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Data acquisition methods for next-generation mass spectrometry-based proteomics
Data acquisition methods for next-generation mass spectrometry-based proteomics
Of all biomolecules, proteins are arguably the most important gears in our cellular machinery when it comes to biological function. Mass spectrometry (MS)-based proteomics has become the method of choice to study proteomic systems in a global and unbiased manner. Yet, it still trails ‘next-generation’ genomics and transcriptomics technology in terms of coverage, throughput and sensitivity. In this thesis, I present three MS acquisition strategies that break through longstanding technological limitations and facilitate comprehensive and high-throughput proteomics. Isobaric labeling enables quantification of multiple samples in a single analysis and thereby increases throughput. In a first project, I established a tailored acquisition strategy for a new generation of isobaric labels termed EASI-tag. As opposed to previous technologies, the EASI-tag method is interference-free and therefore allows multiplexed and accurate quantification, also on widely used tandem mass spectrometers. A core subject of my PhD was establishing ion mobility spectrometry as an additional dimension of separation in MS-based proteomics. This work builds on a high-resolution and high-speed quadrupole time-of-flight platform, which was equipped with a trapped ion mobility (TIMS) device. Making use of the sensitivity and flexibility of the TIMS device, we developed ‘parallel accumulation – serial fragmentation’ (PASEF), which effectively multiplies peptide sequencing speed and sensitivity by first storing all ions and then switching the quadrupole isolation window synchronously with ion mobility separation. The PASEF method has now become an integral part of a full-fledged commercial proteomics instrument. The third method, termed BoxCar, addresses a fundamental limitation of Orbitrap mass analyzers, which are the main workhorses in proteomics laboratories worldwide. For reasons detailed in this thesis, in practice, less than 1% of all ions are used for mass analysis in full scans. BoxCar increases this fraction up to a factor of 10 by dividing the entire mass range into multiple narrow segments or ‘boxes’. This thesis establishes an increase in dynamic range of the mass analysis of about one order of magnitude, which allowed detection of 10,000 proteins in 100 min from mouse brain tissue at the MS1-level. The method is particularly beneficial for analyses that are limited by a large protein abundance range, as is often the case in a clinical context. In our laboratory, BoxCar has already dramatically improved proteomics studies of human heart and plasma samples. In summary, this PhD thesis provides the basis for next-generation MS acquisition methods that promise to elevate proteomics to the next level, closer to the ultimate goal of complete and ubiquitous proteomes. Importantly, the fundamental concepts and methods developed here are generic and can seamlessly be applied in other MS-based omics fields facing similar challenges, fox example metabolomics.
Mass Spectrometry, Data Acquisition Methods, Proteomics
Meier, Florian
2018
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Meier, Florian (2018): Data acquisition methods for next-generation mass spectrometry-based proteomics. Dissertation, LMU München: Faculty of Chemistry and Pharmacy
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Abstract

Of all biomolecules, proteins are arguably the most important gears in our cellular machinery when it comes to biological function. Mass spectrometry (MS)-based proteomics has become the method of choice to study proteomic systems in a global and unbiased manner. Yet, it still trails ‘next-generation’ genomics and transcriptomics technology in terms of coverage, throughput and sensitivity. In this thesis, I present three MS acquisition strategies that break through longstanding technological limitations and facilitate comprehensive and high-throughput proteomics. Isobaric labeling enables quantification of multiple samples in a single analysis and thereby increases throughput. In a first project, I established a tailored acquisition strategy for a new generation of isobaric labels termed EASI-tag. As opposed to previous technologies, the EASI-tag method is interference-free and therefore allows multiplexed and accurate quantification, also on widely used tandem mass spectrometers. A core subject of my PhD was establishing ion mobility spectrometry as an additional dimension of separation in MS-based proteomics. This work builds on a high-resolution and high-speed quadrupole time-of-flight platform, which was equipped with a trapped ion mobility (TIMS) device. Making use of the sensitivity and flexibility of the TIMS device, we developed ‘parallel accumulation – serial fragmentation’ (PASEF), which effectively multiplies peptide sequencing speed and sensitivity by first storing all ions and then switching the quadrupole isolation window synchronously with ion mobility separation. The PASEF method has now become an integral part of a full-fledged commercial proteomics instrument. The third method, termed BoxCar, addresses a fundamental limitation of Orbitrap mass analyzers, which are the main workhorses in proteomics laboratories worldwide. For reasons detailed in this thesis, in practice, less than 1% of all ions are used for mass analysis in full scans. BoxCar increases this fraction up to a factor of 10 by dividing the entire mass range into multiple narrow segments or ‘boxes’. This thesis establishes an increase in dynamic range of the mass analysis of about one order of magnitude, which allowed detection of 10,000 proteins in 100 min from mouse brain tissue at the MS1-level. The method is particularly beneficial for analyses that are limited by a large protein abundance range, as is often the case in a clinical context. In our laboratory, BoxCar has already dramatically improved proteomics studies of human heart and plasma samples. In summary, this PhD thesis provides the basis for next-generation MS acquisition methods that promise to elevate proteomics to the next level, closer to the ultimate goal of complete and ubiquitous proteomes. Importantly, the fundamental concepts and methods developed here are generic and can seamlessly be applied in other MS-based omics fields facing similar challenges, fox example metabolomics.