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Lc and Lc-Free Mass Spectrometry Applications in Non-Targeted Metabolomics for Disease Detection and Early Prediction

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Lc and Lc-Free Mass Spectrometry Applications in Non-Targeted Metabolomics for Disease Detection and Early Prediction LC AND LC-FREE MASS SPECTROMETRY APPLICATIONS IN NON-TARGETED METABOLOMICS FOR DISEASE DETECTION AND EARLY PREDICTION A Dissertation Presented to The Academic Faculty by Xiaoling Zang In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Chemistry and Biochemistry Georgia Institute of Technology August 2018 COPYRIGHT © 2018 BY XIAOLING ZANG LC AND LC-FREE MASS SPECTROMETRY APPLICATIONS IN NON-TARGETED METABOLOMICS FOR DISEASE DETECTION AND EARLY PREDICTION Approved by: Dr. Facundo M. Fernández, Advisor Dr. Ronghu Wu School of Chemistry and Biochemistry School of Chemistry and Biochemistry Georgia Institute of Technology Georgia Institute of Technology Dr. María E. Monge, Co-advisor Dr. Matthew Torres Centro de Investigaciones en School of Biology Bionanociencias (CIBION) Georgia Institute of Technology Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Dr. Julia Kubanek Dr. Mark Styczynski School of Chemistry and Biochemistry School of Chemical and Biomolecular Georgia Institute of Technology Engineering Georgia Institute of Technology Date Approved: July 23, 2018 Never give up on what you really want to do. The person with big dreams is more powerful than one with all the facts. -Albert Einstein, The World as I See It1 ACKNOWLEDGEMENTS There are many people who have helped and supported me a lot during my 6-year Ph.D. life at Georgia Tech. First of all, I want to express my deepest gratitude to my advisor, Dr. Facundo M. Fernández, for his guidance, support, patience and encouragement during my whole Ph.D. study. Under his mentorship over the last 6 years, I have gained so much knowledge and experience in mass spectrometry and have grown to become more independent and capable in handling the research projects. Secondly, I want to thank my co-advisor, Dr. María Eugenia Monge, who took much of her time in training me in metabolomics research during my beginning years in graduate school and have offered much advice and support to me in my Ph.D. research. Thirdly, I want to thank Dr. David Gaul, who have been very kind and patient in helping me to troubleshoot instrument problems and offering me advice and suggestions in my research projects. These people have been wonderful mentors and friend to me during my journey to pursue a Ph.D. degree and I wish to stay in contact with them and keep learning from them throughout my science career. I would also like to thank my committee members Dr. Julia Kubanek, Dr. Ronghu Wu, Dr. Matthew Torres and Dr. Mark Styczynski for their advisement and interest in my graduate research. I also want to pay thanks to the previous and present Fernández group members for their advice and help in my Ph.D. study, including Dr. Christina Jones, Dr. Manshui Zhou, Dr. Eric Parker, Dr. Jay Forsythe, Stephen Zambrzycki, José Pérez, Dr. Anyin Li, Dr. Li Li, Dr. Molly Hopper, Danning Huang, Dr. Rachel Stryffeler, Dr. Joel Keelor, Dr. Martin Paine, Dr. Matthew Bernier, Scott Hogan, Adam Kaylor, Kristin Mckenna, Dr. iv Yafeng Li, Dr. Rosana Alberici, Dr. Julía Culzoni, Dr. Jennifer Pittman, Dr. Marcos Bouza Areces, David Donndelinger, Dr. Prabha