DPM: A Deep Learning and Optimal Transport Framework for Cost-Effective Spatial Metabolomics.

Journal: Analytical chemistry
Published Date: Jan 15, 2026

Abstract

Mass spectrometry imaging (MSI) is a powerful technology in spatial metabolomics that enables the in situ detection and distribution analysis of metabolites in tissue sections. However, the high cost associated with high-resolution and multislice MSI acquisition remains a major limitation. Here, we introduce DeepPathMetabol (DPM), a deep learning-enhanced framework based on optimal transport theory, which accurately predicts spatial metabolite distributions in an MSI section using data from an adjacent section through an optimized mapping strategy. DPM achieves superior alignment and prediction accuracy, outperforming conventional feature similarity-based methods such as those using Euclidean or kernel-based metrics both with and without spatial distance weighting. We further demonstrated that the DPM framework can effectively enhance MSI resolution, providing a powerful tool for cost-effective and high-precision spatial metabolomics research. This approach also shows promising potential for extension to spatial transcriptomics. Collectively, our work establishes histology-facilitated MSI-to-MSI prediction as a versatile strategy for spatial biology research. DPM is open-source and available at https://github.com/LinShuhaiLAB/DeepPathMetabol.

Authors

  • Bo Yao
    Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
  • Longfeng Yang
    State Key Laboratory of Cellular Stress Biology, State Key Laboratory of Vaccines for Infectious Diseases, Xiang'An Biomedicine Laboratory, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.
  • Chi Zhang
    Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yuanyuan Lv
  • Chenkun Yang
    Yazhouwan National Laboratory (YNL), Sanya 572025, China.
  • Li-Jun Di
    Department of Biological Sciences, Faculty of Health Sciences, University of Macau, Macau, China.
  • Jie Luo
  • Shu-Hai Lin
    State Key Laboratory of Cellular Stress Biology, Institute of Artificial Intelligence, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.

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