Label-free peptide profiling of Orbitrap™ full mass spectra

Background. We developed a new version of the open source software package Peptrix that can yet compare large numbers of Orbitrap™ LC-MS data. The peptide profiling results for Peptrix on MS1 spectra were compared with those obtained from a small selection of open source and commercial software packages: msInspect, Sieve™ and Progenesis™. The properties compared in these packages were speed, total number of detected masses, redundancy of masses, reproducibility in numbers and CV of intensity, overlap of masses, and differences in peptide peak intensities. Reproducibility measurements were taken for the different MS1 software applications by measuring in triplicate a complex peptide mixture of immunoglobulin on the Orbitrap™ mass spectrometer. Values of peptide masses detected from the high intensity peaks of the MS1 spectra by peptide profiling were verified with values of the MS2 fragmented and sequenced masses that resulted in protein identifications with a significant score. Findings. Peptrix finds about the same number of peptide features as the other packages, but peptide masses are in some cases approximately 5 to 10 times less redundant present in the peptide profile matrix. The Peptrix profile matrix displays the largest overlap when comparing the number of masses in a pair between two software applications. The overlap of peptide masses between software packages of low intensity peaks in the spectra is remarkably low with about 50% of the detected masses in the individual packages. Peptrix does not differ from the other packages in detecting 96% of the masses that relate to highly abundant sequenced proteins. MS1 peak intensities vary between the applications in a non linear way as they are not processed using the same method. Conclusions. Peptrix is capable of peptide profiling using Orbitrap™ files and finding differential expressed peptides in body fluid and tissue samples. The number of peptide masses detected in Orbitrap™ files can be increased by using more MS1 peptide profiling applications, including Peptrix, since it appears from the comparison of Peptrix with the other applications that all software packages have likely a high false negative rate of low intensity peptide peaks (missing peptides)

Advancing bioinformatics methods for in-depth proteome analysis based on high-resolution mass spectrometry

Mass spectrometry-based shotgun proteomics has become one of the essential techniques for comprehensive studies of living systems. Due to the inherent complexity of proteomes and the data, bioinformatics plays a critical role to translate mass spectra into biological information and knowledge. Adapting to the increased availability of high-resolution mass analyzers, computational strategies for processing shotgun proteomics data should have some adjustments to utilize the advantages of modern instruments. This thesis presents five constituent papers to illustrate the methodological advancements for analyzing shotgun proteomics data that are generated from high-resolution mass spectrometry. Paper-I describes the DeMix workflow for protein identification, in which we broke down an old paradigm of tandem mass spectrometry by converting the unwanted co-fragmentation events into an advantage of natural multiplexing. DeMix simplifies the data processing procedure and significantly improves protein identification outcomes. Paper-III describes a label-free extension of the DeMix workflow, termed DeMix-Q, which makes use of the quantitative features of extracted ion chromatograms (XICs) for reliably propagating peptide identifications across LC-MS/MS experiments. DeMix-Q improves the reproducibility of peptide quantification by addressing the missing value problem that is caused by the data-dependent acquisition of MS/MS. Based on the results, the concept of quantification-centered proteomics has been proposed. In the practice of quantification-centered proteomics, a flexible proteome summarizing approach termed Diffacto is described in Paper-V, which utilizes the information about covariation of peptides’ abundances to improve the relative quantification of proteins. Diffacto offers automatic quality control to remove inconsistent and unreliable quantitative data on peptides. The combination of a weighted summarizing method and an efficient FDR estimation provides significant enhancement of data utility for large-scale comparative proteomics. In Paper-II, an improved pI estimation method has been introduced to the novel device for sample fractionation based on isoelectric focusing technique. In Paper-IV and V, the applications of peptide de novo sequencing have been demonstrated for analyzing complex proteomes in the absence of reference databases

Development of a method for biomarkers characterization by mass spectrometry techniques

