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School of Chemistry and Biochemistry
School of Chemistry and Biochemistry
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ItemInterlaboratory Comparison of a Complex Targeted Assay: Improving Consistency and Reliability in Metabolomics Analyses(Georgia Institute of Technology, 2023-12-07) Phillips, Emily R.Ideal isotope-labeled internal standards for analysis via targeted metabolomics approaches are presented for negative and positive ion modes for both hydrophilic interaction liquid chromatography (HILIC) and reverse phase liquid chromatography (RPLC) chromatography coupled to mass spectrometry. These best performing analytes (BPA) were deduced after experimentation from a collaborative research project involving six top metabolomics research laboratories in the country. These results are detailed in this work, supported by observed behaviors of included chemical classes and chromatographic behaviors, and align with the group hypothesis and expectations
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ItemDeveloping Ion Mobility-Mass Spectrometry Techniques to Increase Sensitivity and Resolution for Carbohydrate Mixture Analysis(Georgia Institute of Technology, 2021-08-19) Mckenna, Kristin RuthThe origin and prebiotic functions of carbohydrates are not well characterized. Limitations in analytical methodology to analyze the regio- and stereochemistry of carbohydrates in complex mixtures exacerbates this problem. Several ion mobility-tandem mass spectrometry techniques were developed to study model prebiotic carbohydrate reactions. Covalent derivatization with 3-carboxy-5-nitrophenylboronic acid (3C5NBA) was determined to improve these characterizations and allow for more complete structural analysis by tandem mass spectrometry. Cyclic ion mobility spectrometry improved the ability to distinguish four monosaccharide and eight disaccharide isomers as their 3C5NBA derivatives. Organic acids were also analyzed for their potential to improve carbohydrate separations as noncovalent modifiers. The optimal organic acid modifiers were determined to be L-malic acid and N-methyl-D-aspartic acid, which were further characterized through a more sensitive, Fourier transform-based ion mobility method.
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ItemNew Tools for Rapid Mass Spectrometric Screening(Georgia Institute of Technology, 2021-07-28) Zambrzycki, Stephen C.The development of new rapid screening tools and the assessment of current technologies helps explore new realms of chemistry and ensure the quality of chemical products. Mass spectrometry is a powerful analytical tool that measures the mass-to-charge ratio of ionized analytes. Two new ambient plasma ionization tools were developed to rapidly ionize samples for mass spectrometry. Portable and high-throughput mass spectrometry was also evaluated for its performance in pharmaceutical and cellular therapy quality screening. In Part 1 of this thesis, new tools were developed for Vacuum-assisted Plasma Ionization (VaPI). First, VaPI was built for the Waters Synapt G2S, an ion mobility mass spectrometer. Then, an aerosolizer and a scanning mobility particle size analyzer was coupled to the VaPI source to create Aero-VaPI. Simultaneous acquisition of the aerosol diameter, ion mobility, and mass-to-charge with Aero-VaPI illustrates the breadth of information that can be acquired in real time for simulated pre-biotic aerosol chemistries. A pyrolysis device was also built for VaPI to rapidly screen and characterize pyrolyzed polymers such as nylons. The combination of measurements in pyrolysis temperature, ion mobility, and mass-to-charge show how new potential molecular structures of pyrolyzed nylons were discovered. In Part 2 of this thesis, portable mass spectrometry was evaluated alongside 11 other portable tools for the rapid screening of small molecule pharmaceuticals. The pros and cons of each device were noted. The Waters QDa mass spectrometer had the highest sensitivities in the study, but it was not deemed suitable for field testing due to its resource requirements and mechanical complexity. Finally, a workflow was developed for matrix assisted laser desorption ionization (MALDI) mass spectrometry to rapidly assess the quality of cellular therapies. Preliminary data was acquired to demonstrate the speed and automation of the MALDI and data processing workflow for cellular therapy quality screening.
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ItemCCSP 2.0: An Open-Source Jupyter Tool for the Prediction of Ion Mobility Collison Cross Sections in Metabolomics(Georgia Institute of Technology, 2021-05) Watson, Chandler AveryTandem mass spectrometric methods revolutionized the chemical identification landscape, allowing serums and molecules to be separated in two or more dimensions. Ion Mobility Mass Spectrometry workflows combined with liquid or gas chromatographic separation have continued to progress chemical identification and further increase the amount and confidence of these identities. Such advancements have also given birth to a new molecular descriptor: the Collision Cross Section, sparking heavy interest in the analytical-computational chemistry to compile these values for known molecules. The main shortcoming has been predicting the CCS value for new molecules such as Poly-Fluorinated Alkyl Sub-stances. Preliminary prediction software has revealed that predicting CCS values for this molecular class is possible, but it can prove temporally, computationally, and financially expensive between different licenses and genetic algorithm. This work combines open-source Python modules (NumPy, Mordred, Pandas, etc.) to construct an alternative workflow that is completely free and capable of running on a mid-specification laptop within a half hour. Using the M-H and combined M+H and M-H datasets taken from the McClean CCS Compendium, median prediction errors of 2.07% and 1.84%, respectively, were found using Support Vector Regression within 5 minutes on a mid-spec laptop, satisfying the 2.50% benchmark. This overall success illustrates the power and versatility of this workflow to produce low errors with datasets as large as 1300+ molecules and as few as 37. This script can be distributed on file-sharing sites like GitHub where other users may customize the free source code to fit their experimental needs.
