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Hochschulschrift

Automated Analysis of Complex Carbohydrates via Ion Mobility-Mass Spectrometry

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons203826

Manz,  Christian
Molecular Physics, Fritz Haber Institute, Max Planck Society;

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Zitation

Manz, C. (2017). Automated Analysis of Complex Carbohydrates via Ion Mobility-Mass Spectrometry. Master Thesis, Freie Universität, Berlin.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002C-C7DA-E
Zusammenfassung
Glycosylation is the most common post-translational modification in eukaryotic cells, making glycans ubiquitous in nature. They play a key role in a variety of physiological functions such as serving as recognition sites in molecular recognition or cell-cell communication. Due to their complex regio- and stereochemistry carbohydrate characterisation remains one of the greatest challenges in modern glycoproteomics. The frequent presence of isomers requires multidimensional analysis techniques to resolve the composition, connectivity and configuration of complex oligosaccharides. Recently, an orthogonal approach combining liquid chromatography (LC) and ion-mobility-mass spectrometry (IM-MS) emerged as a promising tool for carbohydrate analysis. The complementary separation techniques enable the differentiation of isomers based on polarity (LC), molecular size/ shape (IM), and mass-to-charge (MS). However, the combination of methods leads to an enormous increase of multi-layered datasets and processing tools to support such data collections are missing. Furthermore, existing databases lack the required quantity of reference data, therefore preventing the further utilization of this approach as routinely applied analyse tool. Within this thesis, this problem is addressed by using an instrumental setup of LC IM-MS to enable automatic online measurements in combination with a self-designed algorithm for processing ion mobility data (Aprid). For this purpose, the well-known oligosaccharides dextran and raffinose were used to characterize the automated setup based on sample consumption, time, robustness and accuracy in comparison to offline measurements. Furthermore, the biologically relevant milk sugar lacto-N-hexaose (LNH) as well as the blood group antigen Lewis Y were used to rate the automatic processing. The evaluation revealed the enormous potential of the automated setup. The processing time decreased by multiple orders of magnitude, while sample consumption and accuracy of the results stayed comparable to offline measurements and manual data processing. The so-obtained results demonstrate the potential of this approach as high throughput method to obtain reference data for future databases.