The quest to achieve high resolution in ion mobility spectrometry (IMS) has continued to challenge scientist and engineers in the field of separation science. The low resolution presently attainable in IMS has continued to negatively impact its utility and acceptance. Until now, efforts to improve the resolution have mainly focused on better instrumentation and detection methods. However, since the resolution of IMS is diffusion limited, it makes sense to address this limitation in order to attain high resolution. This dissertation presents a new IMS technique, which utilizes a high electric field and opposing high gas flow velocity with the aim to improve resolution. This approach essentially reduces the residence time of ions in the analyzer. This new technique is called "counter-flow ion mobility analysis" (CIMA). Theoretical modeling of this new technique predicted that a resolution of over 1000 is possible, which is over one order of magnitude better than that of conventional IMS techniques currently used. A wind tunnel was designed and constructed to produce a plug gas flow profile that is needed for CIMA. The test region of the wind tunnel was used as the CIMA analyzer region and was constructed from power circuit boards, PCBs, (top and bottom walls) and conductive plastic side walls. An inclined electric field was created by applying suitable voltages to multiple electrode traces on the PCBs. This inclined field, when resolved into its x- and y-components, was used to oppose the counter-gas flow and transport the ions to the detector, respectively. The results obtained did not show an improvement over conventional IMS techniques because of a limitation in the voltage that could be applied to the analyzer region. However, the results predict that high resolution is possible if (1) the ratio of the electric fields in the horizontal (x direction) to the vertical (y direction) is within the range of 2--0.5, (2) very high electric field and high gas flow velocities are applied, and (3) wall effects in the counter-flow gas profile are eliminated. While the resolution obtained using the present instrumentation is far from what was predicted, the foundation for ultimately achieving high resolution has been laid. The use of a wind tunnel has made the instrumentation possible. As far as the author knows, this is the first time a wind tunnel has been used in chemical measurement instrumentation. Chapter 5 of this dissertation, reports a method developed for predicting the reduced mobility constants, of chemical compounds. This method uses a purely statistical regression analysis for a wide range of compounds which is different from similar methods that use a neural network. The calculated value for this method was 87.4% when calculated values were plotted against experimental K0 values, which was close to the value for the neural network method (i.e., 88.7%).

La sensibilité et la profondeur de l'analyse protéomique sont limitées par les ions isobares et les interférences qui entravent l'identification des peptides de faible abondance. Lorsque nous analysons des échantillons de grande complexité, une séparation extensive de l'échantillon est souvent nécessaire pour étendre la couverture protéomique. Ces dernières années, la spectrométrie de mobilité ionique à forme d'onde asymétrique à haut champ (FAIMS) a gagné en popularité dans le domaine de la protéomique pour sa capacité à séparer les ions isobares, à améliorer la capacité de pic et la sensibilité de la spectrométrie de masse (MS). Nous rapportons ici l'intégration d'un appareil FAIMS Pro™ à un Q-Exactive HF™ ainsi qu'un spectromètre de masse Orbitrap Exploris 480™. Des expériences protéomiques sur des digestions d'extraits protéiques issues de cellules Hela à l'aide d'un spectromètre de masse avec FAIMS ont amélioré le rapport signal sur bruit (S/N) et réduit les ions interférents, ce qui a entraîné une augmentation du taux d'identification des peptides de plus de 42 %. FAIMS est également combiné avec le fractionnement ionique segmenté (SIFT), qui utilise tour à tour une fenêtre de 100 ~ 300 m/z au lieu de la large plage traditionnelle (700 ~ 800 m/z), augmentant ainsi la profondeur de la couverture protéomique tout en réduisant la proportion de spectres MS/MS chimériques de 50% à 27%. Dans l'analyse quantitative, nous démontrons l'application de FAIMS pour améliorer les mesures quantitatives lorsque le marquage peptidique isobare est utilisé. Par rapport aux expériences LC-MS/MS conventionnelles, la combinaison des expériences FAIMS et SIFT réalisées sur un modèle à deux protéomes a montré une amélioration de 65 % de la précision des mesures quantitatives. Les digestions tryptiques d'extraits protéiques de différentes lignées cellulaires du cancer colorectal ont été utilisées pour l'évaluation de stratégie combinée FAIMS et SIFT sur un spectromètre de masse Orbitrap Exploris 480™ offre un gain d'identification de 70 % par rapport à l'approche conventionnelle et combinée aux données transcriptomiques elle facilite l’identification de variants protéiques. / The sensitivity and depth of proteomic analysis in mass spectrometry (MS) is limited by isobaric ions and interferences that hinder the identification of low-abundance peptides. For high complexity samples, extensive separation is often required to expand proteomic coverage. In recent years, high-field asymmetric waveform ion mobility spectrometry (FAIMS) has gained popularity in the field of proteomics for its ability to resolve confounding ions, improve peak capacity, and sensitivity. This thesis presents the integration of a FAIMS Pro™ interface with electrical and gas embedded connections to a Q-Exactive HF™ as well as an Orbitrap Exploris 480™ mass spectrometer. Proteomic experiments on tryptic digests of HeLa cell line using a FAIMS integrated mass spectrometer improved signal-to-noise ratio (S/N) and reduced the occurrence of interfering ions. This enabled a 42% increase in peptide identification rate. Also, FAIMS was combined with segmented ion fractionation (SIFT), which in turn scans with windows of 100~300 m/z width instead of the traditional width (700~800 m/z), further increasing the depth of proteome coverage by a reducing from 50% to 27% in terms of MS/MS chimeric spectra numbers. The application of FAIMS gain improvement on quantitative measurements with TMT labeling method is presented. Compared to conventional LC-MS/MS tests, the combination of FAIMS and SIFT experiments showed a improvement by 65% in quantitative accuracy when performed on a human-yeast two-proteome model. As an application of the method, the tryptic digests from different colorectal cancer cell lines were used for the evaluation. FAIMS-SIFTcombined strategy on an Orbitrap Exploris 480™ mass spectrometer provides a 70% gain in identification compared to the conventional LC-MS/MS approach for the same sample amount and instrument time. This enhanced sensitivity facilitates single amino acid mutations confirmed by RNAseq analyses.

Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.