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

0578317 - ÚOCHB 2024 RIV US eng J - Článek v odborném periodiku
Heuckeroth, S. - Behrens, A. - Wolf, C. - Fütterer, A. - Nordhorn, I. D. - Kronenberg, K. - Brungs, Corinna - Korf, A. - Richter, H. - Jeibmann, A. - Karst, U. - Schmid, Robin
On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging.
Nature Communications. Roč. 14, November (2023), č. článku 7495. E-ISSN 2041-1723
Grant CEP: GA ČR(CZ) GM21-11563M
Institucionální podpora: RVO:61388963
Klíčová slova: mass spectrometry * data acquisition * MALDI-TOF
Obor OECD: Biochemistry and molecular biology
Impakt faktor: 16.6, rok: 2022
Způsob publikování: Open access
https://doi.org/10.1038/s41467-023-43298-9

Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS2) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS2 spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource.
Trvalý link: https://hdl.handle.net/11104/0347326