Abstract
Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.
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References
Amara CS, Vantaku V, Lotan Y, Putluri N (2019) Recent advances in the metabolomic study of bladder cancer. Expert Rev Proteomics 16(4):315–324. https://doi.org/10.1080/14789450.2019.1583105
Gardner A, Parkes HG, Carpenter GH, So PW (2018) Developing and standardizing a protocol for quantitative proton nuclear magnetic resonance ((1)H NMR) spectroscopy of saliva. J Proteome Res 17(4):1521–1531. https://doi.org/10.1021/acs.jproteome.7b00847
Lubes G, Goodarzi M (2018) GC–MS based metabolomics used for the identification of cancer volatile organic compounds as biomarkers. J Pharm Biomed Anal 147:313–322
Zhang W, Ramautar R (2021) CE-MS for metabolomics: developments and applications in the period 2018–2020. Electrophoresis 42(4):381–401. https://doi.org/10.1002/elps.202000203
Roca M, Alcoriza MI, Garcia-Cañaveras JC, Lahoz A (2021) Reviewing the metabolome coverage provided by LC-MS: focus on sample preparation and chromatography-a tutorial. Anal Chim Acta 1147:38–55. https://doi.org/10.1016/j.aca.2020.12.025
Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M (2012) Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Curr Bioinforma 7(1):96–108. https://doi.org/10.2174/157489312799304431
Baima G, Iaderosa G, Citterio F, Grossi S, Romano F, Berta GN, Buduneli N, Aimetti M (2021) Salivary metabolomics for the diagnosis of periodontal diseases: a systematic review with methodological quality assessment. Metab Off J Metab Soc 17(1):1. https://doi.org/10.1007/s11306-020-01754-3
Ishikawa S, Sugimoto M, Kitabatake K, Tu M, Sugano A, Yamamori I, Iba A, Yusa K, Kaneko M, Ota S, Hiwatari K, Enomoto A, Masaru T, Iino M (2017) Effect of timing of collection of salivary metabolomic biomarkers on oral cancer detection. Amino Acids 49(4):761–770. https://doi.org/10.1007/s00726-017-2378-5
Nakajima T, Katsumata K, Kuwabara H, Soya R, Enomoto M, Ishizaki T, Tsuchida A, Mori M, Hiwatari K, Soga T, Tomita M, Sugimoto M (2018) Urinary polyamine biomarker panels with machine-learning differentiated colorectal cancers, benign disease, and healthy controls. Int J Mol Sci 19(3). https://doi.org/10.3390/ijms19030756
Hirayama A, Sugimoto M, Suzuki A, Hatakeyama Y, Enomoto A, Harada S, Soga T, Tomita M, Takebayashi T (2015) Effects of processing and storage conditions on charged metabolomic profiles in blood. Electrophoresis 36(18):2148–2155. https://doi.org/10.1002/elps.201400600
Sugimoto M (2020) Salivary metabolomics for cancer detection. Expert Rev Proteomics 17(9):639–648. https://doi.org/10.1080/14789450.2020.1846524
Liebal UW, Phan ANT, Sudhakar M, Raman K, Blank LM (2020) Machine learning applications for mass spectrometry-based metabolomics. Meta 10(6). https://doi.org/10.3390/metabo10060243
Patti GJ, Yanes O, Siuzdak G (2012) Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13(4):263–269. https://doi.org/10.1038/nrm3314
Dunn WB, Wilson ID, Nicholls AW, Broadhurst D (2012) The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis 4(18):2249–2264
Shimizu H, Usui Y, Asakage M, Nezu N, Wakita R, Tsubota K, Sugimoto M, Goto H (2020) Serum metabolomic profiling of patients with non-infectious uveitis. J Clin Med 9(12). https://doi.org/10.3390/jcm9123955
Nam SL, Mata AP, Dias RP, Harynuk JJ (2020) Towards standardization of data normalization strategies to improve urinary metabolomics studies by GC×GC-TOFMS. Meta 10(9). https://doi.org/10.3390/metabo10090376
Misra BB (2021) New software tools, databases, and resources in metabolomics: updates from 2020. Metab Off J Metab Soc 17(5):49. https://doi.org/10.1007/s11306-021-01796-1
Ren S, Hinzman AA, Kang EL, Szczesniak RD, Lu LJ (2015) Computational and statistical analysis of metabolomics data. Metab Off J Metab Soc 11(6):1492–1513
Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, Gauthier C, Jacques P, Li S, Xia J (2021) MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. https://doi.org/10.1093/nar/gkab382
Saigusa D, Okamura Y, Motoike IN, Katoh Y, Kurosawa Y, Saijyo R, Koshiba S, Yasuda J, Motohashi H, Sugawara J, Tanabe O, Kinoshita K, Yamamoto M (2016) Establishment of protocols for global metabolomics by LC-MS for biomarker discovery. PLoS One 11(8):e0160555. https://doi.org/10.1371/journal.pone.0160555
Saito R, Sugimoto M, Hirayama A, Soga T, Tomita M, Takebayashi T (2021) Quality assessment of untargeted analytical data in a large-scale Metabolomic study. J Clin Med 10(9). https://doi.org/10.3390/jcm10091826
Yamamoto H, Suzuki M, Matsuta R, Sasaki K, Kang M-I, Kami K, Tatara Y, Itoh K, Nakaji S (2021) Capillary electrophoresis mass spectrometry-based metabolomics of plasma samples from healthy subjects in a cross-sectional Japanese population study. Meta 11(5):314
Acknowledgments
This research was funded by grants from JSPS KAKENHI (grant number 20B205) and JST OPERA (grant number JPMJOP1842).
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Sugimoto, M., Aizawa, Y., Tomita, A. (2023). Data Processing and Analysis in Liquid Chromatography–Mass Spectrometry-Based Targeted Metabolomics. In: González-Domínguez, R. (eds) Mass Spectrometry for Metabolomics. Methods in Molecular Biology, vol 2571. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2699-3_21
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DOI: https://doi.org/10.1007/978-1-0716-2699-3_21
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