ArticleNature genetics2025
Quantitative characterization of tissue states using multiomics and ecological spatial analysis.
Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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.
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Who cites it
15 citing papers in PubMed.
- Flexible high-resolution ECM micropatterning.Nature protocols · 2026Review
- ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.Bioinformatics (Oxford, England) · 2026Article
- Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data.Nature methods · 2026Article
- Epstein-Barr virus orchestrates spatial reorganization and immunomodulation in the classic Hodgkin lymphoma tumor microenvironment.Cell reports. Medicine · 2026Article
- An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.Cell press blue · 2026Article
- Comparison and optimization of cellular neighbor preference methods for quantitative tissue analysis.Nature communications · 2026Article
- Uncovering Immune Niches in Health and Disease Using Spatial Transcriptomics.European journal of immunology · 2026Review
- Cohort-Scale Spatial Autocorrelation for Tumor Prediction in Mid-Infrared Pathology and Spatial Biomarker Discovery Using MALDI Imaging Lipidomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics.bioRxiv : the preprint server for biology · 2025Article
- Histopathology-based Spatial Profiling of Immune and Molecular Features Predicts Cancer risk in Barrett's Esophagus.medRxiv : the preprint server for health sciences · 2025Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Spatially organized inflammatory myeloid-CD8bioRxiv : the preprint server for biology · 2025Article
- Myeloid-driven immunosuppression in head and neck cancer: single-cell ATAC/RNA and spatial transcriptomic perspectives.Frontiers in oncology · 2025Review
- Immune and non-immune cell fencing of tumor cells is a widespread and functionally relevant spatial pattern in solid cancers.Computational and structural biotechnology journal · 2025Article
- Magnetically Actuated Soft Electrodes for Multisite Bioelectrical Monitoring of Ex Vivo Tissues.Cyborg and bionic systems (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The spatial organization of cells in tissues underlies biological function, and recent advances in spatial profiling technologies have enhanced our ability to analyze such arrangements to study biological processes and disease progression. We propose MESA (multiomics and ecological spatial analysis), a framework drawing inspiration from ecological concepts to delineate functional and spatial shifts across tissue states. MESA introduces metrics to systematically quantify spatial diversity and identify hot spots, linking spatial patterns to phenotypic outcomes, including disease progression. Furthermore, MESA integrates spatial and single-cell multiomics data to facilitate an in-depth, molecular understanding of cellular neighborhoods and their spatial interactions within tissue microenvironments. Applying MESA to diverse datasets demonstrates additional insights it brings over prior methods, including newly identified spatial structures and key cell populations linked to disease states. Available as a Python package, MESA offers a versatile framework for quantitative decoding of tissue architectures in spatial omics across health and disease.
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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.