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School of Chemistry and Biochemistry

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College of Sciences

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Integrating Machine Learning Solutions into Untargeted Metabolomics and Xenobiotics Workflows

(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.
Interlaboratory 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
Quantitative Analysis of Inorganic Chemical Species on an Impact-Penetrator Module

(Georgia Institute of Technology, 2023-04-27) Govindaraj, Chinmayee

In-situ planetary missions to astrobiologically relevant icy worlds with unique access to subsurface samples for habitability analyses are typically carried out using complex and costly soft lander platforms that are large, heavy and power-intensive. Smaller, more power-efficient, lower cost payloads like impactors and penetrators are innovative ways to address these issues. The Ice Shell Impact-Penetrator (IceShIP) is a penetrator science payload under development since 2015. Icy Moon Penetrator Organic Analyzer (IMPOA), is a sub payload of IceShIP, and is a first-of-its-kind, compact science platform capable of sustaining ultra high-g loads of up to 50,000 g, enabling subsurface sampling on icy ocean worlds like Europa, Enceladus, or even Martian polar regions. The IMPOA platform can enable detection of low concentration organic species using the principle of laser-induced fluorescence. To upgrade the impact resistance of the IMPOA payload, several design changes were made, the most prominent of them was to move away from glass microfluidic chip design to polymer-based architecture. To upgrade the scientific capabilities of IceShIP, inorganic detection capability was added by employing the principle of capacitively coupled contactless conductivity detection (C4D). The benchtop instrument was tested using lab-generated Europa-relevant samples, and miniaturized to fit within the IceShIP module. This was named the Micro Inorganic Conductivity Detector for Europa (MicroICE). A complete polymer body version of MicroICE was designed and tested, called the Polymer-based Contactless conductivity Detector for Europan Salts (PolyCODES). PolyCODES is the first C4D device to use the PEDOT:PSS conductive polymer and was a design choice made to increase the potential for impact resistance. MicroICE was equipped with an automated, two-channel microfluidic routing mechanism, called the Solenoid-based actuator assembly for Impact- Penetrators (SIP). The SIP, integrated with MicroICE or PolyCODES demonstrated a low mass, small size, low power instrument at TRL 3. The upgraded IceShIP canister is geared towards high acceleration space flight missions. Future design upgrades could include the integration of microchip capillary electrophoresis. Success during impact tests of components configured to functionally conduct analytical measurements will elevate the readiness to a true TRL of 4 value.
Metabolomics and Machine Learning for Early-Stage Cancer Diagnosis

