ReviewClinical & translational immunology2026
Spatial omics for profiling the dynamic tumor microenvironment.
Review in Clinical & translational immunology, 2026. 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
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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
4 citing papers in PubMed.
- An Integrated Spatial Multi-Omics Workflow for Sequential RNA and Protein Profiling in FFPE Tumor Tissue.Cancer research communications · 2026Article
- Cell type-specific pharmacological modulation of the neuro-immune axis: the role of ADRB2 signaling in reshaping the tumor ecosystem.Frontiers in pharmacology · 2026Review
- From peripheral blood to tumor microenvironment: spatial dimension deficiency and paradigm reconstruction in immunotherapy biomarker research.Frontiers in immunology · 2026Review
- Aminoacyl-tRNA synthetases in tumor immunity: canonical translation, source-resolved immune circuits and therapeutic opportunities.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics (ST) and spatial proteomics (SP) have revolutionised our ability to map RNA and protein distributions within intact tissues, shedding new light on the dynamic interactions that drive physiological processes in healthy and diseased tissues. We discuss how the latest ST and SP technologies, large public data resources and advanced computational pipelines can be applied to study the tumor microenvironment (TME), focussing on the interactions within the TME. We also highlight how these developments have enabled the in-depth spatial characterisation of tumors and their TME across the continuum of cancer progression, from initiation to metastasis. Despite these advances, major gaps persist in cross-platform integration, data standardisation and computational scalability for high-plex single-cell datasets. The integration of artificial intelligence (AI) holds great promise for biological and translational applications but requires standardised workflows, cost-effective pipelines, rigorous pre-clinical and clinical validation, and improved interpretability of AI models. Additional cross-disciplinary development of explainable, scalable tools for TME analysis of cellular interactions and disease progression will be essential to integrate spatial omics into daily precision cancer medicine.
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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 OpenQuestion graph.