Data-Independent Acquisition for Improved Compound Annotation in MALDI MS Imaging.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) enables spatial mapping of biomolecules within biological tissues, where conventional MS1-based workflows often result in ambiguous compound annotations. Data-dependent acquisition (DDA) can improve annotation specificity but is biased toward high-abundant ions and lacks reproducibility. Here, we present a novel MALDI MSI workflow integrating data-independent acquisition (DIA) to obtain both spatial and fragmentation information. In this approach, MS1 and DIA MS2 spectra are acquired alternately across the sample without prior knowledge of compound localization. Each image pixel consists of one MS1 and several DIA MS2 subpixels, providing both high spatial resolution for MS1 data and broad m/z coverage. Using small, randomized m/z isolation windows reduces spectral overlap and improves fragment-ion specificity. Data were processed using an extended Compound Discoverer, integrating mzCloud and LipidSearch for compound annotation. Applied to tissue of the parasitic worm Fasciola hepatica, this workflow produced detailed lipid maps and improved annotation confidence by combining precursor-mass, DIA MS2, and spatial-correlation information. Our results demonstrate that DIA offers a flexible strategy to integrate fragmentation information into MALDI MSI, expanding its capabilities for spatial metabolomics.