(Georgia Institute of Technology, 2023-04-25) Sah, Samyukta

Amongst all omics sciences, metabolomics is the most recent and is rapidly advancing as one of the predominant methodologies for early disease diagnosis and precision medicine. Metabolomics involves the high-throughput analysis of low molecular weight metabolites and their interactions with biological networks. Metabolomics studies involve measuring the abundance of hundreds to thousands of metabolites in biological fluids, tissues, and cells, and provide instantaneous snapshot of the status of the biological system. For cancer research, metabolomics is a powerful platform as it enables the identification of metabolic alterations useful in diagnosis, prognosis, and therapeutics. However, because of the complexity of the metabolome, no single analytical platform can capture the complete metabolic profile of a biological system. Thus, the use of complementary techniques allows for a more comprehensive analysis. Mass spectrometry (MS) is one of the commonly used techniques in metabolomics. Due to its high sensitivity and high-resolution, MS based metabolomics studies can provide a wide breath of knowledge of cancer metabolism. MS is typically coupled with separation techniques such as liquid chromatography (LC) or capillary electrophoresis to reduce the spectral complexity. MS based metabolomics studies generate a large amount of data and thus the use of machine learning (ML) methods are becoming increasingly popular to interpret and visualize metabolomics data and uncover new biological insights into disease biology. This thesis work focuses on MS based metabolomics analysis for improved understanding of cancer metabolism. As diagnosis of ovarian cancer (OC) remains an unmet clinical challenge, the main focus of this thesis is on uncovering the metabolic profile of OC and identifying potential biomarkers for its early diagnosis. In addition, ML methods were applied to identify the urinary metabolic profile of renal cell carcinoma (RCC). Kidney cancer is one the most lethal urinary cancers out of which 90% are renal cell carcinomas (RCC). Diagnosis of RCC is typically performed with expensive imagining tests and biopsies which not only are invasive but also are prone to sampling errors. Due to the proximity of the tumor to the urine, urine metabolomic profiling provides an excellent opportunity to study the metabolic rewiring of RCC. Chapter 1 introduces OC and the application of metabolomics to identify the metabolic reprogramming associated with OC pathogenesis. An overview of the analytical platforms and techniques in metabolomics is given, and the general workflow, including the use of ML in metabolomics data handling, is outlined. The commonly used statistical approaches and ML assisted metabolomics analysis for cancer research are provided. Furthermore, an overview of various MS based metabolomics studies of OC is given, describing the metabolic phenotype of OC. The diagnostic potential of the metabolite panels, as identified by the described studies, is also given. Finally, the biological implications of the potentially important metabolic alterations in OC are described. Chapter 2 presents a longitudinal serum metabolomics profiling of a triple-mutant (TKO) mouse model of high-grade serous carcinoma (HGSC), a subtype of OC. Two complementary ultrahigh performance liquid chromatography (UHPLC) – MS techniques were used to profile both the serum lipidome and the polar metabolome of TKO and TKO control mice. Sequentially collected serum samples from TKO mice starting from 8 weeks of age until death were analyzed, and a comprehensive metabolic map associated HGSC onset, development, and progression was revealed. These UHPLC-MS experiments were complemented with spatial lipidomic profiling of the entire reproductive system of the TKO mice. Ultrahigh-resolution matrix assisted laser desorption/ionization (MALDI) mass spectrometry was used to visualize the lipid alterations in HGSC. The longitudinal analysis of the serum metabolome revealed specific temporal trends for 17 lipid classes, as well as for other polar metabolites including amino acids, TCA acids, bile acids, bile acid derivatives, progesterone metabolites, and metabolites of arachidonic acid. Spatial lipidomic experiments provided a map of the metabolic alterations of HGSC that showed accumulation and reduction of various lipid classes, indicating the observed changes in the serum are in fact a signature of OC. Chapter 3 describes the serum lipidomic alterations in another mouse model of HGSC. For this study, sequentially collected serum samples from a double knockout mouse model (DKO) of HGSC were analyzed with reverse phase (RP) UHPLC-MS. Similar to the serum collection protocol followed for the TKO mouse in chapter 2, serum samples from these DKO mice were collected biweekly starting from 8 weeks of age until death or humane endpoint for sacrifice. Longitudinal lipidomic alterations in the DKO were investigated via machine learning (ML) approaches. A hierarchical clustering analysis identified 4 main lipid trajectory clusters. These clusters mainly consisted of glycerophospholipids including ether linked phospholipids and sphingolipids. Of note, early disease stages were marked by changes in phospholipids and sphingolipids while late disease stages showed more diverse changes. Furthermore, the ML based approaches characterized lipidome alterations at various disease stages. Five ML algorithms were used for classification purposes and a 5-lipid panel discriminated early stage DKO mice from DKO control mice with the area under curve (AUC) value of 0.80, indicating the possibility of employing circulating lipid markers for early detection. Animal models of OC provide a simpler, better-controlled model to study metabolic alterations which can later potentially be translated to humans. In addition, as obtaining early-stage OC samples from humans is challenging, animal models provide a rare opportunity to study early disease stages. However, the goal of these metabolomics studies is to develop clinically relevant biomarkers that aid in selecting therapeutic strategies, and thus investigating and validating metabolic changes in humans is crucial for assessing their translational implications. Additionally, besides detecting metabolic alterations in OC compared to controls with no gynecological malignancies, distinguishing OC from other benign or cancerous gynecological malignancies remains a challenge with significant implications on patient survival. Better results are observed when women with OC are correctly diagnosed and ensured the optimal treatment. In Chapter 4, a comprehensive serum lipidome profiling of OC and various other gynecological malignancies including benign ovarian tumor, benign uterine tumor, and cervical cancer (non-OC), is performed with RP UHPLC-MS. This study used serum samples from two independent tissue banks (Dongsan Hospital Human Tissue Bank and Gangnam Severance Hospital Gene Bank) in South Korea. Age matched patient cohort