The purpose of this study is to define an extractive approach for the detection of the low-molecule peptide fraction from human plasma or serum and the subsequent analysis and interpretation of the obtained data, with the ultimate aim of developing a standardised protocol for the identification of potential biomarkers. The extraction of the low molecular protein fraction was developed thanks to a series of standard peptides solutions and using silica magnetic beads techniques differently functionalised with the purpose to bind target molecules with a different type of intermolecular force. The treatment of the samples, plasma or serum, took place without the use of proteases, as trypsin, to generate digested lysates, or electrophoresis and gel separation techniques, to avoid creating additional complexity in subsequent steps of data interpretation and to use the lower quantity of sample as possible. Both the peptides contained in the standard solution and those in the low molecular weight fraction of the pre-treated biological sample were separated and characterized through high performance liquid chromatography (HPLC) coupled to full scan and tandem mass spectrometry equipped with an electrospray ion source (ESI-MS/MS). Samples from biological sources were subsequently analysed using the mass spectrometry MALDI-TOF technique. In this project the development of the extraction method was followed by its application to real samples. The presence of low-molecular-weight peptides in plasma samples, from dialysis nephrotic patients at various stages of Sars-COV2 infection, and in plasma from healthy donors was evaluated with the aim to find significant differences between groups, especially in terms of qualitative/quantitative differences in the m/z ratios present in MS spectra. A bioinformatics approach to data processing has also been implemented, either by using statistical tools such as the Venn diagram or the Meaning Analysis of Microarrays (SAM) or by developing a series of codes in Python, for processing spectral data combined with algorithms with silico fragmentation rules. Outputs were compared with information from peptide databases to obtain significant correspondences between the theoretical and experimental spectrum

Entwicklung bioanalytischer Methoden zur quantitativen und qualitativen Analyse von markierten Peptiden und Proteinen durch Kopplung von Chromatographie und Massenspektrometrie

This PhD thesis was a Cotutelle between the Université de Pau et des Pays de l’Adour (UPPA) in Pau, France and the Christian-Albrechts University (CAU) in Kiel, Germany. In the course of this international collaboration, bio-analytical methods for the quantitative and qualitative analysis of labelled peptides and proteins were developed, which were based on the hyphenation of chromatography with mass spectrometry. Peptides and protein digests were lanthanide labelled using DOTA-based compounds according to an optimised protocol. Separation on the peptide level was performed using IP-RP-nanoHPLC. Complementary data sets were acquired using MALDI-MS for identification and ICP-MS for quantification. In this context, an online precleaning step was developed and implemented in the nanoHPLC separation routine, which allowed for effective removal of excess reagents. This lead to lowered metal backgrounds during ICP-MS measurements and thus better data interpretability, while guarding peptide recovery at a maximum level. An alternative offline purification using solid phase extraction (SPE) resulted in important peptide losses and can be considered unsuitable for quantitative analysis. Additives to the nanoHPLC eluents, such as HFBA and EDTA were tested and not deemed beneficial for the analysis of normal peptide samples. HFBA can be reconsidered for special application on very hydrophilic peptide species. A set of labelled peptides was developed, which due to application of known quantities could be employed for quick and simple quantification of a low complexity digest sample. In addition this peptide set allowed for the reliable superposition of chromatograms, enabling sample comparability especially for complementary ICP-MS and MALDI-MS data. Experiments for application of fsLA-ICP-MS on MALDI-MS target plates were conducted and showed very promising results. For this purpose, samples that were already identified using MALDI-MS were supposed to be remeasured using fsLA-ICP-MS. First quantification attempts on the modified steel target plate were successful and in the range of expectance. Adjusted parameters for MALDI-MS allowed for proper peptide identifications.Diese Dissertation entstand im Rahmen einer Cotutelle zwischen der Université de Pau et des Pays de l’Adour (UPPA) in Pau, Frankreich und der Christian-Albrechts Universität zu Kiel (CAU), Deutschland. Während dieser internationalen Zusammenarbeit wurden bioanalytische Methoden für die quantitative wie qualitative Analyse markierter Peptide und Proteine erarbeitet. Die Arbeiten basierten auf der Kopplung von Chromatographie und Massenspektrometrie. Peptide und Proteinverdaue wurden nach einem optimierten Protokoll mit DOTA-basierten Verbindungen lanthanid-markiert. Die Separation auf Peptidebene wurde mittels IP-RP-nanoHPLC durchgeführt. Komplementäre Datensätze wurden mittels MALDI-MS zur Peptidindentifizierung und mittels ICP-MS zur Quantifizierung erstellt. In diesem Rahmen wurde ein online Aufreinigungsschritt zur effektiven Entfernung von Reagenzüberschüssen entwickelt und in die nanoHPLC Trennungsmethode implementiert. Dies führte zu niedrigeren Metallhintergrundwertem in nanoHPLC-ICP-MS Messungen und einer besseren Interpretierbarkeit der Daten, gleichzeitig konnten die Peptidausbeuten auf höchstem Niveau erhalten bleiben. Alternative offline Reinigung mittels Festphasenextraktion (SPE) verursachte beträchtliche Verluste in den Peptidausbeuten und konnte für quantitative Analysen als ungeeignet erachtet werden. Die Zumischung verschiedener Substanzen, wie HFBA und EDTA zu den Eluenten der nanoHPLC wurde untersucht und für die Analyse normaler Peptidproben als wenig nutzbringend befunden. HFBA kann dennoch eventuell für Spezialanwendungen auf besonders hydrophile Peptide in Betracht gezogen werden. Ein Satz markierter Peptide wurde zusammengestellt, welcher durch Verwendung bekannter Mengen für eine schnelle und einfache Quantifizierung einer wenig komplexen Probe eingesetzt werden konnte. Zudem konnten diese Peptide dazu verwendet werden, eine zuverlässige Überlagerung von Chromatogrammen zu erwirken und damit die Probenvergleichbarkeit speziell zwischen ICP-MS und MALDI-MS sicher zu stellen. Versuche zur Anwendung von fsLA-ICP-MS auf MALDI-Stahlplatten wurden durchgeführt und zeigten vielversprechende Ergebnisse. Hierzu sollten bereits mit MALDI-MS identifizierte Proben, erneut mittels fsLA-ICP-MS gemessen werden. Erste Quantifizierungsversuche auf modifizierten MALDI-Platten waren erfolgreich. Angepasste MALDI-MS Parameter ermöglichten eine eindeutige Peptididentifikation

Synthetic biology meets proteomics: Construction of a la carte QconCATs for absolute protein quantification

We report a new approach to the assembly and construction of QconCATs, quantitative concatamers for proteomic applications that yield stoichiometric quantities of sets of stable isotope-labelled internal standards. The new approach is based on synthetic biology precepts of biobricks, making use of loop assembly to construct larger entities from individual biobricks. It offers a major gain in flexibility of QconCAT implementation and enables rapid and efficient editability that permits, for example, substitution of one peptide for another. The basic building block (a Qbrick) is a segment of DNA that encodes two or more quantification peptides for a single protein, readily held in a repository as a library resource. These Qbricks are then assembled in a one tube ligation reaction that enforces the order of assembly, to yield short QconCATs that are useable for small quantification products. However, the DNA context of the short also allows a second cycle of assembly such that five different short QconCATs can be assembled into a longer QconCAT in a second, single tube ligation. From a library of Qbricks, a bespoke QconCAT can be assembled quickly and efficiently in a form suitable for expression and labelling in vivo or in vitro. We refer to this approach as the ALACAT strategy as it permits a la carte design of quantification standards

Development and application of quantitative proteomics strategies to analyze molecular mechanisms of neurodegeneration

Neurodegenerative disorders such as Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis or Prion diseases are chronic, incurable and often fatal. A cardinal feature of all neurodegenerative disorders is the accumulation of misfolded and aggregated proteins. Depending on the disease, these aggregated proteins are cell type specific and have distinct cellular localizations, compositions and structures. Despite intensive research, the contribution of protein misfolding and aggregation to the cell type specific toxicity is not completely understood. In recent years, quantitative proteomics has matured into an exceptionally powerful technology providing accurate quantitative information on almost all cellular proteins as well as protein interactions, modifications, and subcellular localizations, which cannot be addressed by other omics technologies. The aim of this thesis is to investigate key features of neurodegeneration such as misfolded proteins and toxic protein aggregates with cutting edge proteomics, presenting a technological “proof of concept” and novel insights into the (patho)physiology of neurodegeneration

Biomarkers identification in fibromyalgia syndrome

Fibromyalgia Syndrome (FMS) is a chronic syndrome characterized by widespread pain. FMS is a collection of other symptoms and overlapping conditions contribute to complicate the diagnosis, the assessment and the treatment. Unknown etiology and none laboratory tests have been appropriately validated for the diagnosis of the disease. The comparison of protein patterns in body fluids of diseased and healthy subjects has the potential to identify new disease-specific biomarkers. Some purine nucleotide metabolism disorders such as myoadenylate deaminase (MAD) deficiency report symptoms similar to those seen in FMS. In consideration of what described above, we carried out a serum proteomic analysis of FMS patients with respect to control subjects searching potentially useful biomarkers for the disease. In addition, we evaluated serum purine metabolite concentrations in patients affected by FMS and the relationships between their levels and FMS clinical parameters. Twenty-two females affected by FMS (according to the American College of Rheumatology, 1990) and twenty-two healthy women were recruited as controls for analysis of purine metabolite. Sixteen females FMS and twelve controls were enrolled in the study for the analyses of the proteome. Proteomic analysis was performed by combining two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) and serum purine levels were quantified using reverse phase high performance liquid chromatographic (RP-HPLC). In our study, using the proteomic approach, we have identified differentially expressed proteins, such as Transthyretin (TTR), Alpha-1 Antitrypsin (A1AT) and Retinol Binding Protein 4 (RBP4). The serum 4 concentrations of these proteins were significantly higher in FMS patients compared with healthy controls. TTR and RBP4 are retinoid transporters, moreover retinoid dysfunction is related to oxidative stress as well as A1AT. These results support the hypothesis that oxidative stress could be implicated in the pathophysiology of FMS. Moreover, considerably higher serum concentration of inosine, hypoxanthine and xanthine levels (p<0.001) and lower serum adenosine (p<0.05) were detected in the FMS patients when compared to healthy controls. Our data show a negative correlation between adenosine and the Fibromyalgia Impact Questionnaire (FIQ). Our results suggest that purines, in particular adenosine and inosine, may be involved in pain transmission in fibromyalgia

Biomarkers identification in fibromyalgia syndrome

Fibromyalgia Syndrome (FMS) is a chronic syndrome characterized by widespread pain. FMS is a collection of other symptoms and overlapping conditions contribute to complicate the diagnosis, the assessment and the treatment. Unknown etiology and none laboratory tests have been appropriately validated for the diagnosis of the disease. The comparison of protein patterns in body fluids of diseased and healthy subjects has the potential to identify new disease-specific biomarkers. Some purine nucleotide metabolism disorders such as myoadenylate deaminase (MAD) deficiency report symptoms similar to those seen in FMS. In consideration of what described above, we carried out a serum proteomic analysis of FMS patients with respect to control subjects searching potentially useful biomarkers for the disease. In addition, we evaluated serum purine metabolite concentrations in patients affected by FMS and the relationships between their levels and FMS clinical parameters. Twenty-two females affected by FMS (according to the American College of Rheumatology, 1990) and twenty-two healthy women were recruited as controls for analysis of purine metabolite. Sixteen females FMS and twelve controls were enrolled in the study for the analyses of the proteome. Proteomic analysis was performed by combining two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) and serum purine levels were quantified using reverse phase high performance liquid chromatographic (RP-HPLC). In our study, using the proteomic approach, we have identified differentially expressed proteins, such as Transthyretin (TTR), Alpha-1 Antitrypsin (A1AT) and Retinol Binding Protein 4 (RBP4). The serum 4 concentrations of these proteins were significantly higher in FMS patients compared with healthy controls. TTR and RBP4 are retinoid transporters, moreover retinoid dysfunction is related to oxidative stress as well as A1AT. These results support the hypothesis that oxidative stress could be implicated in the pathophysiology of FMS. Moreover, considerably higher serum concentration of inosine, hypoxanthine and xanthine levels (p<0.001) and lower serum adenosine (p<0.05) were detected in the FMS patients when compared to healthy controls. Our data show a negative correlation between adenosine and the Fibromyalgia Impact Questionnaire (FIQ). Our results suggest that purines, in particular adenosine and inosine, may be involved in pain transmission in fibromyalgia

Chemometric tools for automated method-development and data interpretation in liquid chromatography

The thesis explores the challenges and advancements in the field of liquid chromatography (LC), particularly focusing on complex sample analysis using high-resolution mass spectrometry (MS) and two-dimensional (2D) LC techniques. The research addresses the need for efficient optimization and data-handling strategies in modern LC practice. The thesis is divided into several chapters, each addressing specific aspects of LC and polymer analysis. Chapter 2 provides an overview of the need for chemometric tools in LC practice, discussing methods for processing and analyzing data from 1D and 2D-LC systems and how chemometrics can be utilized for method development and optimization. Chapter 3 introduces a novel approach for interpreting the molecular-weight distribution and intrinsic viscosity of polymers, allowing quantitative analysis of polymer properties without prior knowledge of their interactions. This method correlates the curvature parameter of the Mark-Houwink plot with the polymer's structural and chemical properties. Chapters 4 and 5 focus on the analysis of cellulose ethers (CEs), essential in various industrial applications. A new method is presented for mapping the substitution degree and composition of CE samples, providing detailed compositional distributions. Another method involves a comprehensive 2D LC-MS/MS approach for analyzing hydroxypropyl methyl cellulose (HPMC) monomers, revealing subtle differences in composition between industrial HPMC samples. Chapter 6 introduces AutoLC, an algorithm for automated and interpretive development of 1D-LC separations. It uses retention modeling and Bayesian optimization to achieve optimal separation within a few iterations, significantly improving the efficiency of gradient LC separations. Chapter 7 focuses on the development of an open-source algorithm for automated method development in 2D-LC-MS systems. This algorithm improves separation performance by refining gradient profiles and accurately predicting peak widths, enhancing the reliability of complex gradient LC separations. Chapter 8 addresses the challenge of gradient deformation in LC instruments. An algorithm based on the stable function corrects instrument-specific gradient deformations, enabling accurate determination of analyte retention parameters and improving data comparability between different sources. Chapter 9 introduces a novel approach using capacitively-coupled-contactless-conductivity detection (C4D) to measure gradient profiles without adding tracer components. This method enhances inter-system transferability of retention models for polymers, overcoming the limitations of UV-absorbance detectable tracer components. Chapter 10 discusses practical choices and challenges faced in the thesis chapters, highlighting the need for well-defined standard samples in industrial polymer analysis and emphasizing the importance of generalized problem-solving approaches. The thesis identifies future research directions, emphasizing the importance of computational-assisted methods for polymer analysis, the utilization of online reaction modulation techniques, and exploring continuous distributions obtained through size-exclusion chromatography (SEC) in conjunction with triple detection. Chemometric tools are recognized as essential for gaining deeper insights into polymer chemistry and improving data interpretation in the field of LC