai Guo Bingbing Xie Guizhong Cui Guangdun Peng Shengbao Suo |
| author_facet | Jincan Ke Jian Xu Jia Liu Yumeng Yang Chenkai Guo Bingbing Xie Guizhong Cui Guangdun Peng Shengbao Suo |
| author_sort | Jincan Ke |
| collection | DOAJ |
| description | Abstract Spatially resolved transcriptomic technologies have emerged as pivotal tools for elucidating molecular regulation and cellular interplay within the intricate tissue microenvironment, but hampered by insufficient gene recovery or challenges in achieving intact single-cell resolution. Here, we develop Cellular Mapping of Attributes with Position (CMAP), a method that efficiently maps large-scale individual cells to their precise spatial locations by integrating single-cell and spatial data through a divide-and-conquer strategy. Analysis of both simulated and real datasets shows that CMAP performs effectively and is adaptable across diverse data types and sequencing platforms. Particularly, CMAP handles scenarios well where discrepancies exist between single-cell and spatial transcriptomics data. Our findings underscore CMAP’s capacity to endow single-cells with exact spatial coordinates, facilitating the dissection of nuanced spatial-organ-specific endothelial cell heterogeneity, as well as the intricate cancer immune microenvironments that elude conventional single-cell or spatial data analysis. |
| format | Article |
| id | doaj-art-afe9255c08534395b81e2433ebc8d4a5 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-afe9255c08534395b81e2433ebc8d4a52025-08-20T03:43:10ZengNature PortfolioNature Communications2041-17232025-07-0116112110.1038/s41467-025-61667-4High-resolution mapping of single cells in spatial contextJincan Ke0Jian Xu1Jia Liu2Yumeng Yang3Chenkai Guo4Bingbing Xie5Guizhong Cui6Guangdun Peng7Shengbao Suo8Center for Cell Lineage Technology and Engineering, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of SciencesGuangzhou National LaboratoryGuangzhou National LaboratoryGuangzhou National LaboratoryCenter for Cell Lineage Technology and Engineering, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of SciencesGuangzhou National LaboratoryGuangzhou National LaboratoryCenter for Cell Lineage Technology and Engineering, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of SciencesGuangzhou National LaboratoryAbstract Spatially resolved transcriptomic technologies have emerged as pivotal tools for elucidating molecular regulation and cellular interplay within the intricate tissue microenvironment, but hampered by insufficient gene recovery or challenges in achieving intact single-cell resolution. Here, we develop Cellular Mapping of Attributes with Position (CMAP), a method that efficiently maps large-scale individual cells to their precise spatial locations by integrating single-cell and spatial data through a divide-and-conquer strategy. Analysis of both simulated and real datasets shows that CMAP performs effectively and is adaptable across diverse data types and sequencing platforms. Particularly, CMAP handles scenarios well where discrepancies exist between single-cell and spatial transcriptomics data. Our findings underscore CMAP’s capacity to endow single-cells with exact spatial coordinates, facilitating the dissection of nuanced spatial-organ-specific endothelial cell heterogeneity, as well as the intricate cancer immune microenvironments that elude conventional single-cell or spatial data analysis.https://doi.org/10.1038/s41467-025-61667-4 |
| spellingShingle | Jincan Ke Jian Xu Jia Liu Yumeng Yang Chenkai Guo Bingbing Xie Guizhong Cui Guangdun Peng Shengbao Suo High-resolution mapping of single cells in spatial context Nature Communications |
| title | High-resolution mapping of single cells in spatial context |
| title_full | High-resolution mapping of single cells in spatial context |
| title_fullStr | High-resolution mapping of single cells in spatial context |
| title_full_unstemmed | High-resolution mapping of single cells in spatial context |
| title_short | High-resolution mapping of single cells in spatial context |
| title_sort | high resolution mapping of single cells in spatial context |
| url | https://doi.org/10.1038/s41467-025-61667-4 |
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