ArticleNature communications2026
Ultra-precision deconvolution of spatial transcriptomics decodes immune heterogeneity and fate-defining programs in tissues.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- IDEAL-Age: an interpretable deep learning framework for single-cell resolution profiling of immunological aging.Genome biology · 2026Article
- Ultra-precision deconvolution of spatial transcriptomics decodes immune heterogeneity and fate-defining programs in tissues.Nature communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
Abstract
Elucidating the spatial organization and functional specialization of immune cells within complex tissues remains challenging. We present UCASpatial, an ultra-precision spatial transcriptomics deconvolution algorithm utilizing entropy-based weighting to accurately map cell subpopulations. Benchmarking confirms its superiority in identifying low-abundant cell subpopulations and distinguishing transcriptionally heterogeneous cell subpopulations. Applying UCASpatial to human colorectal cancer, we reveal that chromosome 20q gain in individual cancer clones orchestrates a T cell-excluded microenvironment, associated with HERV-H silencing and impaired type I interferon responses. In murine wound healing models, we reveal spatiotemporal dynamics distinguishing scarring from regenerative phenotypes. Specifically, we identify a pro-fibrotic community comprising Igfbp5
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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.