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Publications

Journal Article Ppublish 1 citation

PubMed: 39778027 DOI: 10.1002/advs.202410840

Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI.

Adv Sci (Weinh) 12(8):e2410840

Authors: Lei Guo, Peisi Xie, Xionghui Shen, Thomas Ka Yam Lam, Lingli Deng, Chengyi Xie, Xiangnan Xu, Chris Kong Chu Wong, Jingjing Xu, Jiacheng Fang, Xiaoxiao Wang, Zhuang Xiong, Shangyi Luo, Jianing Wang, Jiyang Dong, Zongwei Cai

Affiliations

  • Interdisciplinary Institute for Medical Engineering, Fuzhou University, Fuzhou, 350108, China.
  • State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, 999077, China.
  • Department of Electronic Science, Xiamen University, Xiamen, 361005, China.
  • School of Information Engineering, East China University of Technology, Nanchang, 330013, China.
  • School of Business and Economics, Humboldt-Universitat zu Berlin, 10099, Berlin, Germany.
  • Department of Biology, Hong Kong Baptist University, Hong Kong, SAR, 999077, China.
  • State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, SAR, 999077, China. College of Science, Eastern Institute of Technology, Ningbo, Ningbo, 315000, China.

Abstract

Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the biological tissue into distinct regions with unique characteristics. However, current methods struggle due to the insufficient incorporation of biological context and high computational demand. To address these challenges, a novel deep learning-based approach is proposed, GraphMSI, which integrates metabolic profiles with spatial information to enhance MSI data analysis. Our comparative results demonstrate GraphMSI outperforms commonly used segmentation methods in both visual inspection and quantitative evaluation. Moreover, GraphMSI can incorporate partial or coarse biological contexts to improve segmentation results and enable more effective three-dimensional MSI segmentation with reduced computational requirements. These are facilitated by two optional enhanced modes: scribble-interactive and knowledge-transfer. Numerous results demonstrate the robustness of these two modes, ensuring that GraphMSI consistently retains its capability to identify biologically relevant sub-regions in complex practical applications. It is anticipated that GraphMSI will become a powerful tool for spatial heterogeneity analysis in MSI data.

MeSH

Biomedical Research, Deep Learning

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