ArticleScientific reports2022
Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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55 citing papers in PubMed, 99 citations in OpenAlex.
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- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- SpaBiT: enhancing spatial transcriptomics resolution via bidirectional attention transformers.Bioinformatics (Oxford, England) · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Simultaneous spatial transcriptomics and morphology profiling as tools to explore how microglia change with age.Nature aging · 2026Article
- Hist2Cell: Deciphering fine-grained cellular architectures from histology images.Cell genomics · 2026Article
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- Spatial architecture of development and disease.Nature reviews. Genetics · 2026Review
- Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data.Nature communications · 2026Article
- sCellST predicts single-cell gene expression from H& E images.Nature communications · 2026Article
- Performance of deep learning methods for spatial gene expression prediction from histology images: a comprehensive assessment.Bioinformatics advances · 2026Review
- Spatially resolving cancer: from cell states to therapy.Trends in cancer · 2026Review
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- A deep learning-based multiscale integration of spatial omics with tumor morphology.Nature communications · 2025Article
- SciSt: single-cell reference-informed spatial gene expression prediction from pathological images.Briefings in bioinformatics · 2025Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- FmH2ST: foundation model-based spatial transcriptomics generation from histological images.Nucleic acids research · 2025Article
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5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expression in unmeasured regions and tissues can enhance biologists' histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE, and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region difficult to identify using raw spatial transcriptome data alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.
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