Matrix-assisted laser desorption/ionization (MALDI)-time of flight (TOF)-mass spectrometry imaging (MSI) enables the spatial localization of proteins to be mapped directly on tissue sections, simultaneously detecting hundreds in a single analysis. However, the large data size, as well as the complexity of MALDI-MSI proteomics datasets, requires the appropriate tools and statistical approaches in order to reduce the complexity and mine the dataset in a successful manner. Here, a pipeline for the management of MALDI-MSI data is described, starting with preprocessing of the raw data, followed by statistical analysis using both supervised and unsupervised statistical approaches and, finally, annotation of those discriminatory protein signals highlighted by the data mining procedure.
Keywords: Data processing; MALDI-MSI; Spatial proteomics; Statistical analysis; Supervised analysis; Unsupervised analysis.