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Guo L, Xie C, Miao R, Xu J, Xu X, Fang J, Wang X, Liu W, Liao X, Wang J, Dong J, Cai Z. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging. Anal Chem 2024; 96:3829-3836. [PMID: 38377545 PMCID: PMC10918617 DOI: 10.1021/acs.analchem.3c05002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/27/2024] [Accepted: 02/03/2024] [Indexed: 02/22/2024]
Abstract
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.
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Affiliation(s)
- Lei Guo
- Interdisciplinary
Institute of Medical Engineering, Fuzhou
University, Fuzhou 350108, China
| | - Chengyi Xie
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
- Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Rui Miao
- Department
of Electronic Science, National Institute for Data Science in Health
and Medicine, Xiamen University, Xiamen 361005, China
| | - Jingjing Xu
- Department
of Electronic Science, National Institute for Data Science in Health
and Medicine, Xiamen University, Xiamen 361005, China
| | - Xiangnan Xu
- School
of Business and Economics, Humboldt-Universitat
zu Berlin, Berlin 10099, Germany
| | - Jiacheng Fang
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Xiaoxiao Wang
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Wuping Liu
- International
Joint Research Center for Medical Metabolomics, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
| | - Xiangwen Liao
- Interdisciplinary
Institute of Medical Engineering, Fuzhou
University, Fuzhou 350108, China
| | - Jianing Wang
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Jiyang Dong
- Department
of Electronic Science, National Institute for Data Science in Health
and Medicine, Xiamen University, Xiamen 361005, China
| | - Zongwei Cai
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
- Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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