ArticleNature communications2024
SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Spatial proteomics in precision medicine: technologies, bioinformatics, and translational applications.Precision clinical medicine · 2026Review
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- Spatial proteomics: technical evolution and applications in cancer research.Frontiers in cell and developmental biology · 2026Review
- High-resolution imaging mass cytometry to map subcellular structures.Nature methods · 2025Article
- Antibody-oligonucleotide conjugates for spatial proteomics: principles, applications, and challenges.Acta biochimica et biophysica Sinica · 2025Article
- De-MSI: A Deep Learning-Based Data Denoising Method to Enhance Mass Spectrometry Imaging by Leveraging the Chemical Prior Knowledge.Analytical chemistry · 2025Article
- Spatial omics technology potentially promotes the progress of tumor immunotherapy.British journal of cancer · 2025Review
- Cellular mechanical memory: a potential tool for mesenchymal stem cell-based therapy.Stem cell research & therapy · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Spatial proteomics elucidates cellular biochemical changes with unprecedented topological level. Imaging mass cytometry (IMC) is a high-dimensional single-cell resolution platform for targeted spatial proteomics. However, the precision of subsequent clinical analysis is constrained by imaging noise and resolution. Here, we propose SpiDe-Sr, a super-resolution network embedded with a denoising module for IMC spatial resolution enhancement. SpiDe-Sr effectively resists noise and improves resolution by 4 times. We demonstrate SpiDe-Sr respectively with cells, mouse and human tissues, resulting 18.95%/27.27%/21.16% increase in peak signal-to-noise ratio and 15.95%/31.63%/15.52% increase in cell extraction accuracy. We further apply SpiDe-Sr to study the tumor microenvironment of a 20-patient clinical breast cancer cohort with 269,556 single cells, and discover the invasion of Gram-negative bacteria is positively correlated with carcinogenesis markers and negatively correlated with immunological markers. Additionally, SpiDe-Sr is also compatible with fluorescence microscopy imaging, suggesting SpiDe-Sr an alternative tool for microscopy image super-resolution.
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Registered trials
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