Mass spectrometry (MS) imaging enables the spatial mapping of compound distributions within tissue sections. When applied to biomedical imaging, lateral resolutions in the lower μm range can provide insight into tissues affected by disease states or other conditions. Therefore, MS imaging is becoming more prominent in the life sciences. However, the technique mostly relies on annotations by m/z only, which is unsuitable for reliably identifying lipids due to isomeric and isobaric overlap of lipid species. One reason is that most instruments lack data-dependent acquisition of MS/MS spectra. State-of-the-art ion mobility spectrometry (IMS)-enabled mass spectrometers provide collisional cross sections (CCS), improving annotation confidence in IMS-MS imaging. While the provided CCS values allow for more robust annotations, the community still lacks MS/MS acquisition strategies. Therefore, we present two different strategies to increase confidence for MALDI imaging based lipid identification.
The first strategy aims to integrate results from LC-IMS-MS/MS and MALDI-IMS-MS imaging experiments.[1] The LC-IMS-MS/MS data is used for confident lipid annotation, using a rule-based approach and subsequent quality control by including equivalent carbon number models, as well as Kendrick mass defect plots.[2]
The second strategy does not rely on additional LC-MS/MS data, because MS/MS spectra are acquired as part of the MALDI imaging experiment. For this, we present the spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) workflow to plan dataset-dependent MS/MS acquisition and subsequently acquire MS/MS spectra on a TIMS-QTOF-MS.[3]
Both strategies were integrated into the open-source software mzmine. Full data analysis workflows, covering raw data processing, annotation, integration of datasets from LC-IMS-MS/MS and MALDI-IMS-MS, as well as scheduling MS/MS experiments across the tissue by applying SIMSEF, will be compared.
[1] Schmid, R., Heuckeroth, S., Korf, A. et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol 41, 447–449 (2023).
[2] Korf, A., Vosse, C., Schmid, R. et al. Three‐dimensional Kendrick mass plots as a tool for graphical lipid identification. Rapid Commun Mass Spectrom, 32, 981-991 (2018).
[3] Heuckeroth, S., Behrens, A., Wolf, C. et al. On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging. Nat Commun 14, 7495 (2023).