TY - JOUR
T1 - msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis
AU - Spangenberg, Philippa
AU - Bessler, Sebastian
AU - Thiebes, Stephanie
AU - Migas, Lukasz G.
AU - Kasarla, Siva Swapna
AU - Kleesiek, Jens
AU - Van de Plas, Raf
AU - Shevchuk, Olga
AU - Engel, Daniel R.
AU - More Authors, null
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85217190546&partnerID=8YFLogxK
U2 - 10.1038/s41467-024-55306-7
DO - 10.1038/s41467-024-55306-7
M3 - Article
C2 - 39870624
AN - SCOPUS:85217190546
SN - 2041-1723
VL - 16
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 1065
ER -