Publications by authors named "Ottmar Golf"

Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology.

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Histopathological assessment of lymph node metastases (LNM) depends on subjective analysis of cellular morphology with inter-/intraobserver variability. In this study, LNM from esophageal adenocarcinoma was objectively detected using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Ninety lymph nodes (LN) and their primary tumor biopsies from 11 esophago-gastrectomy specimens were examined and analyzed by DESI-MSI.

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In this study, we make a direct comparison between desorption electrospray ionization-mass spectrometry (DESI-MS) and ultraperformance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) platforms for the profiling of glycerophospholipid (GPL) species in esophageal cancer tissue. In particular, we studied the similarities and differences in the range of GPLs detected and the congruency of their relative abundances as detected by each analytical platform. The main differences between mass spectra of the two modalities were found to be associated with the variance in adduct formation of common GPLs, rather than the presence of different GPL species.

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Gastrointestinal cancers are a leading cause of mortality, accounting for 23 % of cancer-related deaths worldwide. In order to improve outcomes from these cancers, novel tissue characterization methods are needed to facilitate accurate diagnosis. Rapid evaporative ionization mass spectrometry (REIMS) is a technique developed for the in vivo classification of human tissue through mass spectrometric analysis of aerosols released during electrosurgical dissection.

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Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment.

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Rapid evaporative ionization mass spectrometry (REIMS) technology allows real time intraoperative tissue classification and the characterization and identification of microorganisms. In order to create spectral libraries for training the classification models, reference data need to be acquired in large quantities as classification accuracy generally improves as a function of number of training samples. In this study, we present an automated high-throughput method for collecting REIMS data from heterogeneous organic tissue.

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Here we present a proof of concept cross-platform normalization approach to convert raw mass spectra acquired by distinct desorption ionization methods and/or instrumental setups to cross-platform normalized analyte profiles. The initial step of the workflow is database driven peak annotation followed by summarization of peak intensities of different ions from the same molecule. The resulting compound-intensity spectra are adjusted to a method-independent intensity scale by using predetermined, compound-specific normalization factors.

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Rapid evaporative ionization mass spectrometry (REIMS) was investigated for its suitability as a general identification system for bacteria and fungi. Strains of 28 clinically relevant bacterial species were analyzed in negative ion mode, and corresponding data was subjected to unsupervised and supervised multivariate statistical analyses. The created supervised model yielded correct cross-validation results of 95.

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Negative ion desorption electrospray ionization (DESI) was used for the analysis of an ex vivo tissue sample set comprising primary colorectal adenocarcinoma samples and colorectal adenocarcinoma liver metastasis samples. Frozen sections (12 μm thick) were analyzed by means of DESI imaging mass spectrometry (IMS) with spatial resolution of 100 μm using a computer-controlled DESI imaging stage mounted on a high resolution Orbitrap mass spectrometer. DESI-IMS data were found to predominantly feature complex lipids, including phosphatidyl-inositols, phophatidyl-ethanolamines, phosphatidyl-serines, phosphatidyl-ethanolamine plasmalogens, phosphatidic acids, phosphatidyl-glycerols, ceramides, sphingolipids, and sulfatides among others.

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