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

Rui Chen, Jiasu Xu, Boqian Wang, Yi Ding, Aynur Abdulla, Yiyang Li, Lai Jiang and Xianting Ding ()
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Rui Chen: Shanghai Jiao Tong University
Jiasu Xu: Shanghai Jiao Tong University
Boqian Wang: Shanghai Jiao Tong University
Yi Ding: 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

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46989-z

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