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Introduction of Metabolomics: An Overview

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Metabolomics

Abstract

Metabolomics is a rapidly emerging field, whose progress has accelerated over the last decade. As a discipline, it has moved on from the refinement of analytical techniques and methodology toward a new phase where it is being applied to explore fundamental biological and clinical questions. We will address the evolution of metabolomics into an increasingly popular discipline and attempt to show its potential. This includes an overview of the technologies at the forefront of the field and other analytical techniques that provide information about the metabolome in diverse model systems. We will discuss mass spectrometry, NMR, and FTIR and chromatographic techniques that are critical to experimental protocols. Targeted and untargeted metabolomic study design is addressed along with some best practices and resources for practitioners of either methodology. A few technical and analytical hurdles remain, but new solutions continue to be presented. The promise of contemporary metabolomics is to contribute to a systems level understanding of biology and to usher in the era of precision medicine.

In this chapter, we introduce the field of metabolomics in the context of other omics disciplines. We emphasize the trend increased usage of omics technologies but metabolomics especially. We review the need for big-data approaches to modern science and their use. We define the terminologies used in metabolomics and describe the types of methodologies used in the context of the history of the discovery of the electron and the development of mass spectrometry. We discuss the applications of metabolomics and the development of the field.

The next section is a detailed review of methodologies in metabolomics regarding instrumentation (MS, NMR, FTIR, and other techniques). We then discuss separation techniques including principles of operation, covering the use and advantages of different solvent systems such as GC-MS, LC-MS, CE, and IMS. We then offer an overview of sample preparation as it pertains to metabolomics and list the cautions that must be taken when working with unstable or volatile metabolite extracts. We cover different study designs including targeted and untargeted metabolomics.

The final section details challenges that are common to all omics technologies, followed by challenges specific to metabolomics. We discuss technical hurdles to overcome and how they are currently addressed in the field. We conclude with a summary of the chapter and a look at the coming chapters and briefly discuss the coming advances in the field.

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Notes

  1. 1.

    Note that we refer to omics technologies with reference to the actual methodologies – as opposed to the general concept of the comprehensive classification of a field.

  2. 2.

    This is widely accepted to refer to biomolecules <1500 Da.

  3. 3.

    We will focus on metabolomics exclusively in this text, but we refer the reader to an excellent text published by Springer for a thorough examination of proteomics [97].

  4. 4.

    The Orbitrap Mass Analyzer is a version of the ion trap that is licensed exclusively to ThermoFisher Scientific. It is worth mentioning here because of its utility and popularity in metabolomics [98].

  5. 5.

    Almost every MS manufacturer freely provides detailed information about the configuration of the detectors fitted to their instruments. There are many variations on the few that are listed here, but an exhaustive description of all the configurations is outside of the scope of this text.

  6. 6.

    In actuality, when looking for a signal for glucose on a QqQ, commonly a signal is found at 73 m/z in negative mode; we will see why in the next section.

  7. 7.

    Validation of a biomolecule refers to the unambiguous confirmation of a molecule’s identity (including an empirical formula) in a specific method, and it is not a trivial task. Fortunately, guidance exists for small molecules [99, 100], proteins [101], and lipids [102]. Additionally, the FDA offers its own guidance for small molecules [103] but has separate guidelines for validation of analytical methods [104].

References

  1. Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP). www.genome.gov/sequencingcostsdata (2023).

  2. Marshall, J. L. et al. The Essentials of Multiomics. Oncologist vol. 27 272–284 Preprint at https://doi.org/10.1093/oncolo/oyab048 (2022).

  3. Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nature Reviews Drug Discovery vol. 15 473–484 Preprint at https://doi.org/10.1038/nrd.2016.32 (2016).

  4. Manzoni, C. et al. Genome, transcriptome and proteome: The rise of omics data and their integration in biomedical sciences. Brief Bioinform 19, 286–302 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. McGuire, A. L. et al. The road ahead in genetics and genomics. Nature Reviews Genetics vol. 21 581–596 Preprint at https://doi.org/10.1038/s41576-020-0272-6 (2020).

  6. Lawrence, J. G. Why genomics is more than genomes. http://genomebiology.com/2004/5/12/357 (2004).

  7. Alan Chodos. April 1946: First Concept of Time-of-Flight Mass Spectrometer. APS News (2001).

    Google Scholar 

  8. van Eck, N. J. & Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538 (2010).