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ItemLipid Biomarker Alterations Following Mild Traumatic Brain Injury(Georgia Institute of Technology, 2021-01-22) Gier, Eric C.The work presented in this thesis highlights the current state of biomarker research for traumatic brain injury (TBI) and seeks to investigate the potential of novel lipid biomarkers for TBI. Awareness and research interest surrounding TBI have been heightened in recent years due to increased media coverage and epidemics within the military, athletic organizations, accident victims, the elderly, and the general population. The heterogeneous nature of TBI makes diagnosis and biomarker discovery particularly challenging as severities and exposure events vary widely. The first two chapters serve to outline the current state of TBI regarding its impact on human life, methods of diagnosis, injury mechanisms, and current research in the field. These chapters ultimately highlight a current gap between modern research and clinical implementation that is being closed rapidly through omics research. The final two chapters describe the research conducted over the past year to identify potential lipid biomarkers of TBI. Two predictive lipid panels were developed to classify injured and uninjured Sprague-Dawley rat serum across two injury severities and three acute postinjury timepoints. Identified lipid features from the proposed panels consist primarily of phosphatidylcholine and triacylglyceride species which warrant future investigation as proposed biomarkers of TBI. Ultimately, future work is needed to validate the features identified as potential biomarker candidates and to connect the lipid responses discovered in serum to alterations in the brain lipid profile to gain a more holistic picture of TBI.
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ItemApplications of High-resolution Mass Spectrometry and Matrix-assisted Laser Desorption/Ionization Mass Spectrometry Imaging-based Non-targeted Metabolomics in Biomarker Discovery(Georgia Institute of Technology, 2021-01-19) Huang, DanningMass Spectrometry (MS) is the most commonly used technology in metabolomics studies. The high sensitivity of MS enables the detection of low abundance metabolites that are below the detection threshold of other analytical platforms, and high resolution greatly reduces spectral overlaps. When coupled with separation techniques, such as ultra- performance liquid chromatography (UPLC), spectral complexity is greatly reduced and metabolic chemical properties can be revealed. Overall, MS-based non-targeted metabolomics allows the detection and identification of a wide range of metabolites with high sensitivity and high resolution. In this thesis work, UPLC-MS based non-targeted metabolomics was used to investigate metabolic alterations and discover potential biomarkers for high-grade serous carcinoma (HGSC) and medulloblastoma (MB). The evaluation of two leading analytical platforms, Orbitrap ID-X and 12T solariX FT-ICR mass spectrometers, in mass accuracy and relative isotope abundance (RIA) measurements of 13C1 and 18O1, and how these affect the assignment of the correct elemental formulae was performed. In addition, a multi-omics approach was performed to discover candidate critical quality attributes (CQA) that are predictive of MSC immunomodulatory capacity. Taken together, this thesis work has contributed meaningfully to the metabolomics field by discovering potential biomarkers for HGSC and MB diseases, providing the first comparison between high- resolution FT-ICR-MS and Orbitrap Tribrid MS platforms for elemental formulae annotation purposes. Furthermore, the thesis work also provides candidate CQAs that are predictive of MSC immunomodulatory capacity, bringing the potential to inform future manufacturing strategies. This multi-omics approach to CQA discovery can also be translated into other cell therapies.
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ItemUtilization of mass spectrometric techniques to identify novel lipid biomarkers of traumatic brain injury and other practical applications(Georgia Institute of Technology, 2020-01-06) Hogan, Scott R.The primary focus of this thesis centers around the examination of lipids to aid in the diagnosis, prognosis, and mechanistic understanding of traumatic brain injury (TBI). Lipids are important signaling molecules and play a critical role in energy storage as well as membrane structure and organization. Using mass spectrometry (MS) based methods to perform non-targeted metabolomic experiments with a focus on extraction of lipid metabolites, a rodent model is investigated to explore the feasibility of developing lipid biomarker panels to classify injury post-hoc across multiple severities. The developed methods are then used to study another biological system in order to show the broad applicability of lipidomics by investigating the effect of Karenia brevis allelopathy on two phytoplankton competitors.