Application of Desorption Electrospray Ionization (DESI) for Breast Tissue Imaging in Clinical Research

Significance of the Topic

Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) offers a powerful approach for mapping the spatial distribution of biomolecules in breast tissue under ambient conditions. Its minimal sample preparation and non-destructive nature make it appealing for clinical research and potential histology-level cancer diagnostics.

Study Objectives and Overview

The primary objective was to demonstrate the application of DESI coupled to a Xevo G2-XS QTof mass spectrometer for distinguishing malignant and non-malignant regions in breast tissue sections. Fresh-frozen breast samples were imaged to evaluate lipidomic patterns associated with underlying histopathology.

Methodology and Instrumentation

Breast tissue sections (10 µm) were mounted on glass slides and analyzed using a Prosolia 2D DESI source coupled to a Waters Xevo G2-XS QTof mass spectrometer. Imaging parameters included a pixel size of 100 µm, a scan rate of 5 scans/s, and a 95:5 methanol:water spray solvent. Optical images were acquired for co-registration in HDI v1.4 software. Following DESI-MSI, tissues were H&E stained to overlay histological reference images with ion maps.

Used Instrumentation

• Waters Xevo G2-XS Quadrupole Time-of-Flight Mass Spectrometer
• Prosolia 2D DESI Ionization Source
• Waters High Definition Imaging Software v1.4
• SIMCA (Umetrics) for statistical analysis

Main Findings and Discussion

DESI-MSI enabled clear spatial localization of lipid species, notably phosphatidylinositol (38:3) at m/z 887.56, correlating with malignant regions. Negative ion mode yielded intense fatty acid and phospholipid signals. Unsupervised PCA demonstrated distinct clustering of tumor versus stroma. Supervised Recursive Maximum Margin Criterion (RMMC) classification achieved 100% accuracy in differentiating tissue types based on cross-validation.

Benefits and Practical Applications

• Minimal sample handling preserves tissue integrity for downstream histology.
• Rapid, ambient analysis allows direct tissue interrogation.
• High accuracy in tissue type classification supports potential diagnostic workflows.
• Compatibility with existing MS platforms facilitates adoption in research and clinical labs.

Future Trends and Applications

Integration of DESI-MSI into clinical pathology could enable rapid in situ tumor margin assessment. Advances in high-throughput imaging, machine learning–driven data analysis, and expansion to other tissue types will broaden diagnostic and prognostic capabilities. Combining DESI-MSI with multimodal imaging may yield comprehensive molecular histopathology.

Conclusion

DESI-MSI on the Xevo G2-XS QTof offers a robust, non-destructive approach for lipid-based mapping of breast tissue architecture, demonstrating high accuracy in distinguishing malignant structures. This platform holds promise for enhancing molecular diagnostics in oncology.