    Article  PubMed  Google Scholar 

  9. Graw, S. et al. Multi-omics data integration considerations and study design for biological systems and disease. Molecular Omics vol. 17 170–185 Preprint at https://doi.org/10.1039/d0mo00041h (2021).

  10. Krassowski, M., Das, V., Sahu, S. K. & Misra, B. B. State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing. Frontiers in Genetics vol. 11 Preprint at https://doi.org/10.3389/fgene.2020.610798 (2020).

  11. Vahabi, N. & Michailidis, G. Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review. Frontiers in Genetics vol. 13 Preprint at https://doi.org/10.3389/fgene.2022.854752 (2022).

  12. Cusick, M. E., Klitgord, N., Vidal, M. & Hill, D. E. Interactome: Gateway into systems biology. Hum Mol Genet 14, (2005).

    Google Scholar 

  13. Doerr, A. Gene factories made of droplets. Nature Methods vol. 15 160–161 Preprint at https://doi.org/10.1038/nmeth.4622 (2018).

  14. Chen, X., Peng, Z. & Yang, Z. Metabolomics studies of cell-cell interactions using single cell mass spectrometry combined with fluorescence microscopy. Chem Sci 13, 6687–6695 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gupta, N., Duggal, S., Kumar, A., Saquib, N. M. & Rao, K. V. S. Concurrent interactome and metabolome analysis reveals role of AKT1 in central carbon metabolism. BMC Res Notes 11, (2018).

    Google Scholar 

  16. Geier, B. et al. Spatial metabolomics of in situ host–microbe interactions at the micrometre scale. Nat Microbiol 5, 498–510 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Gupta, S., Schillaci, M. & Roessner, U. Metabolomics as an emerging tool to study plant-microbe interactions. Emerging Topics in Life Sciences vol. 6 175–183 Preprint at https://doi.org/10.1042/ETLS20210262 (2022).

  18. Maag, D., Erb, M. & Ga Etan Glauser, &. Metabolomics in plant-herbivore interactions: challenges and applications. https://doi.org/10.7892/boris.72209 (2015).

  19. Tolstikov, V., James Moser, A., Sarangarajan, R., Narain, N. R. & Kiebish, M. A. Current status of metabolomic biomarker discovery: Impact of study design and demographic characteristics. Metabolites vol. 10 Preprint at https://doi.org/10.3390/metabo10060224 (2020).

  20. Rinschen, M. M., Ivanisevic, J., Giera, M. & Siuzdak, G. Identification of bioactive metabolites using activity metabolomics. Nature Reviews Molecular Cell Biology vol. 20 353–367 Preprint at https://doi.org/10.1038/s41580-019-0108-4 (2019).

  21. Yang, J. et al. Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review. Cancer Communications vol. 41 1257–1274 Preprint at https://doi.org/10.1002/cac2.12204 (2021).

  22. Castiglione, V. et al. Biomarkers for the diagnosis and management of heart failure. Heart Failure Reviews vol. 27 625–643 Preprint at https://doi.org/10.1007/s10741-021-10105-w (2022).

  23. Dubin, R. F. & Rhee, E. P. Proteomics and metabolomics in kidney disease, including insights into etiology, treatment, and prevention. Clinical Journal of the American Society of Nephrology 15, 404–411 (2020).

    Article  CAS  PubMed  Google Scholar 

  24. Iida, M., Harada, S. & Takebayashi, T. Application of metabolomics to epidemiological studies of atherosclerosis and cardiovascular disease. Journal of Atherosclerosis and Thrombosis vol. 26 747–757 Preprint at https://doi.org/10.5551/jat.RV17036 (2019).

  25. Alonso, C., Noureddin, M., Lu, S. C. & Mato, J. M. Biomarkers and subtypes of deranged lipid metabolism in nonalcoholic fatty liver disease. World Journal of Gastroenterology vol. 25 3009–3020 Preprint at https://doi.org/10.3748/wjg.v25.i24.3009 (2019).

  26. Oskovi Kaplan, Z. A. & Ozgu-Erdinc, A. S. Prediction of Preterm Birth: Maternal Characteristics, Ultrasound Markers, and Biomarkers: An Updated Overview. Journal of Pregnancy vol. 2018 Preprint at https://doi.org/10.1155/2018/8367571 (2018).

