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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Main Authors: Jincan Ke, Jian Xu, Jia Liu, Yumeng Yang, Chenkai Guo, Bingbing Xie, Guizhong Cui, Guangdun Peng, Shengbao Suo
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
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.
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institution Kabale University
issn 2041-1723
language English
publishDate 2025-07-01
publisher Nature Portfolio
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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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AT bingbingxie highresolutionmappingofsinglecellsinspatialcontext
AT guizhongcui highresolutionmappingofsinglecellsinspatialcontext
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