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SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging

Author

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  • Rui Chen

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Jiasu Xu

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Boqian Wang

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Yi Ding

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Aynur Abdulla

    (Shanghai Jiao Tong University)

  • Yiyang Li

    (Shanghai Jiao Tong University)

  • Lai Jiang

    (Shanghai Jiao Tong University)

  • Xianting Ding

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

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.

Suggested Citation

  • Rui Chen & Jiasu Xu & Boqian Wang & Yi Ding & Aynur Abdulla & Yiyang Li & Lai Jiang & Xianting Ding, 2024. "SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46989-z
    DOI: 10.1038/s41467-024-46989-z
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