Beyond Co-Localization: DeepCoEx Learns Exclusion-Aware Spatial Representations in Mass Spectrometry Imaging via Dual-Contrastive Learning

Abstract Mass spectrometry imaging (MSI) provides spatially resolved molecular profiles, yet the existing computational workflows are heavily biased toward co-localization and remain limited in characterizing exclusion-like spatial relationships, especially when reliable negative labels are scarce. To address this challenge, we present DeepCoEx (Deep Co-localization and Exclusion), a self-supervised framework that learns exclusion-aware spatial representations through dual-contrastive learning for spatial metabolomics analysis. Instead of relying on large collections of annotated negative ion pairs, DeepCoEx embeds positive spatial consistency and exclusion-aware separation into a unified representation space. This design enables the model to preserve spatially coherent ion patterns while improving the separability of spatially complementary distributions. Compared with conventional similarity metrics and dimensionality reduction baselines, DeepCoEx provides more consistent and interpretable retrieval of candidate negative spatial associations. In representative control and PM2.5-exposed rat brains, the framework reveals localized spatial redistribution patterns and brainstem-related complementary ion associations that are not readily captured by conventional correlation-based analysis. In barley seeds, the learned embeddings help resolve chemically related ion species, including protonated and sodiated adducts of phosphatidylcholine species and glycosylated hordatine derivatives. Furthermore, DeepCoEx supports subregion-specific network construction, enabling exploratory analysis of localized molecular association patterns under different tissue or treatment contexts. By establishing an exclusion-aware representation learning strategy for MSI, DeepCoEx expands spatial metabolomics beyond co-localization and provides a practical workflow for analyzing complementary molecular organization in complex tissues.

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Publication Details

Journal
Analytical Chemistry
Published
2026-10-16
DOI
https://doi.org/10.1021/acs.analchem.6c02338
Primary Topic
Mass Spectrometry Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Beyond Co-Localization: DeepCoEx Learns Exclusion-Aware Spatial Representations in Mass Spectrometry Imaging via Dual-Contrastive Learning

Jiyang Dong, Xin Diao, Lei Guo, Zongwei Cai et al.
Analytical Chemistry
Mass Spectrometry Techniques and Applications
article

Beyond Co-Localization: DeepCoEx Learns Exclusion-Aware Spatial Representations in Mass Spectrometry Imaging via Dual-Contrastive Learning

Jiyang Dong, Xin Diao, Lei Guo, Zongwei Cai, Thomas Ka‐Yam Lam, Jingjing Xu, Shuang Gao, Lingli Deng, Weiji Chen, Yusong Zhang, Siyang Liu
article en

Abstract

Abstract Mass spectrometry imaging (MSI) provides spatially resolved molecular profiles, yet the existing computational workflows are heavily biased toward co-localization and remain limited in characterizing exclusion-like spatial relationships, especially when reliable negative labels are scarce. To address this challenge, we present DeepCoEx (Deep Co-localization and Exclusion), a self-supervised framework that learns exclusion-aware spatial representations through dual-contrastive learning for spatial metabolomics analysis. Instead of relying on large collections of annotated negative ion pairs, DeepCoEx embeds positive spatial consistency and exclusion-aware separation into a unified representation space. This design enables the model to preserve spatially coherent ion patterns while improving the separability of spatially complementary distributions. Compared with conventional similarity metrics and dimensionality reduction baselines, DeepCoEx provides more consistent and interpretable retrieval of candidate negative spatial associations. In representative control and PM2.5-exposed rat brains, the framework reveals localized spatial redistribution patterns and brainstem-related complementary ion associations that are not readily captured by conventional correlation-based analysis. In barley seeds, the learned embeddings help resolve chemically related ion species, including protonated and sodiated adducts of phosphatidylcholine species and glycosylated hordatine derivatives. Furthermore, DeepCoEx supports subregion-specific network construction, enabling exploratory analysis of localized molecular association patterns under different tissue or treatment contexts. By establishing an exclusion-aware representation learning strategy for MSI, DeepCoEx expands spatial metabolomics beyond co-localization and provides a practical workflow for analyzing complementary molecular organization in complex tissues.

Analytical Chemistry
Eastern Institute of Technology (NZ), Fujian Medical University (CN), Hong Kong Baptist University (HK), Xiamen University (CN), East China University of Technology (CN), Fuzhou University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 27%
Mass Spectrometry Techniques and Applications
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