  27. Emamzadeh, F. N. & Surguchov, A. Parkinson’s disease: Biomarkers, treatment, and risk factors. Frontiers in Neuroscience vol. 12 Preprint at https://doi.org/10.3389/fnins.2018.00612 (2018).

  28. Badhwar, A. P. et al. A multiomics approach to heterogeneity in Alzheimer’s disease: Focused review and roadmap. Brain vol. 143 1315–1331 Preprint at https://doi.org/10.1093/brain/awz384 (2020).

  29. Fernández-Ochoa, Á. et al. Recent Analytical Approaches for the Study of Bioavailability and Metabolism of Bioactive Phenolic Compounds. Molecules vol. 27 Preprint at https://doi.org/10.3390/molecules27030777 (2022).

  30. Castelli, F. A. et al. Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests. https://doi.org/10.1007/s00216-021-03586-z/Published.

  31. López-Yerena, A. et al. Metabolomics technologies for the identification and quantification of dietary phenolic compound metabolites: An overview. Antioxidants vol. 10 Preprint at https://doi.org/10.3390/antiox10060846 (2021).

  32. Beulens, J. W. J. et al. Environmental risk factors of type 2 diabetes-an exposome approach. https://doi.org/10.1007/s00125-021-05618-w/Published.

  33. Juarez, P. D., Hood, D. B., Song, M. A. & Ramesh, A. Use of an Exposome Approach to Understand the Effects of Exposures From the Natural, Built, and Social Environments on Cardio-Vascular Disease Onset, Progression, and Outcomes. Frontiers in Public Health vol. 8 Preprint at https://doi.org/10.3389/fpubh.2020.00379 (2020).

  34. Sun, J. et al. A review of environmental metabolism disrupting chemicals and effect biomarkers associating disease risks: Where exposomics meets metabolomics. Environment International vol. 158 Preprint at https://doi.org/10.1016/j.envint.2021.106941 (2022).

  35. Adav, S. S. & Wang, Y. Metabolomics signatures of aging: Recent advances. Aging and Disease vol. 12 646–661 Preprint at https://doi.org/10.14336/AD.2020.0909 (2021).

  36. Sharma, R. & Ramanathan, A. The Aging Metabolome—Biomarkers to Hub Metabolites. Proteomics vol. 20 Preprint at https://doi.org/10.1002/pmic.201800407 (2020).

  37. Han, X., Aslanian, A. & Yates, J. R. Mass spectrometry for proteomics. Current Opinion in Chemical Biology vol. 12 483–490 Preprint at https://doi.org/10.1016/j.cbpa.2008.07.024 (2008).

  38. Tsedilin, A. M. et al. How Sensitive and Accurate are Routine NMR and MS Measurements? Mendeleev Communications 25, 454–456 (2015).

    Article  CAS  Google Scholar 

  39. Wang, Y., Sun, J., Qiao, J., Ouyang, J. & Na, N. A ‘soft’ and ‘hard’ Ionization Method for Comprehensive Studies of Molecules. Anal Chem 90, 14095–14099 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Alan Dronsfield. Mass spectrometry – the early days. Royal Soceity of Chemistry 1 (2010).

    Google Scholar 

  41. Wong, P. S. H. & Cooks, R. G. Ion Trap Mass Spectrometry.

    Google Scholar 

  42. Dilling, J., Blaum, K., Brodeur, M. & Eliseev, S. Penning-Trap Mass Measurements in Atomic and Nuclear Physics. https://doi.org/10.1146/annurev-nucl-102711 (2018).

  43. Eliseev, S. & Novikov, Y. High-precision Penning-trap mass spectrometry for neutrino physics. European Physical Journal A vol. 59 Preprint at https://doi.org/10.1140/epja/s10050-023-00946-4 (2023).

  44. Schwaiger-Haber, M. et al. A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer. ACS Measurement Science Au 1, 35–45 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Fan, T. W. M. & Lane, A. N. Applications of NMR spectroscopy to systems biochemistry. Progress in Nuclear Magnetic Resonance Spectroscopy vols 92–93 18–53 Preprint at https://doi.org/10.1016/j.pnmrs.2016.01.005 (2016).

  46. Edison, A. S. et al. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Analytical Chemistry vol. 93 478–499 Preprint at https://doi.org/10.1021/acs.analchem.0c04414 (2021).

