Super-resolved spatial transcriptomics by deep data fusion
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Abstract
Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone.
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Researchers have utilized deep learning to leverage histopathology images to predict spatial gene expression from these images alone, pointing to morphological features not captured by human experts that could potentially enhance utility and lower costs.
“Of note, researchers have utilized deep learning to leverage histopathology images to predict spatial gene expression from these images alone, pointing to morphological features in these images not captured by human experts that could potentially enhance the utility and lower the costs of this technology 25,26 .”
From Multimodal biomedical AI (2022) · cites this paper as [26]
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Scientific discovery in the age of artificial intelligence.
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“Deep convolutional methods, which utilize algorithmic advances such as spectral deconvolution 54,55 , flexible sparsity 52 and generative capability 56 , can transform poor spatiotemporally resolved measurements into high-quality, super-resolved and structured images.”
MethodExploring tissue architecture using spatial transcriptomics
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Methods and applications for single-cell and spatial multi-omics
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BackgroundIntegrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
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Self-citationSpatially resolved transcriptomes—next generation tools for tissue exploration
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Self-citationStatistical and machine learning methods for spatially resolved transcriptomics data analysis
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- Authors
- Bergenstråhle, L., He, B., Bergenstråhle, J., Abalo, X., Mirzazadeh, R., Thrane, K., Ji, A. L., Andersson, A., Larsson, L., Stakenborg, N., Boeckxstaens, G., Khavari, P., Zou, J., Lundeberg, J., & Maaskola, J.
- Affiliations
- Science for Life Laboratory (SE)
- Stanford University (US)
- Stanford Cancer Institute (US)
- KU Leuven (BE)
- Stockholm University (SE)
- Corresponding author
- Lundeberg, Joakim
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2021-11-29
- Volume
- 40
- Issue
- 4
- Pages
- 476-479
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W3217269355
- Cited by
- 188
- References
- 49
- Citations by year
- 2026: 38 · 2025: 44 · 2024: 43 · 2023: 25 · 2022: 22 · 2021: 14 · 2020: 2
- FWCI
- 8.63
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Funders
- Knut och Alice Wallenbergs Stiftelse (NA)
- Familjen Erling-Perssons Stiftelse (NA)
- Cancerfonden (NA)
- Stiftelsen för Strategisk Forskning (NA)
- Vetenskapsrådet (NA)
- Leona M. and Harry B. Helmsley Charitable Trust (NA)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial transcriptomics, super-resolution, histological images, spatial gene expression, deep generative models, tissue sections
- MeSH terms
- Transcriptome
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