Mass spectrometry imaging (MSI) is a powerful, label-free technique that provides detailed maps of hundreds of molecules in complex samples with high sensitivity and subcellular spatial resolution. Accurate quantification in MSI relies on a detailed understanding of matrix effects associated with the ionization process along with evaluation of the extraction efficiency and mass-dependent ion losses occurring in the analysis step. We present a critical summary of approaches developed for quantitative MSI of metabolites, lipids, and proteins in biological tissues and discuss their current and future applications. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 72 is April 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Mass spectrometry imaging (MSI) is a powerful technique for the label-free spatiallyresolved analysis of biological tissues. Coupling ion mobility (IM) separation with MSI allows separation of isobars in the mobility dimension and increases confidence of peak assignments. Recently, imaging experiments have been implemented on the Agilent 6560 Ion Mobility Quadrupole Time of Flight Mass Spectrometer, making MSI experiments more broadly accessible to the MS community. However, the absence of data analysis software for this system presents a bottleneck. Herein, we present a vendor-specific imaging workflow to visualize IM-MSI data produced on the Agilent IM-MS system. Specifically, we have developed a Python script, the ion mobility-mass spectrometry image creation script (IM-MSIC), which interfaces Agilent's Mass Hunter Mass Profiler software with the MacCoss lab's Skyline software and generates drift time and mass-to-charge selected ion images. In the workflow, Mass Profiler is used for an untargeted feature detection. The IM-MSIC script mediates user input of data and extracts ion chronograms utilizing Skyline's command-line interface, then proceeds towards ion † Undergraduate student researchers.