In chemical analyses, it is crucial to distinguish between chemical species. This is often accomplished via chromatographic separations. These separations are often pushed to their limits in terms of the number of analytes that can be sufficiently resolved from one another, particularly when a quantitative analysis of these compounds is needed. Very often, complicated methods or new technology is required to provide adequate separation of samples arising from a variety of fields such as metabolomics, environmental science, food analysis, etc. An often overlooked means for improving analysis is the use of chemometric data analysis techniques. Particularly, the use of chemometric curve resolution techniques can mathematically resolve analyte signals that may be overlapped in the instrumental data. The use of chemometric techniques facilitates quantitation, pattern recognition, or any other desired analyses. Unfortunately, these methods have seen little use outside of traditionally chemometrics focused research groups. In this dissertation, we attempt to show the utility of one of these methods, multivariate curve resolution-alternating least squares (MCR-ALS), to liquid chromatography as well as its application to more advanced separation techniques. First, a general characterization of the performance of MCR-ALS for the analysis of liquid chromatography-diode array detection (LC-DAD) data is accomplished. It is shown that under a wide range of conditions (low chromatographic resolution, low signal-to-noise, and high similarity between analyte spectra), MCR-ALS is able to increase the number of quantitatively analyzable peaks. This increase is up to five-fold in many cases. Second, a novel methodology for MCR-ALS analysis of comprehensive two-dimensional liquid chromatography (LC x LC) is described. This method, called two dimensional assisted liquid chromatography (2DALC), aims to improve quantitation in LC x LC by combining the advantages of both one-dimensional and two dimensional chromatographic data. We show that 2DALC can provide superior quantitation to both LC x LC and one dimensional LC under certain conditions. Finally, we apply MCR-ALS to an LC x LC analysis of fourteen furanocoumarins in three apiaceous vegetables. The optimal implementation of MCR-ALS and subsequent integration was determined. For these data, simply performing MCR-ALS on the two dimensional chromatogram and manually integrating the results proved to be the superior method. These results demonstrate the usefulness of these curve resolution techniques as a compliment to advanced chromatographic techniques.

Proteomics serves an important role at the systems-level in understanding of biological functioning. Mass spectrometry proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. In the most widely used bottom-up approach to MS-based high-throughput quantitative proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and then analyzed using a mass spectrometer. The three fundamental challenges in the analysis of bottom-up MS-based proteomics are as follows: (i) Identifying the proteins that are present in a sample, (ii) Aligning different samples on elution (retention) time, mass, peak area (intensity) and etc, (iii) Quantifying the abundance levels of the identified proteins after alignment. Each of these challenges requires knowledge of the biological and technological context that give rise to the observed data, as well as the application of sound statistical principles for estimation and inference. In this dissertation, we present a set of statistical methods in bottom-up proteomics towards protein identification, alignment and quantification. We describe a fully Bayesian hierarchical modeling approach to peptide and protein identification on the basis of MS/MS fragmentation patterns in a unified framework. Our major contribution is to allow for dependence among the list of top candidate PSMs, which we accomplish with a Bayesian multiple component mixture model incorporating decoy search results and joint estimation of the accuracy of a list of peptide identifications for each MS/MS fragmentation spectrum. We also propose an objective criteria for the evaluation of the False Discovery Rate (FDR) associated with a list of identifications at both peptide level, which results in more accurate FDR estimates than existing methods like PeptideProphet. Several alignment algorithms have been developed using different warping functions. However, all the existing alignment approaches suffer from a useful metric for scoring an alignment between two data sets and hence lack a quantitative score for how good an alignment is. Our alignment approach uses "Anchor points" found to align all the individual scan in the target sample and provides a framework to quantify the alignment, that is, assigning a p-value to a set of aligned LC-MS runs to assess the correctness of alignment. After alignment using our algorithm, the p-values from Wilcoxon signed-rank test on elution (retention) time, M/Z, peak area successfully turn into non-significant values. Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical mass spectrometry-based proteomics data sets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of "presence / absence", we enable the selection of proteins not typically amendable to quantitative analysis; e.g., "one-state" proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence / absence analysis of a given data set in a principled way, resulting in a single list of selected proteins with a single associated FDR.

