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Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center i Research | Alexandrov T.,SCiLS GmbH | Alexandrov T.,University of California at San Diego | Bartels A.,University of Bremen
Bioinformatics | Year: 2013

Motivation: Imaging mass spectrometry has emerged in the past decade as a label-free, spatially resolved and multi-purpose bioanalytical technique for direct analysis of biological samples. However, solving two everyday data analysis problems still requires expert judgment: (i) the detection of unknown molecules and (ii) the testing for presence of known molecules.Results: We developed a measure of spatial chaos of a molecular image corresponding to a mass-to-charge value, which is a proxy for the molecular presence, and developed methods solving considered problems. The statistical evaluation was performed on a dataset from a rat brain section with test sets of molecular images selected by an expert. The measure of spatial chaos has shown high agreement with expert judges. The method for detection of unknown molecules allowed us to find structured molecular images corresponding to spectral peaks of any low intensity. The test for presence applied to a list of endogenous peptides ranked them according to the proposed measure of their presence in the sample. © The Author 2013. Source


Laouirem S.,University Paris Diderot | Le Faouder J.,University Paris Diderot | Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center i Research | And 9 more authors.
Journal of Pathology | Year: 2014

Cirrhosis is a lesion at risk of hepatocellular carcinoma (HCC). Identifying mechanisms associated with the transition from cirrhosis to HCC and characterizing biomarkers of cirrhosis at high risk of developing into cancer are crucial for improving early diagnosis and prognosis of HCC. We used MALDI imaging to compare mass spectra obtained from tissue sections of cirrhosis without HCC, cirrhosis with HCC, and HCC, and a top-down proteomics approach to characterize differential biomarkers. We identified a truncated form of monomeric ubiquitin lacking the two C-terminal glycine residues, Ubi(1-74), the level of which increased progressively, from cirrhosis without HCC to cirrhosis with HCC to HCC. We showed that kallikrein-related peptidase 6 (KLK6) catalysed the production of Ubi(1-74) from monomeric ubiquitin. Furthermore, we demonstrated that KLK6 was induced de novo in cirrhosis and increased in HCC in parallel with accumulation of Ubi(1-74). We investigated in vitro the possible consequences of Ubi(1-74) accumulation and demonstrated that Ubi(1-74) interferes with the normal ubiquitination machinery in what is likely to be a kinetic process. Our data suggest that de novo KLK6 expression during early liver carcinogenesis may induce production of Ubi(1-74) by post-translational modification of ubiquitin. Given the deleterious effect of Ubi(1-74) on protein ubiquitination and the major role of ubiquitin machinery in maintenance of cell homeostasis, Ubi(1-74) might severely impact a number of critical cellular functions during transition from cirrhosis to cancer. Ubi(1-74) and KLK6 may serve as markers of cancer risk in patients with cirrhosis. Copyright © 2014 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. Source


Le Faouder J.,University Paris Diderot | Laouirem S.,University Paris Diderot | Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center i Research | And 7 more authors.
Proteomics | Year: 2014

Cholangiocarcinoma (CC) is the second most common primary malignancy of the liver. Although all CC derive from biliary epithelial cells, two main subtypes, hilar (H), and peripheral (P) CC are described. The objective of the study was to compare, using MALDI imaging mass spectrometry (MALDI IMS), in situ proteomic profiles of H- and P-CC in order to assess whether these subtypes may express different markers and to describe their respective localizations. Twenty-seven CC (16 P-CC and 11 H-CC) were subjected to MALDI IMS. Proteomic data were submitted to a dedicated cross-classification comparative design, enabling comparison of the entire generated spectra. Immunohistochemistry was performed for validation. Comparative analysis yielded a list of 19 differential protein peaks for the two subtypes, 14 of which were overexpressed in H-CC and five in P-CC. Among H-CC protein markers, most discriminant were human neutrophil peptides 1-3 that were expressed mainly by tumor cells and S100 proteins (A6 and A11) that were restricted to the stromal area. In P-CC, thymosin β4 was diffusely overexpressed. These results highlight the potential of MALDI IMS to discover new relevant biomarkers of CC and to characterize the heterogeneity of the two different subtypes. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source


Alexandrov T.,University of Bremen | Alexandrov T.,SCiLS GmbH | Alexandrov T.,Steinbeis Innovation Center i Research | Alexandrov T.,University of California at San Diego | And 5 more authors.
Analytical Chemistry | Year: 2013

Imaging mass spectrometry (imaging MS) has emerged in the past decade as a label-free, spatially resolved, and multipurpose bioanalytical technique for direct analysis of biological samples from animal tissue, plant tissue, biofilms, and polymer films.1,2 Imaging MS has been successfully incorporated into many biomedical pipelines where it is usually applied in the so-called untargeted mode-capturing spatial localization of a multitude of ions from a wide mass range.3 An imaging MS data set usually comprises thousands of spectra and tens to hundreds of thousands of mass-to-charge (m/z) images and can be as large as several gigabytes. Unsupervised analysis of an imaging MS data set aims at finding hidden structures in the data with no a priori information used and is often exploited as the first step of imaging MS data analysis. We propose a novel, easy-to-use and easy-to-implement approach to answer one of the key questions of unsupervised analysis of imaging MS data: what do all m/z images look like? The key idea of the approach is to cluster all m/z images according to their spatial similarity so that each cluster contains spatially similar m/z images. We propose a visualization of both spatial and spectral information obtained using clustering that provides an easy way to understand what all m/z images look like. We evaluated the proposed approach on matrix-assisted laser desorption ionization imaging MS data sets of a rat brain coronal section and human larynx carcinoma and discussed several scenarios of data analysis. © 2013 American Chemical Society. Source


Krasny L.,Institute Of Microbiology Vvi | Hoffmann F.,Friedrich - Schiller University of Jena | Ernst G.,Friedrich - Schiller University of Jena | Trede D.,SCiLS GmbH | And 10 more authors.
Journal of the American Society for Mass Spectrometry | Year: 2014

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) is a well-established analytical technique for determining spatial localization of lipids in biological samples. The use of Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometers for the molecular imaging of endogenous compounds is gaining popularity, since the high mass accuracy and high mass resolving power enables accurate determination of exact masses and, consequently, a more confident identification of these molecules. The high mass resolution FT-ICR imaging datasets are typically large in size. In order to analyze them in an appropriate timeframe, the following approach has been employed: the FT-ICR imaging datasets were spatially segmented by clustering all spectra by their similarity. The resulted spatial segmentation maps were compared with the histologic annotation. This approach facilitates interpretation of the full datasets by providing spatial regions of interest. The application of this approach, which has originally been developed for MALDI-TOF MSI datasets, to the lipidomic analysis of head and neck tumor tissue revealed new insights into the metabolic organization of the carcinoma tissue. © 2014 American Society for Mass Spectrometry. Source

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