Improving Metabolite Identification During Imaging by Desorption Electrospray Ionization (DESI) Mass Spectrometry

Importance of the Topic

Desorption electrospray ionization imaging mass spectrometry enables direct metabolite imaging without sample preparation. However, isobaric interferences from the complex biological matrix often limit confident compound identification. Integrating ion mobility separation and collision cross section measurements into DESI IMS workflows addresses these challenges and enhances the specificity of metabolite mapping in situ.

Objectives and Study Overview

This work explores three complementary strategies to improve molecular identification in DESI IMS: accurate mass combined with collision cross section values for global profiling; targeted ion mobility MSMS monitoring of specific precursor–product transitions; and data independent acquisition using HDMSE to collect simultaneous profiling and fragmentation data for each pixel in imaging experiments.

Methodology

Rat brain sections were harvested, flash frozen, and cryosectioned for direct DESI IMS analysis without additional sample preparation. Drift times were calibrated using a standard mix, enabling the conversion of ion mobility measurements to calibrated collision cross section values. Mass spectra were acquired on a quadrupole time-of-flight instrument with ion mobility separation and lock mass correction. Data processing leveraged high definition imaging software to align low energy and high energy functions, matching precursor and fragment data by drift time and spatial correlation.

Used Instrumentation

• Quadrupole time-of-flight mass spectrometer with ion mobility separation
• DESI imaging platform on a SYNAPT G2-XS system
• Major Mix IMS/Tof Calibration Kit for collision cross section calibration
• High Definition Imaging software version 1.5

Key Results and Discussion

Accurate mass and CCS comparison of standard compounds showed errors below 2 ppm and CCS deviations under 2 %, confirming method precision. Ion mobility MSMS enabled clear separation of precursor and fragment ions, improving clarity of targeted imaging transitions. HDMSE demonstrated robust acquisition of both molecular profiles and fragmentation spectra per pixel, facilitating confident identification of lipids and metabolites in tissue sections.

Benefits and Practical Applications

• Enhanced specificity in metabolite localization without chromatographic separation
• Improved confidence in compound identification through orthogonal ion mobility data
• Capability for both global profiling and targeted imaging in a single DESI IMS experiment

Future Trends and Opportunities

Advances in ion mobility resolution and software-driven data analysis are expected to further refine DESI IMS workflows. Integration with machine learning for automated CCS prediction and real-time imaging may expand applications in clinical diagnostics, pharmaceutical research, and spatial metabolomics.

Conclusion

Combining accurate mass measurement with collision cross section and ion mobility-based fragmentation strategies significantly improves metabolite identification in DESI imaging. The described approaches provide practical solutions to overcome isobaric interferences and support high-confidence spatial metabolomics studies.