Johan O. R. Gustafsson

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Imaging mass spectrometry (IMS) is a powerful technology for mapping distributions of biological molecules like proteins and peptides within tissue sections. It is therefore potentially extremely useful for the analysis of pathological conditions such as neoplastic diseases. The use of IMS is typically limited to fresh frozen tissue specimens. However,(More)
The quality of MALDI-TOF mass spectrometric analysis is highly dependent on the matrix and its deposition strategy. Although different matrix-deposition methods have specific advantages, one major problem in the field of proteomics, particularly with respect to quantitation, is reproducibility between users or laboratories. Compounding this is the varying(More)
MALDI imaging mass spectrometry (MALDI-IMS) allows acquisition of mass data for metabolites, lipids, peptides and proteins directly from tissue sections. IMS is typically performed either as a multiple spot profiling experiment to generate tissue specific mass profiles, or a high resolution imaging experiment where relative spatial abundance for potentially(More)
One of the important challenges for MALDI imaging mass spectrometry (MALDI-IMS) is the unambiguous identification of measured analytes. One way to do this is to match tryptic peptide MALDI-IMS m/z values with LC-MS/MS identified m/z values. Matching using current MALDI-TOF/TOF MS instruments is difficult due to the variability of in situ time-of-flight(More)
Deleted in liver cancer 1 (DLC1) is a tumor suppressor protein that is frequently downregulated in various tumor types. DLC1 contains a Rho GTPase activating protein (GAP) domain that appears to be required for its tumor suppressive functions. Little is known about the molecular mechanisms that regulate DLC1. By mass spectrometry we have mapped a novel(More)
Imaging Mass Spectrometry (IMS) provides a means to measure the spatial distribution of biochemical features on the surface of a sectioned tissue sample. IMS datasets are typically huge and visualisation and subsequent analysis can be challenging. Principal component analysis (PCA) is one popular data reduction technique that has been used and we propose(More)
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