High-resolution mapping of single cells in spatial context

Jincan Ke, Jian Xu, Jia Liu, Yumeng Yang, Chenkai Guo, Bingbing Xie, Guizhong Cui, Guangdun Peng () and Shengbao Suo ()
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Jincan Ke: Chinese Academy of Sciences
Jian Xu: Guangzhou National Laboratory
Jia Liu: Guangzhou National Laboratory
Yumeng Yang: Guangzhou National Laboratory
Chenkai Guo: Chinese Academy of Sciences
Bingbing Xie: Guangzhou National Laboratory
Guizhong Cui: Guangzhou National Laboratory
Guangdun Peng: Chinese Academy of Sciences
Shengbao Suo: Guangzhou National Laboratory

Nature Communications, 2025, vol. 16, issue 1, 1-21

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-61667-4

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