Dwivedi, and Dr. Chaminda Gamage. I want to pay special thanks to Dr. Christina Jones, who have passed her knowledge and experience in mass spectrometry to me during our overlapping years in the group and offered great help and encouragement to me during my Ph.D. life. I also want to thank Dr. Manshui Zhou for his guidance in career and life during my first year of graduate school and the remote contact in the following years in my Ph.D. study. In addition, I would like to thank all my friends who have accompanied me though my Ph.D. journey in many ways, giving me great help and encouragement, including Yu Cao, Ying Sha, Chu Han, Ran Lin, Annie Cheng, Zhishuai Geng, Haopeng Xiao, Zhanjun Guo, Cong Liu, Mengqi Cui, Miao Zhou, etc. Finally, I want to express my great gratitude to my family for their unconditional love and support during the 6 years of my Ph.D. life. My parents always trust and believe in me, and never doubt that I can achieve my goals. My dad always gives me laughter and comfort when I encounter difficulties, with his witty and humorous mind. They have helped in any way they can in my academic career and life to make me stronger, and I could not appreciate enough. v TABLE OF CONTENTS ACKNOWLEDGEMENTS iv LIST OF TABLES xi LIST OF FIGURES xiv LIST OF SYMBOLS AND ABBREVIATIONS xxii SUMMARY xxiiivi CHAPTER 1. INTRODUCTION 1 1.1 Abstract 1 1.2 MS-based Non-targeted Metabolomics for Disease Biomarker Discovery 1 1.3 Recent MS-based Non-targeted Metabolomics Studies in Human Disease Biomarker Discovery 6 1.3.1 Diabetes 6 1.3.2 Hepatocellular carcinoma (HCC) 9 1.3.3 Breast cancer 10 1.3.4 Cardiovascular disease 11 1.3.5 Parkinson disease 12 1.3.6 Respiratory diseases 13 1.3.7 Prostate cancer (PCa) 14 1.3.8 Human immunodeficiency virus (HIV) 15 1.3.9 Other diseases 15 1.4 Advances in Sample Preparation for Non-targeted Metabolomics for Disease Detection 16 1.5 MS-based Metabolic Profiling Platforms 23 1.5.1 GC-MS and LC-MS 23 1.5.2 CE-MS 25 1.5.3 High-throughput MS: direct and flow injection methods 25 1.5.4 Ambient MS and Imaging MS 26 1.5.5 IM-MS 29 1.5.6 Multiplexed activity metabolomics 30 1.6 Data Analysis 31 1.7 Metabolite Annotation and Identification 34 1.8 Pathway Mapping and Multi-omics Analysis 38 1.9 Limitations and Outlook 40 1.10 References 42 PART I: LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY BASED NON-TARGETED METABOLOMICS FOR DISEASE DETECTION AND EARLY PREDICTION 70 vi CHAPTER 2. PROSTATE CANCER DETECTION BY ULTRAPERFORMANCE LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY SERUM METABOLOMICS 71 2.1 Abstract 71 2.2 Prostate Cancer Diagnosis 72 2.2.1 Prostate Cancer Screening 72 2.2.2 Overview of Biomarker Discovery Approaches for Prostate Cancer Detection 73 2.3 Hypothesis 75 2.4 Materials and Methods 75 2.4.1 Chemicals 75 2.4.2 Cohort Description 76 2.4.3 Sample Preparation 77 2.4.4 Metabolic Profiling by UPLC-MS 77 2.4.5 Data Analysis 78 2.4.6 Metabolite Identification Procedure 80 2.5 Results and Discussion 81 2.5.1 Classification Performance 81 2.5.2 IVDMIA vs. PSA Diagnosis 85 2.5.3 IVDMIA Potential in Clinical Applications 87 2.5.4 Identification of Metabolites Used in the IVDMIA 92 2.5.5 Biological Relevance of the IVDMIA Metabolites 93 2.6 Conclusions 100 2.7 References 102 CHAPTER 3. EARLY DETECTION OF CYSTIC FIBROSIS ACUTE PULMONARY EXACERBATIONS BY ULTRAPERFORMANCE LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY EXHALED BREATH CONDENSATE