The abstract is read at the publisher; the summary is JClub's.
msiFlow 2.0 improves MSI mass accuracy by recalibrating pixel-wise m/z values, achieving a mean absolute mass error below 1.5 ppm for MS/MS-supported lipid features.
- Why it matters: MSI datasets are large and complex, and prior workflows often lack integrated recalibration, annotation, batch correction, and quality checks needed for reproducible, scalable analysis.
- What they did: The team extended msiFlow with pixel-wise mass recalibration, automated lipid annotation, ComBat batch correction, visual quality control, plus improved alignment and spatially informed deisotoping, then tested it on four human and mouse MALDI-2 and t-MALDI-2 lipidomic MSI datasets.
- The result: Lipid annotation reduced unsupported lipid candidates, ComBat reduced sample-linked variation while preserving tissue-compartment separation, and computational optimization cut alignment runtime by ~80%.
The findingWhy it mattersWhat they didThe result
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