  47. Wishart, D. S. et al. NMR and Metabolomics—A Roadmap for the Future. Metabolites vol. 12 Preprint at https://doi.org/10.3390/metabo12080678 (2022).

  48. Nagana Gowda, G. A. & Raftery, D. Can NMR solve some significant challenges in metabolomics? Journal of Magnetic Resonance 260, 144–160 (2015).

    Article  CAS  PubMed  Google Scholar 

  49. Emwas, A. H. et al. Nmr spectroscopy for metabolomics research. Metabolites vol. 9 Preprint at https://doi.org/10.3390/metabo9070123 (2019).

  50. Ribay, V., Praud, C., Letertre, M. P. M., Dumez, J. N. & Giraudeau, P. Hyperpolarized NMR metabolomics. Current Opinion in Chemical Biology vol. 74 Preprint at https://doi.org/10.1016/j.cbpa.2023.102307 (2023).

  51. Bhinderwala, F., Wase, N., Dirusso, C. & Powers, R. Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation. J Proteome Res 17, 4017–4022 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Martens, J. et al. Molecular identification in metabolomics using infrared ion spectroscopy. Sci Rep 7, (2017).

    Google Scholar 

  53. Wang, M., Da, Y. & Tian, Y. Fluorescent proteins and genetically encoded biosensors. Chemical Society Reviews vol. 52 1189–1214 Preprint at https://doi.org/10.1039/d2cs00419d (2023).

  54. Rodriguez, E. L. et al. Affinity chromatography: A review of trends and developments over the past 50 years. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences vol. 1157 Preprint at https://doi.org/10.1016/j.jchromb.2020.122332 (2020).

  55. Perez de Souza, L., Alseekh, S., Scossa, F. & Fernie, A. R. Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research. Nature Methods vol. 18 733–746 Preprint at https://doi.org/10.1038/s41592-021-01116-4 (2021).

  56. Pezzatti, J. et al. Implementation of liquid chromatography–high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial. Analytica Chimica Acta vol. 1105 28–44 Preprint at https://doi.org/10.1016/j.aca.2019.12.062 (2020).

  57. Patti, G. J. Separation strategies for untargeted metabolomics. Journal of Separation Science vol. 34 3460–3469 Preprint at https://doi.org/10.1002/jssc.201100532 (2011).

  58. Alpert, A. J. Hydrophilic-interaction chromatography for the separation of peptides, nucleic acids and other polar compounds. Journal of Chromatography vol. 499 (1990).

    Google Scholar 

  59. Hosseinkhani, F. et al. Systematic Evaluation of HILIC Stationary Phases for Global Metabolomics of Human Plasma. Metabolites 12, (2022).

    Google Scholar 

  60. Svec, F. Preparation and HPLC applications of rigid macroporous organic polymer monoliths. Journal of Separation Science vol. 27 747–766 Preprint at https://doi.org/10.1002/jssc.200401721 (2004).

  61. Walsby-Tickle, J. et al. Anion-exchange chromatography mass spectrometry provides extensive coverage of primary metabolic pathways revealing altered metabolism in IDH1 mutant cells. Commun Biol 3, (2020).

    Google Scholar 

  62. Fiehn, O. Metabolomics by gas chromatography-mass spectrometry: Combined targeted and untargeted profiling. Curr Protoc Mol Biol 2016, (2016).

    Google Scholar 

  63. Neusüß, C. & Jooß, K. Capillary Electrophoresis-Mass Spectrometry Methods and Protocols Methods in Molecular Biology 2531. http://www.springer.com/series/7651.

  64. Paglia, G., Smith, A. J. & Astarita, G. Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics. Mass Spectrometry Reviews vol. 41 722–765 Preprint at https://doi.org/10.1002/mas.21686 (2022).

  65. Lu, W. et al. Metabolite measurement: Pitfalls to avoid and practices to follow. Annual Review of Biochemistry vol. 86 277–304 Preprint at https://doi.org/10.1146/annurev-biochem-061516-044952 (2017).

  66. Gil, A. et al. Stability of energy metabolites-An often overlooked issue in metabolomics studies: A review. Electrophoresis vol. 36 2156–2169 Preprint at https://doi.org/10.1002/elps.201500031 (2015).