included 208 women with OC of various histological types and disease stages (mean age 51.9 years) and 137 non-OC patients (mean age 49.9 years). Among the OC patients, 93 women had early stage (I and II) OC out of which 31% were of serous histology. Serous tumors accounted for 86% of late stage (III and IV) cases while the remaining 14% of the cases included clear cell, transitional, mucinous, and carcinosarcoma subtypes. The serum lipidome of OC showed most lipid species to be reduced in OC with some lipid classes, including ceramides and triglycerides, showing increased abundance. Stage-stratified analyses were conducted to investigate if lipids show distinct alterations in early stage (I and II) or late stage (III and IV) OC versus non-OC. While both early OC or later OC vs. non-OC conditions showed alterations in various lipid classes including phospholipids and sphingolipids, changes in certain lipid species such as diglycerides, fatty acids and cholesterol were distinctive of advanced stage OC vs. non-OC. Moreover, results indicated that lipidome alterations in OC were present when the cancer was localized, and those changes amplified as the disease progresses. Besides, OC of different histological types showed similar lipidome changes for most lipid classes. Additionally, a panel of 10 top discriminating lipids, consisting mainly of ether phospholipids, phosphatidylcholine species, and one sphingomyelin species, was selected that differentiated OC from non-OC conditions with AUC of 0.91 and 0.74 in training and test sets, respectively. These results provided a systemic analysis of circulating lipidomic alterations in OC patients, highlighting the potential of lipids as a complementary class of blood-based biomarkers for OC diagnosis. Although UHPLC-MS remains one of the major tools in metabolomics studies, the use of complementary analytical techniques holds promise to significantly improve metabolite coverage. Capillary electrophoresis (CE) coupled with high-resolution MS (HRMS) offers high selectivity and sensitivity for charged polar metabolites and provides a complementary view of the metabolome. Recent advances in CE-MS technologies include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI), enabling fast and sensitive analysis. In Chapter 5, a microchip CE (µCE)-MS based targeted metabolomics assay was developed to analyze 40 key metabolites related to cancer progression. A commercial µCE-MS system from 908 devices (Boston MA) was coupled with high-resolution accurate mass Q Exactive plus mass spectrometer (Thermo Fisher, MA). The developed method was applied to biweekly collected serum samples from 3 TKO and 3 TKO control mice of HGSC. The µCE-MS method produced sharp baseline resolved peak shapes and calibration curves maintained good linearity. When applied to serum samples from the TKO mouse, 30 metabolites were successfully detected. These included amino acids, amino acids derivatives, and nucleotides. The data collected from this µCE-MS platform were compared with the previously collected UHPLC-MS data in Chapter 2. Time-resolved data for the 5 metabolites detected with both platforms showed identical temporal trends, indicating the µCE-MS method performed satisfactorily in capturing granular time-course data in complex biological matrices. Chapter 6 presents the major conclusions drawn from this thesis work. Metabolic alterations in the two animal models of OC are discussed, highlighting the similarities and differences observed in each case. A summary of conclusions from the lipidomic profiling of human ovarian cancer samples is presented, and the results from animal models of OC are compared with alterations observed in humans. Possible future directions to continue with this work are also discussed. Appendix A presents a pilot study to investigate the feasibility of liquid-based Papanicolaou (Pap) tests as biospecimens for OC detection. As past studies have observed OC cells in Pap tests, we hypothesized that lipid alterations that are observed in serum and ovarian tissues can also be present in cells obtained in a Pap test. To test this hypothesis, a UHPLC-MS based lipidomics pipeline is developed and a liquid-based Pap test sample from a woman with normal cytology was analyzed. Cells were pelleted out followed by an extraction protocol to extract lipids. Results showed that lipids can be detected in these cell pellets. The detected lipids included sphingolipids, phospholipids, ether linked phospholipids, and glycerolipids. These results suggest a possibility of developing non-invasive techniques for OC diagnosis and detection. Appendix B outlines collaborative metabolomics work that combined both nuclear magnetic resonance (NMR) and UHPLC-MS datasets and used ML to discover urine-based candidate biomarkers for RCC prediction. Urine samples from patients at Emory University Hospital with a solid renal mass that were confirmed to be RCC were used for this study. Controls were identified during the annual physical exam. The study cohort consisted of 105 RCC patients and 179 controls, which is larger than most previously published studies. ML based feature selection led to a panel of seven metabolites that discriminated RCC from controls with 88% accuracy, 94% sensitivity, 85% specificity and AUC of 0.98 in the test cohort. This panel consisted of metabolites that were detected on the MS platform. High resolution MS and tandem MS experiments were conducted to assign metabolite annotation. The annotated metabolites included 2-phenylacetamide, lysine-isoleucine or lysine-leucine, hippuric acid, a mannitol hippurate derivative, N-acetyl-glucosaminic acid and two exogenous metabolites: 2-mercaptobenzothiazole, and dibutylamine. Furthermore, Appendix C presents a machine learning based urine metabolomics study to identify the metabolites associated with RCC staging and to estimate RCC tumor size. The same dataset that was collected for the analysis in Appendix B was used in this study. The metabolites associated with RCC progression included 3-hydroxyanthranilic acid, lysyl-glycine, glycine, and citrate. Overall, this multiplatform metabolomics study provided a broad coverage of metabolites and provided a complementary view of the urine metabolome of RCC. These results suggest the use of urinary metabolomics profiling as a promising platform for RCC detection.
Developing Ion Mobility-Mass Spectrometry Techniques to Increase Sensitivity and Resolution for Carbohydrate Mixture Analysis

(Georgia Institute of Technology, 2021-08-19) Mckenna, Kristin Ruth

The 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.
New 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.
CCSP 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 Avery

Tandem 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.
Lipid 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.
Applications 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, Danning

Mass 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.
Utilization 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.