DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging

Lei Guo, Chengyi Xie, Rui Miao, Jingjing Xu, Xiangnan Xu, Jiacheng Fang, Xiaoxiao Wang, Wuping Liu, Xiangwen Liao*, Jianing Wang*, Jiyang Dong*, Zongwei Cai*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

Original languageEnglish
Pages (from-to)3829–3836
Number of pages8
JournalAnalytical Chemistry
Issue number9
Early online date20 Feb 2024
Publication statusPublished - 5 Mar 2024

Scopus Subject Areas

  • Analytical Chemistry


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