Lipid Visualization and Identification Through Collisional Cross Section Aided Correlation of MS Imaging and Ex Situ MS Data for MS-MS Identification

Significance of the Topic

Mass spectrometry imaging is essential for spatially resolved lipidomics but identification challenges arise due to isobaric and near-isobaric species. Incorporating collisional cross section (CCS) metrics provides an orthogonal parameter that enhances confidence when correlating imaging peaks with ex situ MS-MS data.

Goals and Study Overview

This study demonstrates how CCS values can be used to align MALDI MSI data of mouse brain tissue with lipid extracts analyzed by MS-MS, improving the reliability of molecular identifications.

Methodology and Instrumentation

• Sample preparation: Cryosectioned mouse brain tissue coated with 9-aminoacridine matrix by spray deposition.
• Imaging acquisition: Negative-ion MALDI MSI on a SYNAPT G2-Si HDMS with TriWave ion mobility, 45 µm resolution, m/z 50–1,150, using an external lock mass.
• Calibration: Poly-alanine standard calibrated via IntelliStart to derive CCS calibration constants.
• Ex situ extraction: Lipid recovery from consecutive sections using 2:1 chloroform/ethanol and 4:1 ethanol/water, followed by MALDI spot analysis.
• Data processing: HDI Software v1.3 for image analysis; Lipid Maps database for mass matching (<±3 ppm); CCS cross-validation within ±0.5%.

Key Results and Discussion

• 168 candidate lipids identified in MSI based on accurate mass and CCS.
• Comparison with two extraction datasets yielded 50 common lipid peaks, average CCS deviation ±0.11%.
• 34 of 36 glycerophospholipid candidates were confirmed by CID MS-MS, resolving head groups and fatty acyl chains; sn-position remained unresolved.
• Example: PI(18:0_20:4) at m/z 885.549 with CCS 303.34 Ų, matching 18 of 21 expected fragments.

Benefits and Practical Applications

• CCS adds specificity in distinguishing isobaric lipid species, reducing misidentifications.
• Enhanced confidence when correlating imaging data with targeted MS-MS identifications.
• Applicable to diverse tissue analyses requiring spatial lipid mapping in research and QA/QC settings.

Future Trends and Opportunities

• Integration of comprehensive CCS libraries into lipidomics workflows for automated identification.
• Advances in ion mobility resolution to separate structural isomers in situ.
• Combination with quantitative imaging and multiomic approaches for deeper biological insights.

Conclusion

Employing CCS values as an additional identification criterion strengthens the correlation between MALDI MSI and ex situ MS-MS data, delivering more definitive lipid assignments and advancing spatial lipidomics methodologies.

Used Instrumentation

• SYNAPT G2-Si HDMS Mass Spectrometer with TriWave ion mobility.
• IntelliStart calibration using poly-alanine standard.
• High Definition Imaging (HDI) Software v1.3.
• 9-Aminoacridine matrix for negative-ion MALDI.
• Cryotome for tissue sectioning and spray-coating apparatus.