A Streamlined Workflow\nfor Microscopy-Driven MALDI\nImaging Mass Spectrometry Data Collection

Matrix-assisted laser desorption/ionization imaging mass\nspectrometry\n(MALDI IMS) is a rapidly advancing technology for biomedical research.\nAs spatial resolution increases, however, so do acquisition time,\nfile size, and experimental cost, which increases the need to perform\nprecise sampling of targeted tissue regions to optimize the biological\ninformation gleaned from an experiment and minimize wasted resources.\nThe ability to define instrument measurement regions based on key\ntissue features and automatically sample these specific regions of\ninterest (ROIs) addresses this challenge. Herein, we demonstrate a\nworkflow using standard software that allows for direct sampling of\nmicroscopy-defined regions by MALDI IMS. Three case studies are included,\nhighlighting different methods for defining features from common sample\ntypesmanual annotation of vasculature in human brain tissue,\nautomated segmentation of renal functional tissue units across whole\nslide images using custom segmentation algorithms, and automated segmentation\nof dispersed HeLa cells using open-source software. Each case minimizes\ndata acquisition from unnecessary sample regions and dramatically\nincreases throughput while uncovering molecular heterogeneity within\ntargeted ROIs. This workflow provides an approachable method for spatially\ntargeted MALDI IMS driven by microscopy as part of multimodal molecular\nimaging studies.