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Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Nature Protocols (2024)Cite this article

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Abstract

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography–MS, gas chromatography–MS and MS–imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography–(IMS–)MS, gas chromatography–MS and (IMS–)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Key points

  • MZmine is a program designed to process data from untargeted mass spectrometry (MS) experiments acquired in data-dependent acquisition mode; specifically, collision-induced dissociation and higher-energy collisional dissociation.

  • This protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by instrumental setups: liquid chromatography–(ion mobility spectrometry–)MS, gas chromatography–MS and (ion mobility spectrometry–)MS imaging.

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Fig. 1: An overview of the data processing workflows in MZmine.
Fig. 2: A schematic representation of the LC(–IMS)–MS workflow described in Procedure 1.
Fig. 3: An overview of popular modules and third-party tools for feature annotation integrated with MZmine.
Fig. 4: Feature grouping by feature shape correlation.
Fig. 5: IIN annotation refinement.
Fig. 6: A schematic representation of the GC–EI–MS workflow described in Procedure 2.
Fig. 7: The S/N threshold parameter of ADAP resolver.
Fig. 8: A schematic representation of the MS imaging and IMS–MS imaging workflows described in Procedure 3.
Fig. 9: A screenshot of a feature list visualized in MZmine.

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Data availability

All example datasets used in this protocol are publicly available through the GNPS-MassIVE, MetaboLights and Metabolomics Workbench repositories under the following accession numbers: MSV000091634, Procedure 1, LC–IMS–MS; ST000981, Procedure 2, GC–EI–MS; MSV000090328, Procedure 3, IMS–MS imaging; MSV000091642, lipid annotation (Procedure 1, Step 21), LC–IMS–MS; MTBLS265, export for statistics (Procedure 1, Step 25), LC–MS. The FBMN results can be accessed on GNPS at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=ffd5aee568b54d9da1f3b771c459ebe5.

Code availability

The latest release of MZmine can be downloaded from https://www.mzmine.org. The complete source code is available at https://github.com/mzmine/mzmine3/ under the MIT licence. The MZmine documentation is hosted on GitHub and available at https://www.mzmine.org/documentation.

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Acknowledgements

T.P. is supported by the Czech Science Foundation (GA CR) grant 21-11563M and by the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 891397. T.D. is supported by the European Regional Development Fund, Programme Johannes Amos Comenius project ‘IOCB MSCA PF Mobility’ no. CZ.02.01.01/00/22_010/0002733. C.B. is supported by the Czech Academy of Sciences Program to Support Prospective Human Resources. A.S. and X.D. are supported by the National Institutes of Health grant U01CA235507. P.C.D. is supported by R01GM107550, R03OD034493, R01DK136117 and NSF 2152526. We thank F. Rooks and M. FitzGerald for editing the manuscript.

Author information

Author notes

  1. These authors contributed equally: Steffen Heuckeroth, Tito Damiani.

Authors and Affiliations

  1. University of Münster, Münster, Germany

    Steffen Heuckeroth, Uwe Karst & Robin Schmid

  2. Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic

    Tito Damiani, Olena Mokshyna, Corinna Brungs, Ansgar Korf, Joshua David Smith, Robin Schmid & Tomáš Pluskal

  3. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aleksandr Smirnov & Xiuxia Du

  4. First Faculty of Medicine, Charles University, Prague, Czech Republic

    Joshua David Smith

  5. University of Tuebingen, Tuebingen, Germany

    Paolo Stincone & Daniel Petras

  6. Waters Corporation, Wilmslow, UK

    Nicola Dreolin

  7. University of Geneva, Geneva, Switzerland

    Louis-Félix Nothias

  8. Université Côte d’Azur, CNRS, ICN, Nice, France

    Louis-Félix Nothias

  9. Örebro University, Örebro, Sweden

    Tuulia Hyötyläinen & Matej Orešič

  10. University of Turku and Åbo Akademi University, Turku, Finland

    Matej Orešič

  11. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA

    Pieter C. Dorrestein & Robin Schmid

  12. University of California Riverside, Riverside, CA, USA

    Daniel Petras

  13. Wageningen University & Research, Wageningen, the Netherlands

    Justin J. J. van der Hooft

  14. University of Johannesburg, Johannesburg, South Africa

    Justin J. J. van der Hooft

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  1. Steffen Heuckeroth
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Contributions

Design and oversight of the project performed by S.H., T.D., A.S., R.S. and T.P. Online MZmine documentation performed by S.H., T.D., O.M., C.B., A.K., R.S. Testing the workflows described and improving the descriptions performed by S.H., T.D., O.M., C.B., A.K., J.D.S., P.S., N.D., L.-F.N., T.H., M.O., U.K., P.C.D., D.P., X.D., J.J.J.vdH. and R.S.

Corresponding authors

Correspondence to Robin Schmid or Tomáš Pluskal.

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Competing interests

P.C.D. served as a consultant for DSM Animal Health in 2023, is an advisor and has equity in Cybele and is a co-founder, advisor and holds equity in Ometa, Enveda and Arome with prior approval by UC San Diego. J.J.J.vdH. is a member of the Scientific Advisory Board of NAICONS Srl., Milan, Italy, and consults for Corteva Agriscience, Indianapolis, IN, USA.

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Nature Protocols thanks Guowang Xu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Schmid, R. et al. Nat. Biotechnol. 41, 447–449 (2023): https://doi.org/10.1038/s41587-023-01690-2

Schmid, R. et al. Nat. Commun. 12, 3832 (2021): https://doi.org/10.1038/s41467-021-23953-9

Heuckeroth, S. et al. Nat. Commun. 14, 7495 (2023): https://doi.org/10.1038/s41467-023-43298-9

Extended data

Extended Data Fig. 1 Screenshot of the batch mode dialogue box.

The current processing steps are displayed in the ‘Batch queue’ panel. Additional steps can be selected from the ‘Modules’ panel and included using the double-arrows buttons. The current batch file can be saved using the ‘Save’ button whereas other batch files can be imported using the ‘Load’ button. Some modules offer a ‘Show preview’ option that can be opened by ticking the corresponding checkbox. For the preview to work, data must be already imported in MZmine. The online documentation for each processing module can be opened using the ‘Help’ button.

Extended Data Fig. 2 Screenshot of the ‘Raw data overview’ module.

The module displays three panels: chromatogram panel (left), mass spectrum panel (right) and scan information panel (bottom panel), which contains information for every scan in the data file.

Extended Data Fig. 3 Screenshot of the interactive visualisation panel in the Local minimum resolver module.

Two subpanels are present: one for ‘noisy’ and one for ‘good’ EIC traces. The goal of the parameters optimization is to ensure detection of true features while minimising ‘noisy’ peaks to be retained as features. Feature lists and EIC traces to display can be chosen from the corresponding drop-down menus. Detected features are colour-filled and resolved peaks are shown in different colours.

Extended Data Fig. 4 Screenshot of the ‘Ion mobility raw data overview’ module.

a, A summed frame spectrum with a blue indicator at the selected m/z. b, A chromatogram plot showing the BPC (black) and EIC (blue) of the selected m/z. The blue indicator shows the RT of the selected frame. c, A total ion mobilogram of the selected frame. d, A mobility vs. m/z heatmap of the selected frame. e, An ion mobility trace of the selected m/z in RT and mobility dimensions.

Supplementary information

Reporting Summary

Supplementary Data 1

All batch files optimized for each example dataset and the corresponding data processing outputs (feature lists and MS2 spectral lists).

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Heuckeroth, S., Damiani, T., Smirnov, A. et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00996-y

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