From Pixels to Chemical Knowledge: Interpreting Ion Mobility-Enabled Multidimensional Mass Spectrometry Imaging

Abstract Mass spectrometry imaging (MSI) is an advancing platform that enables the spatial mapping of molecular features directly onto tissue sections. The chemical profile is scanned pixel by pixel: analytes are ionized at each position and their mass spectra are recorded. To overcome the lack of chemical specificity for isomeric and isobaric species, an additional separation step is required in the conventional MSI. Integrating MSI with ion mobility (IM), known as IM-MSI, mitigates this limitation by enhancing the chemical specificity in the absence of online chromatography. This approach allows for the high-throughput generation of 4D molecular data comprising x–y spatial coordinates, m/z, and mobility-related dimensions such as drift time and collision cross-section (CCS). However, the processing and analysis of IM-MSI data remain poorly documented and heavily dependent on proprietary software, which limit data accessibility and interoperability. These bottlenecks hinder the development of standardized and reproducible analytical pipelines for large-scale biological studies. To address these challenges, this review focuses on current IM technologies and the transition from conventional 3D MSI to 4D IM-MSI data analysis. Current constraints on the accessibility of vendor-specific IM-MSI data are summarized alongside preprocessing strategies for 4D IM-MSI data sets and existing open-source tools. Furthermore, the role of CCS in resolving annotation ambiguity is discussed, together with emerging artificial intelligence approaches for IM-MSI, with a focus on their potential to improve data interpretation, annotation accuracy, and workflow automation. By highlighting current limitations and future opportunities, this review provides a practical framework for advancing robust, reproducible, and biologically meaningful IM-MSI analysis.