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Metabolomics: Going Deeper, Going Broader, Going Further

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Cell-Wide Identification of Metabolite-Protein Interactions

Abstract

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

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References

  1. Jang C, Chen L, Rabinowitz JD (2018) Metabolomics and isotope tracing. Cell 173:822–837. https://doi.org/10.1016/j.cell.2018.03.055

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Link H, Kochanowski K, Sauer U (2013) Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo. Nat Biotechnol. https://doi.org/10.1038/nbt.2489

  3. Perez de Souza L, Alseekh S, Scossa F, Fernie AR (2021) Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research. Nat Methods 18. https://doi.org/10.1038/s41592-021-01116-4

  4. Lu W, Su X, Klein MS et al (2017) Metabolite measurement: pitfalls to avoid and practices to follow | Annual Review of Biochemistry. Annu Rev Biochem 86. https://doi.org/10.1146/annurev-biochem-061516-044952

  5. Kulkarni A, Anderson AG, Merullo DP, Konopka G (2019) Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr Opin Biotechnol 58:129–136. https://doi.org/10.1016/j.copbio.2019.03.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Vignoli A, Ghini V, Meoni G et al (2019) High-throughput metabolomics by 1D NMR. Angew Chemie - Int Ed 58:968–994. https://doi.org/10.1002/anie.201804736

    Article  CAS  Google Scholar 

  7. Hyötyläinen T (2021) Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics. J Sep Sci 44:1769–1787. https://doi.org/10.1002/jssc.202001263

    Article  CAS  PubMed  Google Scholar 

  8. D’Ari R, Casadesus J (1998) Underground metabolism. Bioessays 20:181–186

    Article  Google Scholar 

  9. Álvarez-Sánchez B, Priego-Capote F, Luque de Castro MD (2010) