ArticleNature communications2025
High-resolution mapping of single cells in spatial context.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Single-cell omics in tumor lymph node metastasis: mechanisms and therapeutic implications.Molecular cancer · 2026Review
- Region-aware bridge modeling enables interpretable mesoscale representation of spatial transcriptomic tissue sections.Bioinformatics advances · 2026Article
- Neurovascular Signaling at the Gliovascular Interface: From Flow Regulation to Cognitive Energy Coupling.International journal of molecular sciences · 2025Review
- High-resolution mapping of single cells in spatial context.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.