MALDI Imaging HDMSE: A Novel Data Independent Technique for the Visualisation and Identification of Lipids Directly from a Single Tissue Section

Lipidomics is an emerging application area that is taken advantage of the mass spectrometry developments that enabled the simultaneous analysis of a wide range of analytes. The spatial localization of lipids within tissue microstructures is however often lost during the lipid extraction process and challenges image analysis. MS imaging developments on the other hand allowed for the mapping of lipid species in entire tissue section. Structural identification is typically performed after processing the untargeted MS imaging data, followed by further manual acquisitions either on the same or consecutive sections. A data independent acquisition method called MALDI Imaging HDMSE is presented, where MS and MS/MS information are acquired within the same experiment, without any precursor selection. Post-acquisition, precursors and fragments are correlated on the basis of ionic drift in the gas phase, which is further refined utilizing commonality of their spatial distributions. Proof-of-principal experiments have been carried out using part of a rat whole body section where CHCA matrix was applied evenly to the sample in several coats using a SunCollect nebulising spray device. Data were acquired using a MALDI SYNAPT G2 instrument with tri-wave ion guide optics to separate ions according to their gas phase mobility. Within the same MALDI imaging experiment, the mass spectrometer was set to apply alternate collision energies to the transfer cell between low energy and elevated collision energies, with the latter inducing lipid fragmentation. Since fragmentation occurs post-ion mobility separation, the precursors at low energy have the same drift time as their associated fragments from the elevated energy scan. The dataset was subsequently processed using High Definition Imaging (HDI) MALDI software for enhanced image analysis. Drift time alignment and spatial correlation of the data from both low and elevated energy functions were carried out with the same platform.