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Abstract

Accessing the relative changes in protein abundance is essential for a proper understanding of the various processes underlying disease progression and development. Nowadays, mass spectrometry-based proteomics allows for the identification of several thousand proteins in a single analysis. Unfortunately, mass spectrometry is inherently not quantitative, which is why additional techniques for protein quantification have to be developed. To measure quantitative changes in protein abundance, biological samples need either to be labeled using stable isotopes or protein abundances have to be computed using so called label-free techniques. Label-based quantification approaches are costly and the number of samples that can be quantified against each other is limited. Furthermore, depending on the sample, the introduction of the labels can be elaborate. Label-free quantification is not confronted with these limitations; principally, an unlimited number of samples can be quantified without the introduction of isotopes. Yet these advantages have their price: The development of label-free quantification algorithms is not trivial and requires profound knowledge both in bioinformatics and mass spectrometry. Namely the design of systems flexible enough to quantify data deriving from different mass spectrometric systems and proteomic workflows require additional experience and time. In order to quantify data acquired by LC-MALDI-MS, a novel software suite termed MSQBAT was developed and evaluated. MSQBAT is a platform independent software suite for MS1-based, label-free protein quantification. In contrast to other software solutions, MSQBAT is highly flexible and suited for the quantification of mass spectrometric data from various instrumental setups and proteomic workflows, such as (Ge)LC-MALDI-MS and (Ge)LC-ESI-MS. Quantification capabilities were evaluated using spike-in experiments analyzed using both different proteomic workflows and instruments. Human proteins were spiked in variable concentrations into a complex E.coli back-ground proteome and processed using both an LC-MS and a GeLC-MS approach. Samples were chromatographically separated on a nanoACQUITY UPLC system using a 120 minutes gradient and subsequently analyzed by an AB SCIEX TOF/TOF 5800 system and an AB SCIEX QTRAP 6500 system. Furthermore, a publicly available quantification benchmark data set has been used to evaluate LC-ESI-MS quantification capabilities. Obtained results show that MSQBAT can be applied to quantify data deriving from both LC-/GeLC-MALDI-MS and LC-/GeLC-ESI-MS workflows with high accuracy. Therefore, this software suite has a range of application outperforming all currently available solutions.