ArticleNature methods2025
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Bringing human early embryo development to clinical interpretation: HESTA as an evolving reference.Clinical and translational medicine · 2026Article
- Review
- HisToSpatialCNV: an interpretable deep learning method predicting spatial copy number variations from histopathology images.Nature biomedical engineering · 2026Article
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- SpatialCell AI achieves reference-free single-cell resolution from spot-based spatial transcriptomics through morphology-guided enhancement.Scientific reports · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expression.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Interpretable Differential Abundance Signature (iDAS).Small methods · 2026Article
- Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.American journal of clinical and experimental immunology · 2026Article
- Applications and development of in situ nucleic acid visualization techniques.Frontiers in bioengineering and biotechnology · 2026Review
- Reprogramming the immunosuppressive breast cancer microenvironment: integrating cellular, metabolic, and stromal targets for rational immunotherapy.Frontiers in immunology · 2026Review
- Large Language Models for Accessible Reporting of Bioinformatics Analyses in Interdisciplinary Contexts.bioRxiv : the preprint server for biology · 2025Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images.bioRxiv : the preprint server for biology · 2025Article
- Bridging histology and spatial gene expression across scales.Nature methods · 2025Article
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Authors and funding
9 authors.
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Abstract
The increased use of spatially resolved transcriptomics provides new biological insights into disease mechanisms. However, the high cost and complexity of these methods are barriers to broader application. Consequently, methods have been created to predict spot-based gene expression from routinely collected histology images. Recent benchmarking showed that current methodologies have limited accuracy and spatial resolution, constraining translational capacity. Here, we introduce GHIST, a deep learning-based framework that predicts spatial gene expression at single-cell resolution by leveraging subcellular spatial transcriptomics and synergistic relationships between multiple layers of biological information. We validated GHIST using public datasets and The Cancer Genome Atlas data, demonstrating its flexibility across different spatial resolutions and superior performance. Our results underscore the utility of in silico generation of single-cell spatial gene expression measurements and the capacity to enrich existing datasets with a spatially resolved omics modality, paving the way for scalable multi-omics analysis and biomarker identification.
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