L’objectif de cette thèse était de développer et d’optimiser de nouvelles méthodologies et approches analytiques afin d’améliorer le potentiel de l’analyse protéomique pour les études biologiques.La première partie de ce travail est consacrée à la détermination massive et exacte de la position N-Terminale des protéines (N-Terminome). Pour cela, nous avons utilisé et développé une approche basée sur une dérivation N-Terminale au TMPP. Cette méthodologie de marquage de la position N-Terminale a permis d’aborder l’étude des clivages protéolytiques des protéines exportées par le parasite P. falciparum (pathogène de la malaria) dans le globule rouge.Afin de permettre une exploitation automatique à haut débit des données de MS/MS, nous avons élaboré une nouvelle méthodologie (dénommée dN-TOP). Celle-Ci repose sur l’utilisation de TMPP portant des isotopes stables et permet ainsi d’accéder à la détermination des positions N-Terminales pour des études de N-Terminome à large échelle.La seconde partie est dédiée aux développements de différentes stratégies analytiques de quantification, aussi bien au niveau peptidique qu’au niveau protéique, appliquées à une série de problématiques biologiques. Ces optimisations ont été réalisées dans le contexte de l’étude des complexes protéiques, du dosage de prion par SRM, de quantification des glycations d’anticorps monoclonaux thérapeutiques et de l’hémoglobine HbA2 pour la standardisation des méthodes de référence. / The objective of this Ph.D. thesis was to develop and optimize new methodologies and analytical approaches to improve the potential of the mass spectrometry based proteomics.The first part of this work focused on the development of the N-Termini proteomics. This topic was addressed with a specific N-Termini chemical derivatization based on TMPP. We have shown that our method allowed both specific N-Terminomics and classical proteomics studies in the same experiment.This N-Terminus methodology was applied to study the proteolytic cleavages of the exported proteins in P. falciparum, a parasite responsible for the malaria.In order to automatize the complex and tedious informatics processsing of the MS/SM data of ourTMPP based N-Terminomics method, we have introduced a new approach (named dN-TOP), based on the use of a stable isotope labeled TMPP which made now N-Terminome proteomics compatible with high throughput studies.The second part addresses quantitative aspects of proteomics. It describes the optimization of quantitative methods at the peptide level or at the protein level for five different proteomic studies in the context of protein complex subunits, targeted SRM based prion, quantification of monoclonal antibodies glycation and hemoglobin HbA2 for reference measurement methods standardization.

Mass spectrometry imaging (MSI) has been extensively used to produce qualitative maps of distributions of proteins, peptides, lipids, neurotransmitters, small molecule pharmaceuticals and their metabolites directly in biological tissue sections. Moreover, during the last 10 years, there has been growing demand to quantify target compounds in tissue sections of various organs. This thesis focuses on development and application of a novel instrument- and manufacturer-independent MSI software suite, msIQuant, in the open access format imzML, which has been developed specifically for quantitative analysis of MSI data. The functionality of msIQuant facilitates automatic generation of calibration curves from series of standards that can be used to determine concentrations of specific analytes. In addition, it provides many tools for image visualization, including modules enabling multiple interpolation, low intensity transparency display, and image fusion and sharpening. Moreover, algorithms and advanced data management modules in msIQuant facilitate management of the large datasets generated following rapid recent increases in the mass and spatial resolutions of MSI instruments, by using spectra transposition and data entropy reduction (at four selectable levels: coarse, medium, fine or superfine) before lossless compression of the data. As described in the thesis, implementation of msIQuant has been exemplified in both quantitative (relative or absolute) and qualitative analyses of distributions of neurotransmitters, endogenous substances and pharmaceutical drugs in brain tissue sections. Our laboratory have developed a molecular-specific approach for the simultaneous imaging and quantitation of multiple neurotransmitters, precursors, and metabolites, such as tyrosine, tryptamine, tyramine, phenethylamine, dopamine, 3-methoxytyramine, serotonin, gamma-aminobutyric acid (GABA), and acetylcholine, in histological tissue sections at high spatial resolution by matrix-assisted laser desorption ionization (MALDI) and desorption electrospray ionization (DESI) MSI. Chemical derivatization by charge-tagging primary amines of analytes significantly increased the sensitivity, enabling mapping of neurotransmitters that were not previously detectable by MSI. The two MSI approaches have been used to directly measure changes in neurotransmitter levels in specific brain structures in animal disease models, which facilitates understanding of biochemical mechanisms of drug treatments. In summary, msIQuant software has proven potency (particularly in combination with the reported derivatization technique) for both qualitative and quantitative analyses. Further developments will enable its implementation in multiple operating system platforms and use for statistical analysis.

Proline-rich antimicrobial peptides (PrAMPs) are promising candidates for the treatment of infections caused by highpriority human pathogens. Their mode of action consists of (I) passive diffusion across the outer membrane, (II) active transport through the inner membrane, and (III) inhibition of protein biosynthesis by blocking the exit tunnel of the 70S ribosome. We tested whether in vitro data on ribosomal binding and bacterial uptake could predict the antibacterial activity of PrAMPs against Gram-negative and Gram-positive bacteria. Ribosomal binding and bacterial uptake rates were measured for 47 derivatives of PrAMP Onc112 and compared to the minimal inhibitory concentrations (MIC) of each peptide. Ribosomal binding was evaluated for ribosome extracts from four Gram-negative bacteria. Bacterial uptake was assessed by quantifying each peptide in the supernatants of bacterial cultures. Oncocin analogues with a higher net positive charge appeared to be more active, although their ribosome binding and uptake rates were not necessarily better than for Onc112. The data suggest a complex mode of action influenced by further factors improving or reducing the antibacterial activity, including diffusion through membranes, transport mechanism, secondary targets, off-target binding, intracellular distribution, and membrane effects. Relying only on in vitro binding and uptake data may not be sufficient for the rational development of more active analogues.

