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

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, Jens Kleesiek, Dagmar Führer, Lars C. Moeller, Heike Heuer, Raf Plas, Matthias Gunzer, Oliver Soehnlein, Jens Soltwisch, Olga Shevchuk, Klaus Dreisewerd and Daniel R. Engel ()
Additional contact information
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
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
Matthias Gunzer: University Hospital Essen
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

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-024-55306-7

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