The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors
Open accessAbstract
Recent developments in spatial transcriptomics (ST) technologies have enabled the profiling of transcriptome-wide gene expression while retaining the location information of measured genes within tissues. Moreover, the corresponding high-resolution hematoxylin and eosin-stained histology images are readily available for the ST tissue sections. Since histology images are easy to obtain, it is desirable to leverage information learned from ST to predict gene expression for tissue sections where only histology images are available. Here we present HisToGene, a deep learning model for gene expression prediction from histology images. To account for the spatial dependency of measured spots, HisToGene adopts Vision Transformer, a state-of-the-art method for image recognition. The well-trained HisToGene model can also predict super-resolution gene expression. Through evaluations on 32 HER2+ breast cancer samples with 9,612 spots and 785 genes, we show that HisToGene accurately predicts gene expression and outperforms ST-Net both in gene expression prediction and clustering tissue regions using the predicted expression. We further show that the predicted super-resolution gene expression also leads to higher clustering accuracy than observed gene expression. Gene expression predicted from HisToGene enables researchers to generate virtual transcriptomics data at scale and can help elucidate the molecular signatures of tissues.
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“Several studies have explored predicting gene expression from H&E images using AI models59–62.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [59]
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Cites
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“However, this bias has been recently alleviated by Vision Transformer (17), which internally utilizes self-attention mechanism for divided image patches and has shown strong performance on many tasks, including medical image classification, segmentation (18), and registration (19).”
BackgroundVisualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
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“cutaneous squamous cell carcinoma (cSCC) dataset (37), which includes 12 tissue sections 421 obtained from 4 patients, with each patient having 3 sections.”
BackgroundHu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2021) · Nature Methods · doi:10.1038/s41592-021-01255-8 · Cited by 1,113
Self-citationHigh-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue
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Research works printed but not linked: 1
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Sorted by citations
Zeng, Y., Wei, Z., Yu, W., Yin, R., Yuan, Y., Li, B., Tang, Z., Lu, Y., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac297 · Cited by 211
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Zeng, Z., Li, Y., Li, Y., & Luo, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02653-7 · Cited by 186
THItoGene: a deep learning method for predicting spatial transcriptomics from histological images
Jia, Y., Liu, J., Chen, L., Zhao, T., & Wang, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad464 · Cited by 130
A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118
“As an application of Tangram with OmiCLIP embeddings instead of gene expression data, Loki Decompose applied Tangram’s nonconvex optimization algorithm 47 to deconvolute the OmiCLIP embeddings of an H&E image patch or the embeddings of a Visium spot’s transcriptomic profile rather than raw gene…”
Wang, B., Luo, J., Liu, Y., Shi, W., Xiong, Z., Shen, C., & Long, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad262 · Cited by 91
Metadata
- Authors
- Pang, M., Su, K., & Li, M.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- University of Pennsylvania (US)
- Corresponding author
- Su, Kenong
- Li, Mingyao
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2021-11-28
- OpenAlex
- W3216067893
- Cited by
- 130
- References
- 37
- Citations by year
- 2026: 35 · 2025: 49 · 2024: 28 · 2023: 13 · 2022: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- spatial transcriptomics, HER2-positive breast cancer, vision transformer, tumor molecular signatures
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