OffsampleAI: artificial intelligence approach to recognize off-sample mass spectrometry images.
Katja Ovchinnikova1, Vitaly Kovalev1, Lachlan Stuart1
1Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
BMC Bioinformatics
|April 5, 2020
Summary
Artificial intelligence now automates the identification of off-sample ions in imaging mass spectrometry (imaging MS) data. This approach enhances spatial metabolomics by improving statistical analysis and metabolite identification in biological and medical research.
Area of Science:
- Spatial metabolomics
- Biomedical research
- Mass spectrometry imaging
Background:
- Imaging mass spectrometry (imaging MS) is crucial for spatial metabolomics in biology and medicine.
- Off-sample ions from matrix application (e.g., MALDI) contaminate imaging MS data.
- Contamination hinders statistical analysis, metabolite identification, and downstream applications, lacking automated solutions.
Purpose of the Study:
- To develop an automated artificial intelligence (AI) approach for recognizing and mitigating off-sample ion images in imaging MS.
- To improve the accuracy and reliability of spatial metabolomics data analysis.
Main Methods:
- Created a gold standard dataset of 23,238 expert-tagged ion images from 87 public METASPACE datasets.
- Developed and evaluated machine learning and deep learning models, including residual deep learning, spatio-molecular biclustering, and molecular co-localization.
- Investigated off-sample images related to 2,5-dihydroxybenzoic acid (DHB) matrix clusters.
Main Results:
- AI models achieved high agreement with expert judgments: residual deep learning (F1-score 0.97), semi-automated spatio-molecular biclustering (F1-score 0.96), and molecular co-localization (F1-score 0.90).
- Successfully characterized properties of matrix clusters associated with DHB, a common MALDI matrix.
- Demonstrated the effectiveness of AI in addressing data quality issues in imaging MS.
Conclusions:
- AI approaches, powered by open-access data and machine learning, offer novel solutions for persistent challenges in imaging MS.
- The developed methods enhance the reliability of spatial metabolomics data, facilitating more accurate biological and medical discoveries.
- This work paves the way for automated data quality control in imaging MS workflows.
Keywords:
Artificial intelligenceDeep learningImaging mass spectrometryMETASPACEMachine learningOff-sample imagesPattern recognitionMore Related Videos
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