Presented by Dr. George Guo, University of Auckland The unique set of expressed proteins, specific to a particular cell type, location, or place in time or space, critically underpins organ function and disease states. The liquid-chromatography mass spectrometry (LC-MS/MS) proteomic method allows for global characterisation of these proteomes at the expense of spatial information, which limits our understanding of disease mechanisms. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) can survey this spatial proteomic complexity. But identification and quantification of peptides are mutually exclusive, primarily due to the typically lower amount of evidence provided within a given MS imaging coordinate. To address this, we developed HIT-MAP (High-resolution Informatics Toolbox in MALDI mass-spectrometry imaging Proteomics), an R-based pipeline for the automated annotation and visualization of proteomic MALDI-MSI datasets. HIT-MAP uses statistical methods for spatially aware pixel clustering and m/z feature summarization to perform proteomics annotation via a false discovery rate-controlled peptide mass fingerprinting and protein coverage analysis pipeline.