On-tissue dataset-dependent MALDI-TIMS-MS² bioimaging.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Metabolomics

Background:

• Trapped ion mobility spectrometry (TIMS) coupled with mass spectrometry (MS) imaging offers advanced spatial metabolomics.
• Lack of fragmentation spectra (MS2) in TIMS-MS imaging hinders confident compound identification.
• On-tissue annotation remains a challenge in spatial metabolomics.

Purpose of the Study:

• To introduce spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) for TIMS-MS imaging.
• To augment TIMS-MS imaging datasets with essential MS2 spectra.
• To improve on-tissue compound annotation and chemical space exploration.

Main Methods:

• Developed SIMSEF, a dataset-dependent acquisition strategy for TIMS-MS imaging.
• Systematically distributed fragmentation experiments with multiple collision energies.
• Integrated SIMSEF into the open-source software MZmine for data processing and analysis.
• Utilized matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS on rat brain tissue.

Main Results:

• SIMSEF successfully augmented TIMS-MS imaging datasets with MS2 spectra.
• Enabled on-tissue compound annotation via spectral library matching and rule-based lipid annotation in MZmine.
• Facilitated mapping of known and unknown chemical space using molecular networking.
• Demonstrated the workflow on rat brain tissue sections.

Conclusions:

• SIMSEF significantly enhances the analytical capabilities of TIMS-MS imaging.
• Provides a robust method for confident on-tissue compound annotation in spatial metabolomics.
• The open-source algorithm and data processing pipelines offer a valuable community resource for chemical space exploration.