METABOLOMICS 108 3.1 Abstract 108 3.2 Detection and Prediction of Cystic Fibrosis Acute Pulmonary Exacerbations 110 3.2.1 Introduction: Cystic Fibrosis Acute Pulmonary Exacerbations 110 3.2.2 Overview of Biomarker Discovery Approaches for Cystic Fibrosis Acute Pulmonary Exacerbation Detection and Prediction 112 3.3 Hypothesis 115 3.4 Materials and Methods 116 3.4.1 Chemicals 116 3.4.1.1 Pilot study 116 3.4.1.2 Large cohort study 117 3.4.2 Cohort Description 118 3.4.3 EBC Sample Collection and Preparation 120 3.4.4 Metabolic Profiling by UPLC-MS 121 3.4.5 Flow Injection-Traveling Wave Ion Mobility-MS Analysis 124 3.4.6 Data Analysis 125 vii 3.4.7 Metabolite Identification and Pathway Analysis 129 3.5 Results and Discussion for the Pilot Study 130 3.5.1 Data Processing 130 3.5.2 Classification Performance 133 3.5.3 Identification of Discriminant Metabolites and Their Biological Roles 138 3.6 Results and Discussion for the Large Cohort Study 142 3.6.1 Data Processing Results 142 3.6.2 Classification Performance 145 3.6.3 Identification of Discriminant Metabolites and Their Biological Relevance 149 3.6.4 Pathway Analysis 160 3.7 Conclusion 162 3.8 References 164 PART II: FLOW INJECTION-ION MOBILITY-MS AND DIRECT INFUSION- ION MOBILITY-MS BASED NON-TARGETED METABOLOMICS FOR DISEASE DETECTION AND EARLY PREDICTION 175 CHAPTER 4. FLOW INJECTION-TRAVELING WAVE ION MOBILITY- MASS SPECTROMETRY FOR HIGH THROUGHPUT PROSTATE CANCER METABOLOMICS 176 4.1 Abstract 176 4.2 MS-based Metabolic Profiling Strategies for High Sample Throughput 177 4.2.1 Direct Infusion and Flow Injection Mass Spectrometry in Metabolomics 177 4.2.2 Biomarker Discovery for Prostate Cancer Detection 178 4.3 Hypothesis 179 4.4 Materials and Methods 179 4.4.1 Chemicals 179 4.4.2 Cohort Description 180 4.4.3 Sample Preparation 180 4.4.4 Metabolic Profiling by FI-TWIM-MS 181 4.4.5 Compound Identification 182 4.4.6 Data Processing and Analysis 184 4.5 Results and Discussion 191 4.5.1 FI-TWIM-MS serum profiling 191 4.5.2 Compound Identification and Validation 193 4.5.3 Distribution of Compounds in the Mobility‒Mass Plot 200 4.5.4 Multivariate Analysis 207 4.5.5 Biological Roles of Discriminant Metabolites 213 4.6 Limitations of the Proposed Approach 214 4.7 Conclusion 216 4.8 References 218 CHAPTER 5. EXHALED BREATH CONDENSATE METABOLIC FINGERPRINTING FOR CYSTIC FIBROSIS STUDIES BY TRAVELING WAVE ION MOBILITY-MASS SPECTROMETRY 224 5.1 Abstract 224 viii 5.2 Mass Spectrometry-Based Approaches for Rapid Exhaled Breath Condensate Metabolomics 225 5.2.1 Exhaled Breath Condensate Metabolomics to Study Respiratory Diseases 225 5.2.2 Ambient and Atmospheric Pressure Ion Sources for EBC Metabolomics 225 5.3 Hypothesis 227 5.4 Materials and Methods 228 5.4.1 Chemicals 228 5.4.2 Sample Collection and Preparation 228 5.4.2.1 EBC for Method Development and Comparison 228 5.4.2.2 EBC of Controls and Cystic Fibrosis Patients 229 5.4.3 Transmission-Mode Direct Analysis in Real-Time (TM-DART) 229 5.4.4 Direct Infusion Electrospray Chemical Ionization (DI-ESCi) 230 5.4.5 Traveling Wave Ion Mobility Time-of-Flight Mass Spectrometry (TWIM-TOF-MS) 231 5.4.6 Data Analysis 232 5.5 Results and Discussion 233 5.5.1 TM-DART-TWIM-TOF-MS Optimization 233 5.5.2 Comparison of TM-DART and DI- ESCi MS for EBC Analysis 234 5.5.3 CF Sample Analysis and Multivariate Classification 246 5.6 Conclusions 252 5.7 References 253 CHAPTER 6.
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