Computational Framework for Data-Independent Acquisition Proteomics.

Mass spectrometry (MS) is one of the main techniques for high throughput discovery- and targeted-based proteomics experiments. The most popular method for MS data acquisition has been data dependent acquisition (DDA) strategy which primarily selects high abundance peptides for MS/MS sequencing. DDA incorporates stochastic data acquisitions to avoid repetitive sequencing of same peptide, resulting in relatively irreproducible results for low abundance peptides between experiments. Data independent acquisition (DIA), in which peptide fragment signals are systematically acquired, is emerging as a promising alternative to address the DDA's stochasticity. DIA results in more complex signals, posing computational challenges for complex sample and high-throughput analysis. As a result, targeted extraction which requires pre-existing spectral libraries has been the most commonly used approach for automated DIA data analysis. However, building spectral libraries requires additional amount of analysis time and sample materials which are the major barriers for most research groups. In my dissertation, I develop a computational tool called DIA-Umpire, which includes computational and signal processing algorithms to enable untargeted DIA identification and quantification analysis without any prior spectral library. In the first study, a signal feature detection algorithm is developed to extract and assemble peptide precursor and fragment signals into pseudo MS/MS spectra which can be analyzed by the existing DDA untargeted analysis tools. This novel step enables direct and untargeted (spectral library-free) DIA identification analysis and we show the performance using complex samples including human cell lysate and glycoproteomics datasets. In the second study, a hybrid approach is developed to further improve the DIA quantification sensitivity and reproducibility. The performance of DIA-Umpire quantification approach is demonstrated using an affinity-purification mass spectrometry experiment for protein-protein interaction analysis. Lastly, in the third study, I improve the DIA-Umpire pipeline for data obtained from the Orbitrap family of mass spectrometers. Using public datasets, I show that the improved version of DIA-Umpire is capable of highly sensitive, untargeted analysis of DIA data for the data generated using Orbitrap family of mass spectrometers. The dissertation work addresses the barriers of DIA analysis and should facilitate the adoption of DIA strategy for a broad range of discovery proteomics applications.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120699/1/tsouc_1.pd

Tissue Proteomes: Quantitative Mass Spectrometry of Murine Liver and Ovarian Endometrioma

A human genome contains more than 20 000 protein-encoding genes. A human proteome, instead, has been estimated to be much more complex and dynamic. The most powerful tool to study proteins today is mass spectrometry (MS). MS based proteomics is based on the measurement of the masses of charged peptide ions in a gas-phase. The peptide amino acid sequence can be deduced, and matching proteins can be found, using software to correlate MS-data with sequence database information. Quantitative proteomics allow the estimation of the absolute or relative abundance of a certain protein in a sample. The label-free quantification methods use the intrinsic MS-peptide signals in the calculation of the quantitative values enabling the comparison of peptide signals from numerous patient samples. In this work, a quantitative MS methodology was established to study aromatase overexpressing (AROM+) male mouse liver and ovarian endometriosis tissue samples. The workflow of label-free quantitative proteomics was optimized in terms of sensitivity and robustness, allowing the quantification of 1500 proteins with a low coefficient of variance in both sample types. Additionally, five statistical methods were evaluated for the use with label-free quantitative proteomics data. The proteome data was integrated with other omics datasets, such as mRNA microarray and metabolite data sets. As a result, an altered lipid metabolism in liver was discovered in male AROM+ mice. The results suggest a reduced beta oxidation of long chain phospholipids in the liver and increased levels of pro-inflammatory fatty acids in the circulation in these mice. Conversely, in the endometriosis tissues, a set of proteins highly specific for ovarian endometrioma were discovered, many of which were under the regulation of the growth factor TGF-β1. This finding supports subsequent biomarker verification in a larger number of endometriosis patient samples.Siirretty Doriast

Quantitative mass spectrometry-based proteomics: An overview

In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics

Quantification and Simulation of Liquid Chromatography-Mass Spectrometry Data

Computational mass spectrometry is a fast evolving field that has attracted increased attention over the last couple of years. The performance of software solutions determines the success of analysis to a great extent. New algorithms are required to reflect new experimental procedures and deal with new instrument generations. One essential component of algorithm development is the validation (as well as comparison) of software on a broad range of data sets. This requires a gold standard (or so-called ground truth), which is usually obtained by manual annotation of a real data set. Comprehensive manually annotated public data sets for mass spectrometry data are labor-intensive to produce and their quality strongly depends on the skill of the human expert. Some parts of the data may even be impossible to annotate due to high levels of noise or other ambiguities. Furthermore, manually annotated data is usually not available for all steps in a typical computational analysis pipeline. We thus developed the most comprehensive simulation software to date, which allows to generate multiple levels of ground truth and features a plethora of settings to reflect experimental conditions and instrument settings. The simulator is used to generate several distinct types of data. The data are subsequently employed to evaluate existing algorithms. Additionally, we employ simulation to determine the influence of instrument attributes and sample complexity on the ability of algorithms to recover information. The results give valuable hints on how to optimize experimental setups. Furthermore, this thesis introduces two quantitative approaches, namely a decharging algorithm based on integer linear programming and a new workflow for identification of differentially expressed proteins for a large in vitro study on toxic compounds. Decharging infers the uncharged mass of a peptide (or protein) by clustering all its charge variants. The latter occur frequently under certain experimental conditions. We employ simulation to show that decharging is robust against missing values even for high complexity data and that the algorithm outperforms other solutions in terms of mass accuracy and run time on real data. The last part of this thesis deals with a new state-of-the-art workflow for protein quantification based on isobaric tags for relative and absolute quantitation (iTRAQ). We devise a new approach to isotope correction, propose an experimental design, introduce new metrics of iTRAQ data quality, and confirm putative properties of iTRAQ data using a novel approach. All tools developed as part of this thesis are implemented in OpenMS, a C++ library for computational mass spectrometry

Neuroproteomics — LC-MS Quantitative Approaches

Neuroproteomics is a scientific field that aims to study all the proteins of the central nervous system, their expression, function, and interactions. The central nervous system is intricate and heterogeneous, and the study of its proteome is consequently complex, with many biological questions still requiring deep investigation. For this, mass spectrometry approaches, most often coupled with liquid chromatography (LC-MS), have been the number one choice in proteomics, and over the years it has added many important findings to the field. At this point it is important that proteomics turns to the quantitative expression of proteins instead of only identifying which proteins are present in a given sample, much because the most important alterations may be slight alterations in the quantity of a protein in a given situation. Therefore, many LC-MS quantitative approaches have been developed relying on the labeling of the proteins or even by using label-free techniques