ReviewNature communications2025
Combining spatial transcriptomics with tissue morphology.
Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Understanding heterogeneity in the pathogenesis and drug responses of ulcerative colitis through single-cell and spatial transcriptomics.Frontiers in immunology · 2026Pooled it
- Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.Journal for immunotherapy of cancer · 2025Trial
- Molecular histomorphometry and the emerging role of transcriptomics in celiac disease diagnosis and therapeutic trials.Annals of medicine · 2026Review
- Reclaiming Anatomy as Method: From Morphological Reasoning to Clinical Relevance.Clinical anatomy (New York, N.Y.) · 2026Review
- STAT3D as a new image-tailored Xenium-based spatial transcriptomics tool for accurately dissecting the tumor microenvironment.Nucleic acids research · 2026Article
- Integrative analysis of single-cell multi-omics reveals heterogeneity in piriform cortex principal neurons and their selective vulnerability in Alzheimer's disease.Clinical and translational medicine · 2026Article
- Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning.PLoS computational biology · 2026Article
- Artificial Intelligence-Powered Histopathology in Stem Cell Research: Bridging Morphology, Function, and Omics.Current issues in molecular biology · 2026Review
- Genome-scale molecular profiling from routine histopathology with a multimodal pathology foundation model.Research square · 2026Article
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Single-cell Technologies in Atherosclerosis: Uncovering Cellular Heterogeneity, Mechanisms, and Therapeutic Opportunities.Current atherosclerosis reports · 2026Review
- Genome integrity, somatic mutation, and the N-of-1 imperative in aging research.Biogerontology · 2026Review
- Decoding cellular population dynamics through mechanistic modelling and statistical data analysis.NPJ systems biology and applications · 2026Review
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Epigenetic and Transcriptomic Pathways Underlying Animal Models of Cognitive and Psychiatric Disorders: A Scoping Review.Current issues in molecular biology · 2026Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research.Genomics, proteomics & bioinformatics · 2026Review
- Strategies to control cellular spatial organization in microphysiological systems.Microsystems & nanoengineering · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics has transformed our understanding of tissue architecture by preserving the spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. This review introduces a framework for categorizing methods that combine spatial transcriptomics with tissue morphology, focusing on either translating or integrating morphological features into spatial transcriptomics. Translation involves using morphology to predict gene expression, creating super-resolution maps or inferring genetic information from H&E-stained samples. Integration enriches spatial transcriptomics by identifying morphological features that complement gene expression. We also explore learning strategies and future directions for this emerging field.
Indexed as
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What OpenQuestion 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 OpenQuestion graph.