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Metabolism is the sum of all chemical processes with the purpose to maintain life, as well as enable reproduction, in a living organism. Through the study of metabolism, increased understanding of pharmacological mechanisms and diseases can be achieved. This thesis describes several ways of doing so, including targeted analysis of selected metabolites and investigations of systematic metabolic differences between selected groups through pattern recognition. A method for exploring metabolic patterns in urine samples after intake of coffee or tea was developed. The methodology was later used with the aim to find biomarkers for prostate cancer and urinary bladder cancer. Furthermore, a fully automated quantitative method was developed for concentration measurements of the double prodrug ximelagatran and its metabolites in pig liver. The method was then used to study the roll of active transporters in pig liver cells. Moreover, a fundamental study was conducted to investigate how monitoring of small, doubly charged analytes can improve the limit of detection and precision in a quantitative method. The techniques used for the experiments were liquid separation coupled to electrospray mass spectrometry. Extra efforts were made to make the separation and the ionization as compatible as possible to each other for increased quality of the collected data.

Performing quantitative analysis of targeted bioactive compounds in biological samples, such as blood plasma, cerebrospinal fluid or extracts from pig liver, put high demands on the ruggedness of the method acquiring the results. In addition to the complexity of the sample matrix, the bioactive compounds targeted for analysis usually have low levels of natural abundance, further increasing the demand on the analytical method sensitivity. Furthermore, quantitation of analytes at trace levels in the presence of large amounts of interfering species in biofluids must aim for repeatable precision, high accuracy ensuring the closeness to the true values, a linear response spanning over several orders of magnitude and low limits of quantitation to be valid for monitoring disease states in clinical analysis. An analytical method most commonly follow a certain order of events, called the analytical chain, which includes; experimental planning, sampling, sample pre-treatment, separation of species, detection, evaluation, interpretation and validation, all equally important for the outcome of the results. In this thesis, the scope has been to create novel methods, or to refine already existing methods, in order to achieve even better performances of the different events in the analytical chain. One of the aspects has been to sample and enrich analytes in vivo by the use of solid supported microdialysis, giving the advantage of almost real-time monitoring of analyte levels within a living host with targeted selectivity due to the analyte affinity for solid particles. Another aspect to selectively clean and enrich analytes in a complex matrix has been developed and automated on-line by the use of a column-switching technique before the analytical separation. By using several steps of extraction and separation coupled on-line to selected detection by the use of a triple quadrupole mass spectrometer facilitates great selectivity of species. The mass spectrometer also gives the ability to distinguish between isotopically labelled analogues coeluting with the analytes yielding the necessary accuracy for quantitative evaluation. Both development of analytical methods and clinical applications has been performed, as well as improvements of existing techniques, all to improve the quantitation of trace levels of targeted analytes in biofluids.

From day to day, the role of HRMAS (High-Resolution Magic Angle Sinning) Nuclear Magnetic Resonance Spectroscopy (NMRS) in medical diagnosis is increasing. This technique enables setting up metabolite profiles of ex vivo pathological and healthy tissue. Automatic spectrum quantitation enables monitoring of diseases. However for several metabolites, the values of chemical shifts of proton groups may slightly differ according to the micro-environment in the tissue or cells, in particular to its pH. This hampers accurate estimation of the metabolite concentrations mainly when using quantitation algorithms based on a metabolite basis-set. The present word is devoted to the optimization of NMR metabolite basis set signals, particularly to the algorithms of chemical shift mismatch correction. Two sighal processing ("warping") methods were developed for simple and fast spectrum optimization : signal stretching/shrinking (resampling) and spectrum splitting. Then, another optimization method, QM-QUEST, coupling Quantrum Mechanical simulation and quantitation algorithms was implemented. The latter provides more robust fitting while limiting user involvement and respects the correct fingerprints of metabolites. Its efficiency is demonstrated by accurately quantitating signals from tissue samples of human brains with oligodendroglioma, obtained at 11.7 Tesla and spectra of cells acquired at 9.4T by HRMAS-NMR. As the necessity of fast NMR signal simulation based on quantum Mechanics is raised in the thesis, a part of the word is dedicated to an approximate method speeding-up the calculations. The algorithm based on spin-system fragmentation could become an important part of the QM-QUEST optimization method and will be implemented as an option of simulation in NMR-SCOPE, module of the jMRUI software package.