  67. Martias, C. et al. Optimization of sample preparation for metabolomics exploration of urine, feces, blood and saliva in humans using combined nmr and uhplc-hrms platforms. Molecules 26, (2021).

    Google Scholar 

  68. Vuckovic, D. Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry. Analytical and Bioanalytical Chemistry vol. 403 1523–1548 Preprint at https://doi.org/10.1007/s00216-012-6039-y (2012).

  69. Conesa, A. & Beck, S. Making multi-omics data accessible to researchers. Scientific Data vol. 6 Preprint at https://doi.org/10.1038/s41597-019-0258-4 (2019).

  70. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic Gene Expression in a Single Cell. Science (1979) 297, 1183–1186 (2002).

    Google Scholar 

  71. Eling, N., Morgan, M. D. & Marioni, J. C. Challenges in measuring and understanding biological noise. Nature Reviews Genetics vol. 20 536–548 Preprint at https://doi.org/10.1038/s41576-019-0130-6 (2019).

  72. National Academies of Sciences, E., National Academies of Sciences, E., National Academies of Sciences, E., National Academies of Sciences, E. & Committee on Science, E. Reproducibility and replicability in science.

    Google Scholar 

  73. Harvard Data Science Review • Issue 2.4, Fall 2020 Reproducibility and Replicability in Science: Report Highlightsnas-report-highlights License: Creative Commons Attribution 4.0 International License (CC-BY 4.0). (2020).

    Google Scholar 

  74. Munafò, M. R. et al. A manifesto for reproducible science. Nature Human Behaviour vol. 1 Preprint at https://doi.org/10.1038/s41562-016-0021 (2017).

  75. Fanelli, D. Is science really facing a reproducibility crisis, and do we need it to? doi:10.1073/pnas.1708272114/-/DC Supplemental.

    Google Scholar 

  76. Kolker, E. et al. Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications. OMICS A Journal of Integrative Biology vol. 18 10–14 Preprint at https://doi.org/10.1089/omi.2013.0149 (2014).

  77. Perng, W. & Aslibekyan, S. Find the needle in the haystack, then find it again: Replication and validation in the ‘omics era. Metabolites vol. 10 1–13 Preprint at https://doi.org/10.3390/metabo10070286 (2020).

  78. Liu, C. et al. High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI). BMC Syst Biol 10, (2016).

    Google Scholar 

  79. Altman, N. & Krzywinski, M. The curse(s) of dimensionality this-month. Nature Methods vol. 15 399–400 Preprint at https://doi.org/10.1038/s41592-018-0019-x (2018).

  80. Chattopadhyay, A. & Lu, T.-P. Gene-gene interaction: the curse of dimensionality. Ann Transl Med 7, 813–813 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Riley, R. D. et al. Minimum sample size for developing a multivariable prediction model: PART II – binary and time-to-event outcomes. Stat Med 38, 1276–1296 (2019).

    Article  PubMed  Google Scholar 

  82. Krassowski, M., Das, V., Sahu, S. K. & Misra, B. B. State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing. Frontiers in Genetics vol. 11 Preprint at https://doi.org/10.3389/fgene.2020.610798 (2020).

  83. Yamada, R., Okada, D., Wang, J., Basak, T. & Koyama, S. Interpretation of omics data analyses. Journal of Human Genetics vol. 66 93–102 Preprint at https://doi.org/10.1038/s10038-020-0763-5 (2021).

  84. Ponomarenko, E. A. et al. The Size of the Human Proteome: The Width and Depth. International Journal of Analytical Chemistry vol. 2016 Preprint at https://doi.org/10.1155/2016/7436849 (2016).

  85. Bennett, B. D. et al. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat Chem Biol 5, 593–599 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Zenobi, R. Single-cell metabolomics: Analytical and biological perspectives. Science vol. 342 Preprint at https://doi.org/10.1126/science.1243259 (2013).

  87. Ong, S. E. & Mann, M. Mass Spectrometry–Based Proteomics Turns Quantitative. Nat Chem Biol 1, 252–262 (2005).

    Article  CAS  PubMed  Google Scholar 

  88. Mahieu, N. G. & Patti, G. J. Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites. Anal Chem 89, 10397–10406 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Busch, M., Ahlberg, E., Ahlberg, E. & Laasonen, K. How to Predict the pKa of Any Compound in Any Solvent. ACS Omega (2022) https://doi.org/10.1021/acsomega.2c01393.

