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

IF 7.3 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Jingjing Xu,Xin Diao,Siyang Liu,Thomas Ka-Yam Lam,Yusong Zhang,Shuang Gao,Lingli Deng,Weiji Chen,Lei Guo,Zongwei Cai,Jiyang Dong
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引用次数: 0

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.
超越共定位:DeepCoEx通过双对比学习学习质谱成像中排斥感知的空间表征
质谱成像(MSI)提供了空间分辨的分子图谱,但现有的计算工作流程严重偏向于共定位,并且在表征排外的空间关系方面仍然有限,特别是在可靠的负标签稀缺的情况下。为了解决这一挑战,我们提出了DeepCoEx(深度共定位和排除),这是一个自我监督框架,通过双对比学习来学习空间代谢组学分析的排斥感知空间表征。DeepCoEx不依赖于大量带注释的负离子对,而是将正空间一致性和排斥性分离嵌入到统一的表示空间中。这种设计使模型能够保持空间相干的离子模式,同时提高空间互补分布的可分离性。与传统的相似性度量和降维基线相比,DeepCoEx提供了更一致和可解释的候选负空间关联检索。在具有代表性的对照和暴露于pm2.5的大鼠大脑中,该框架揭示了局部空间再分配模式和脑干相关的互补离子关联,这些是传统的基于相关性的分析不易捕获的。在大麦种子中,学习到的包埋有助于解析化学相关的离子种类,包括磷脂酰胆碱的质子化和钠化加合物和糖基化的胆碱衍生物。此外,DeepCoEx支持亚区域特异性网络构建,可以在不同组织或治疗背景下对局部分子关联模式进行探索性分析。DeepCoEx为MSI建立了排异感知表征学习策略,将空间代谢组学扩展到共定位之外,并为分析复杂组织中的互补分子组织提供了实用的工作流程。
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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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