Novel strategies to increase throughput and differentiate lipid isomers in mass spectrometry imaging : Development of computational tools and complex mass spectrometric methods for nanospray desorption electrospray ionization

Abstract: In this thesis, method development for improved analyte identification and throughput in mass spectrometry imaging (MSI) is discussed. In MSI, the spatial distribution of analytes from a sample is determined and visualized, information about the detected molecules interaction within the sample can thereby the deduced. Most MSI methods utilize high resolution accurate mass (HRAM) to assign an identity to a feature by its mass-to-charge (m/z) value. However, HRAM cannot distinguish isomeric species. I have therefore developed novel tools for annotation and separation of lipid isomer for MSI with nanospray desorption electrospray ionization (nano-DESI). Specifically, I show that tandem mass spectrometry (MSn) of silver ion species of lipids can be used for the separation of both fatty acid and phospholipid isomers. Additionally, I developed a method for parallelized MSn experiments, by performing multiple ion trap MSn in parallel to a fourier transform mass spectrometry (FTMS) transient. The ion trap MSn, albeit with lower resolution, has orthogonal specificity to FTMS and therefore generates a data set where the analytes identity can be deduced. Because the ITMS is executed in parallel to the typically used FTMS scan the imaging parameters are kept constant, thus generating a richer data set without increasing spatial resolution or experimental runtime.Lastly, data sets generated with nano-DESI MSI are complex and require specialized software tools for processing. I also discuss an open-source tool for data processing with high flexibility and fast processing speeds. With the newly developed tool we were able to process and interrogate data sets, thereby making better use of the acquired data.