ArticleNature communications2024
Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Genome-scale molecular profiling from routine histopathology with a multimodal pathology foundation model.Research square · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture.Nature communications · 2026Article
- Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs.Nature aging · 2026Article
- Spatial architecture of development and disease.Nature reviews. Genetics · 2026Review
- Biophysical simulation enables segmentation and nervous system atlas mapping for image first spatial omics.NPJ systems biology and applications · 2025Article
- Self-supervised learning for data augmentation in histopathology image segmentation.Scientific reports · 2025Article
- A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images.Nature communications · 2025Article
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
- Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.Nature communications · 2025Article
- A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images.Nature communications · 2025Article
- Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.Nature communications · 2025Article
- Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data.Briefings in bioinformatics · 2025Article
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.Nature communications · 2024Article
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6 authors.
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Abstract
As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, we can automatically segment tissues into their constituent tissue substructures and pathology proportions across thousands of whole slide images, outperforming other self-supervised methods (43% increase in silhouette score). Additionally, we can detect and quantify histological pathologies present, such as arterial calcification (AUROC = 0.93) and identify missing calcification diagnoses. Finally, to link gene expression to tissue morphology, we introduce RNAPath, a set of models trained on 23 tissue types that can predict and spatially localise individual RNA expression levels directly from H&E histology (mean genes significantly regressed = 5156, FDR 1%). We validate RNAPath spatial predictions with matched ground truth immunohistochemistry for several well characterised control genes, recapitulating their known spatial specificity. Together, these results demonstrate how self-supervised machine learning when applied to vast histological archives allows researchers to answer questions about tissue pathology, its spatial organisation and the interplay between morphological tissue variability and gene expression.
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