Journal Article

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

Ding, Yi, Xu, Jiasu, Wang, Boqian, Ding, Xianting, Abdulla, Aynur, Li, Yiyang, Chen, Rui, Jiang, Lai

2024

Overview

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. Imaging mass cytometry (IMC) is a powerful single-cell resolution platform for targeted spatial proteomics, but it can be constrained by imaging noise and resolution. Here, the authors propose SpiDe-Sr, a super-resolution network embedded with a denoising module for IMC spatial resolution enhancement.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 101/58 / 631/114/1305 / 631/114/1564 / 631/1647/2067 / 631/1647/296 / 631/67/327 / 82/79 / Animals / Bacteria / Breast cancer / Breast Neoplasms - diagnostic imaging / Carcinogenesis / Carcinogens / Cluster Analysis / Clustering / Cytometry / Diagnostic Imaging / Female / Fluorescence microscopy / Gram-negative bacteria / Human tissues / Humanities and Social Sciences / Humans / Image Processing, Computer-Assisted - methods / Image resolution / Image segmentation / Immunology / Medical imaging / Mice / Microscopy / Modules / multidisciplinary / Noise reduction / Proteomics / Science / Science (multidisciplinary) / Signal to noise ratio / Spatial discrimination / Spatial resolution / Tumor Microenvironment