  90. Lisec, J., Hoffmann, F., Schmitt, C. & Jaeger, C. Extending the Dynamic Range in Metabolomics Experiments by Automatic Correction of Peaks Exceeding the Detection Limit. Anal Chem 88, 7487–7492 (2016).

    Article  CAS  PubMed  Google Scholar 

  91. Yuan, J., Fowler, W. U., Kimball, E., Lu, W. & Rabinowitz, J. D. Kinetic flux profiling of nitrogen assimilation in Escherichia coli. Nat Chem Biol 2, 529–530 (2006).

    Article  CAS  PubMed  Google Scholar 

  92. Rowan, D. D. Volatile metabolites. Metabolites vol. 1 41–63 Preprint at https://doi.org/10.3390/metabo1010041 (2011).

  93. Mahieu, N. G., Huang, X., Chen, Y. J. & Patti, G. J. Credentialing features: A platform to benchmark and optimize untargeted metabolomic methods. Anal Chem 86, 9583–9589 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Matuszewski, B. K., Constanzer, M. L. & Chavez-Eng, C. M. Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS. Anal Chem 75, 3019–3030 (2003).

    Article  CAS  PubMed  Google Scholar 

  95. Rosman’ And, K. J. R. & Taylor, P. D. P. INTERNATIONAL UNION OF PURE AND APPLIED CHEMISTRY INORGANIC CHEMISTRY DIVISION COMMISSION ON ATOMIC WEIGHTS AND ISOTOPIC ABUNDANCES* SUBCOMMITTEE FOR ISOTOPIC ABUNDANCE MEASUREMENTS** ISOTOPIC COMPOSITIONS OF THE ELEMENTS 1997 (Technical Report) Prepared for publication by Isotopic compositions of the elements 1997 (Technical Report). Pure & Appl. Chern vol. 70 (1998).

    Google Scholar 

  96. Goldfarb, D., Lafferty, M. J., Herring, L. E., Wang, W. & Major, M. B. Approximating isotope distributions of biomolecule fragments.

    Google Scholar 

  97. Comai, L., Katz, J. E. & Mallick, P. Proteomics Methods and Protocols Methods in Molecular Biology 1550. http://www.springer.com/series/7651.

  98. Zubarev, R. A. & Makarov, A. Orbitrap mass spectrometry. Anal Chem 85, 5288–5296 (2013).

    Article  CAS  PubMed  Google Scholar 

  99. Sreekumar, J., Hogan, T. J., Taylor, S., Turner, P. & Knott, C. A quadrupole mass spectrometer for resolution of low mass isotopes. J Am Soc Mass Spectrom 21, 1364–1370 (2010).

    Article  CAS  PubMed  Google Scholar 

  100. Schymanski, E. L. et al. Identifying small molecules via high resolution mass spectrometry: Communicating confidence. Environmental Science and Technology vol. 48 2097–2098 Preprint at https://doi.org/10.1021/es5002105 (2014).

  101. Nakayasu, E. S. et al. Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation. Nature Protocols vol. 16 3737–3760 Preprint at https://doi.org/10.1038/s41596-021-00566-6 (2021).

  102. Köfeler, H. C. et al. Recommendations for good practice in ms-based lipidomics. Journal of Lipid Research vol. 62 Preprint at https://doi.org/10.1016/j.jlr.2021.100138 (2021).

  103. Hfv-, C. Guidance for Industry #118 – Mass Spectrometry for Confirmation of the Identity of Animal Drug Residues – Final Guidance, May 1, 2003. http://www.fda.gov/AnimalVeterinary/GuidanceComplianceEnforcement/GuidanceforIndustry/default.htm (2003).

  104. FDA & CDER. Bioanalytical Method Validation Guidance for Industry Biopharmaceutics Bioanalytical Method Validation Guidance for Industry Biopharmaceutics Contains Nonbinding Recommendations. http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm and/or http://www.fda.gov/AnimalVeterinary/GuidanceComplianceEnforcement/GuidanceforIndustry/default.htm (2018).

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Hartman, T.E., Lees, H.J. (2023). Introduction of Metabolomics: An Overview. In: Soni, V., Hartman, T.E. (eds) Metabolomics. Springer, Cham. https://doi.org/10.1007/978-3-031-39094-4_1

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