(Georgia Institute of Technology, 2024-05-01)
Rainey, Markace Alan
Untargeted metabolomics explores the entirety of small molecules within biological samples, providing insights into metabolic alterations associated with various conditions. Standard methodologies like NMR and LC-MS are pivotal in identifying molecular markers but often fall short in fully deciphering the metabolic landscape due to limitations in accurately annotating a vast number of metabolites. This gap in annotation hampers the diagnostic application and biological interpretation of metabolomic data.
Ion mobility spectrometry (IMS) offers a solution by providing semi-orthogonal data that enhances metabolite annotation. IMS separates ions based on their collision cross-section (CCS), a property influenced by an ion's mass, shape, charge, and external factors like temperature and pressure. When integrated with mass spectrometry (MS), IMS aids in resolving ions’ of similar or identical mass-to-charge ratio (m/z), offering a refined approach to metabolite characterization.
This thesis focuses on employing computational strategies within LC-IM-MS workflows to facilitate rapid metabolite characterization. Chapter 1 outlines the challenges in metabolomics, specifically the limitations of current LC-MS workflows and the concept of the "dark metabolome." This introductory chapter provides the theoretical framework to better understand ion mobility and the use of quantitative-structural activity relationships to predict molecular properties. The chapter also discusses xenobiotics—external compounds impacting health—and their characterization challenges.
Chapter 2 introduces Collision Cross Section Predictor 2.0 (CCSP 2.0), a machine learning-based tool for predicting ion mobility-derived CCS values. CCSP 2.0, developed to improve the accuracy and ease of CCS prediction, is evaluated for its efficacy in enhancing annotation accuracy in LC-MS workflows. It utilizes a support-vector regression model and incorporates a comprehensive library of molecular descriptors, demonstrating superior prediction accuracy and utility in reducing false positive annotations.
Chapter 3 presents a workflow for automated detection of polyhalogenated xenobiotics in biological samples using LC-IM-MS. This approach combines CCS to m/z ratios, Kendrick mass defect analysis, and CCS prediction to filter isomeric candidates. A case study on the detection of per- and polyfluorinated alkyl substances in human serum exemplifies the workflow's effectiveness.
Chapter 4 describes an analytical chemistry experiment for undergraduate students, focusing on laser-induced breakdown spectroscopy (LIBS) and its application in data science education. This chapter emphasizes enhancing students' programming literacy and analytical skills through hands-on experiments and analysis using Jupyter Notebooks. The experiment, adaptable to various curricula, showcases real-world applications of LIBS, including its use in space exploration.
Chapter 5 summarizes key findings from the research, discussing the implications of integrating computational methods in metabolomics and the potential advancements in ion-mobility mass spectrometry. Future research directions are proposed to further explore and refine these methodologies.
Appendix A explores an on-going project aimed at predicting analyte concentrations without standard calibration curves using machine learning. This approach predicts relative ionization efficiencies of lipids from their structural properties, demonstrating the potential of machine learning in streamlining quantitative analyses in metabolomics.
In conclusion, this thesis underscores the importance of computational approaches in enhancing metabolite annotation and characterizing xenobiotics, contributing valuable tools and methodologies to the field of metabolomics.
(Georgia Institute of Technology, 2023-01-25)
Seaton, Kenneth Marshall
Our search for extraterrestrial life is enabled by both the development and maturation of chemical instruments capable of life detection and the characterization of extraterrestrial analogues on Earth representing the extreme conditions present on other planetary bodies. Yet, despite the extreme physical and chemical conditions present in these extraterrestrial analogues (salinity, acidity, pH, etc.), terrestrial life finds a way to adapt and even thrive. Here, I present an outline of my thesis, the goals of which are twofold. The first portion of my thesis focuses on the development of analytical methods to enable the quantitative compositional analysis of these extraterrestrial analogue environments using capillary electrophoresis with laser-induced fluorescence detection, enabling a better understanding of the relative abundances of amino acids present in extreme environments hosting complex ecosystems. The second part of my thesis explores the development of a New Frontiers class mission concept for the Saturnian moon Enceladus to (1) directly search for life through a combination of in situ chemical compositional analysis and (2) contextualize these measurements through a combination of geophysical and geomorphological investigations to assess the habitability of Enceladus. Reviews of instrumentation for planetary chemical and astrobiological studies are presented, followed by the mission concept study undertaken.