Linking Mass Spectrometric Imaging and Traditional Peptidomics: A Validation in the Obese Mouse Model

MALDI mass spectrometry imaging (MSI) is a promising technique in the field of molecular (immuno)histology but is confronted with the problematic large-scale identification of peptides from thin tissue sections. In this study we present a workflow that significantly increased the number of identified peptides in a given MALDI-MSI data set and we evaluated its power concerning relative peptide quantifications. Fourier transform mass spectrometry (FTMS) profiling on matrix-coated thin tissue sections allowed us to align spectra of different MS sources, matching identical peaks in the process, thus linking MSI data to tandem mass spectrometry (MS/MS) on one hand and semiquantitative liquid chromatography (LC)/MS data on the other. Bonanza clustering was applied in order to group MS/MS spectra of structurally related peptides, making it possible to infer the identity of MSI-detected compounds based on identified members within the same cluster, effectively increasing the number of identifications in a single MSI data set. Out of 136 detected peptides with MALDI-MSI, we were able to identify 46 peptides. For 31 of these, a LC/quadrupole time-of-flight (QTOF) counterpart was detected, and we observed similar obese (ob/ob) to wild-type (wt) peak intensity ratios for 18 peptides. This workflow significantly increased the number of identifications of peptide masses detected with MALDI-MSI and evaluated the power of this imaging method for relative quantification of peptide levels between experimental conditions.