Metabolites Analysis in Mouse Brain Using the Image Analysis Function of IMAGEREVEAL MS

Importance of the Topic

The ability to map a diverse set of metabolites across brain tissues supports insights into neurochemistry, disease pathology, and pharmacological responses. MALDI-MSI provides label-free spatial profiling of numerous molecules in one experiment, and advances in image analysis software help to distill complex datasets into biologically meaningful patterns.

Objectives and Study Overview

This work presents an automated workflow combining high-resolution MALDI-QTOF imaging with advanced statistical analysis to segment and characterize molecular distributions in mouse brain sections. The study aims to demonstrate how iMScope QT and IMAGEREVEAL MS can be used for both non-targeted discovery and targeted metabolite identification.

Methodology

Frozen mouse brains were cryosectioned at 8 µm thickness and mounted on ITO glass slides. α-Cyano-4-hydroxycinnamic acid was applied via sublimation using an automated deposition system. Mass spectra were acquired in positive ion mode across m/z 100–210 with MALDI-MSI at a laser diameter of 2 arbitrary units and 2.1 kV detector voltage. Data processing included dimensionality reduction with UMAP for image segmentation and similarity-based m/z feature extraction.

Used Instrumentation

• iMScope QT MALDI-QTOF mass spectrometer (Shimadzu)
• iMLayer matrix deposition system (Shimadzu)
• IMAGEREVEAL MS image analysis software (Shimadzu)
• Cryostat CM1950 for tissue sectioning

Main Results and Discussion

Dimensionality reduction by UMAP partitioned the dataset into eight spatial segments, each reflecting distinct anatomical or matrix-related regions. Cluster C6 corresponded to hippocampus and cerebellar granular layers with clear localization. Similar image extraction linked m/z 203.223 and 104.107 to spermine and choline, respectively, validating the workflow’s ability to recover biomolecular identities from segmented images.

Benefits and Practical Applications

• Non-targeted detection of hundreds of metabolites with high mass accuracy
• Automated segmentation simplifies the identification of region-specific biomarkers
• User-friendly software reduces the barrier for statistical analysis of imaging data

Future Trends and Potential Applications

Integration of MALDI-MSI with machine learning could drive automated annotation of tissue features. Expansion to larger m/z ranges and multi-omics data fusion may enable comprehensive molecular atlases of the brain. Real-time image analysis and cloud-based workflows are expected to enhance throughput and collaborative research.

Conclusion

The combined use of high-resolution MALDI-MSI on iMScope QT and IMAGEREVEAL MS’s advanced analysis tools enables efficient, non-targeted profiling and localization of brain metabolites. The segmentation and similarity extraction approach demonstrated here facilitates rapid discovery and identification of region-specific molecular signatures.

References

1. Shimma S et al. Visualization of Glutamate Decarboxylase Activity in Germinated Legume Seeds with iMScope QT. Shimadzu Application Note No.80.
2. Shimma S et al. Imaging of Phospholipids and Glucose in Rice Koji Using MALDI-MS. Shimadzu Application Note No.64.
3. Shimma S et al. Enzyme Histochemistry via Mass Spectrometry Imaging. Shimadzu Application Note No.68.
4. Tsuji Y et al. Mass Spectrometry Imaging Delineates Thymus-Centric Metabolism In Vivo After Dexamethasone. Appl. Sci. 11, 10038 (2021).
5. Smets T et al. Evaluation of Distance Metrics and Spatial Autocorrelation in UMAP and t-SNE for Mass Spectrometry Imaging Data. Anal. Chem. 91, 5716–5724 (2019).