The development of tissue micro-array (TMA) technologies provides insights into high-throughput analysis of proteomics patterns from a large number of archived tumour samples. In the work reported here, matrix-assisted laser desorption/ionisation-ion mobility separation-mass spectrometry (MALDI-IMS-MS) profiling and imaging methodology has been used to visualise the distribution of several peptides and identify them directly from TMA sections after on-tissue tryptic digestion. A novel approach that combines MALDI-IMS-MSI and principal component analysis-discriminant analysis (PCA-DA) is described, which has the aim of generating tumour classification models based on protein profile patterns. The molecular classification models obtained by PCA-DA have been validated by applying the same statistical analysis to other tissue cores and patient samples. The ability to correlate proteomic information obtained from samples with known and/or unknown clinical outcome by statistical analysis is of great importance, since it may lead to a better understanding of tumour progression and aggressiveness and hence improve diagnosis, prognosis as well as therapeutic treatments. The selectivity, robustness and current limitations of the methodology are discussed.

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MALDI-mass spectrometry imaging (MALDI-MSI) is a technique that allows proteomic information, that is, the spatial distribution and identification of proteins, to be obtained directly from tissue sections. The use of in situ enzymatic digestion as a sample pretreatment prior to MALDI-MSI analysis has been found to be useful for retrieving protein identification directly from formalin-fixed, paraffin-embedded (ffpe) tissue sections. Here, an improved method for the study of the distribution and the identification of peptides obtained after in situ digestion of fppe pancreatic tumor tissue sections by using MALDI-mass spectrometry imaging coupled with ion mobility separation (IMS) is described. MALDI-IMS-MS images of peptide obtained from pancreatic tumor tissue sections allowed the localization of tumor regions within the tissue section, while minimizing the peak interferences which were observed with conventional MALDI-TOF MSI. The use of ion mobility separation coupled with MALDI-MSI improved the selectivity and specificity of the method and, hence, enabled both the localization and in situ identification of glucose regulated protein 78 kDa (Grp78), a tumor biomarker, within pancreatic tumor tissue sections. These findings were validated using immunohistochemical staining.

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