msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis
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 s...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55306-7 |
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| author | 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 Van de Plas Matthias Gunzer Oliver Soehnlein Jens Soltwisch Olga Shevchuk Klaus Dreisewerd Daniel R. Engel |
| author_facet | 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 Van de Plas Matthias Gunzer Oliver Soehnlein Jens Soltwisch Olga Shevchuk Klaus Dreisewerd Daniel R. Engel |
| author_sort | Philippa Spangenberg |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6d42362a4ea643e08b354e713241307a |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-6d42362a4ea643e08b354e713241307a2025-02-02T12:31:18ZengNature PortfolioNature Communications2041-17232025-01-0116111310.1038/s41467-024-55306-7msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysisPhilippa Spangenberg0Sebastian Bessler1Lars Widera2Jenny Bottek3Mathis Richter4Stephanie Thiebes5Devon Siemes6Sascha D. Krauß7Lukasz G. Migas8Siva Swapna Kasarla9Prasad Phapale10Jens Kleesiek11Dagmar Führer12Lars C. Moeller13Heike Heuer14Raf Van de Plas15Matthias Gunzer16Oliver Soehnlein17Jens Soltwisch18Olga Shevchuk19Klaus Dreisewerd20Daniel R. Engel21Institute for Experimental Immunology and Imaging, University Hospital EssenInstitute of Hygiene, University of MünsterInstitute for Experimental Immunology and Imaging, University Hospital EssenInstitute for Experimental Immunology and Imaging, University Hospital EssenInstitute of Experimental Pathology (ExPat), Center of Molecular Biology of Inflammation (ZMBE), University of MünsterInstitute for Experimental Immunology and Imaging, University Hospital EssenInstitute for Experimental Immunology and Imaging, University Hospital EssenInstitute for Experimental Immunology and Imaging, University Hospital EssenMass Spectrometry Research Center, Vanderbilt UniversityLeibniz-Institut für Analytische Wissenschaften – ISAS – e.V.Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V.Institute for AI in Medicine (IKIM), University Hospital EssenDepartment of Endocrinology, Diabetes and Metabolism, University Hospital EssenDepartment of Endocrinology, Diabetes and Metabolism, University Hospital EssenDepartment of Endocrinology, Diabetes and Metabolism, University Hospital EssenMass Spectrometry Research Center, Vanderbilt UniversityInstitute for Experimental Immunology and Imaging, University Hospital EssenInstitute of Experimental Pathology (ExPat), Center of Molecular Biology of Inflammation (ZMBE), University of MünsterInstitute of Hygiene, University of MünsterInstitute for Experimental Immunology and Imaging, University Hospital EssenInstitute of Hygiene, University of MünsterInstitute for Experimental Immunology and Imaging, University Hospital EssenAbstract 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.https://doi.org/10.1038/s41467-024-55306-7 |
| spellingShingle | 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 Van de Plas Matthias Gunzer Oliver Soehnlein Jens Soltwisch Olga Shevchuk Klaus Dreisewerd Daniel R. Engel msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis Nature Communications |
| title | msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis |
| title_full | msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis |
| title_fullStr | msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis |
| title_full_unstemmed | msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis |
| title_short | msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis |
| title_sort | msiflow automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis |
| url | https://doi.org/10.1038/s41467-024-55306-7 |
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