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msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis

Author

Listed:
  • Philippa Spangenberg

    (University Hospital Essen)

  • Sebastian Bessler

    (University of Münster)

  • Lars Widera

    (University Hospital Essen)

  • Jenny Bottek

    (University Hospital Essen)

  • Mathis Richter

    (University of Münster)

  • Stephanie Thiebes

    (University Hospital Essen)

  • Devon Siemes

    (University Hospital Essen)

  • Sascha D. Krauß

    (University Hospital Essen)

  • Lukasz G. Migas

    (Vanderbilt University
    Delft University of Technology)

  • Siva Swapna Kasarla

    (Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V.)

  • Prasad Phapale

    (Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V.)

  • Jens Kleesiek

    (University Hospital Essen)

  • Dagmar Führer

    (University Hospital Essen)

  • Lars C. Moeller

    (University Hospital Essen)

  • Heike Heuer

    (University Hospital Essen)

  • Raf Plas

    (Vanderbilt University
    Delft University of Technology
    Vanderbilt University)

  • Matthias Gunzer

    (University Hospital Essen
    Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V.)

  • Oliver Soehnlein

    (University of Münster)

  • Jens Soltwisch

    (University of Münster)

  • Olga Shevchuk

    (University Hospital Essen)

  • Klaus Dreisewerd

    (University of Münster)

  • Daniel R. Engel

    (University Hospital Essen)

Abstract

Multimodal imaging by matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI MSI) and microscopy holds potential for understanding pathological mechanisms by mapping molecular signatures from the tissue microenvironment to specific cell populations. However, existing software solutions for MALDI MSI data analysis are incomplete, require programming skills and contain laborious manual steps, hindering broadly applicable, reproducible, and high-throughput analysis to generate impactful biological discoveries. Here, we present msiFlow, an accessible open-source, platform-independent and vendor-neutral software for end-to-end, high-throughput, transparent and reproducible analysis of multimodal imaging data. msiFlow integrates all necessary steps from raw data import to analytical visualisation along with state-of-the-art and self-developed algorithms into automated workflows. Using msiFlow, we unravel the molecular heterogeneity of leukocytes in infected tissues by spatial regulation of ether-linked phospholipids containing arachidonic acid. We anticipate that msiFlow will facilitate the broad applicability of MSI in multimodal imaging to uncover context-dependent cellular regulations in disease states.

Suggested Citation

  • Philippa Spangenberg & Sebastian Bessler & Lars Widera & Jenny Bottek & Mathis Richter & Stephanie Thiebes & Devon Siemes & Sascha D. Krauß & Lukasz G. Migas & Siva Swapna Kasarla & Prasad Phapale & J, 2025. "msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55306-7
    DOI: 10.1038/s41467-024-55306-7
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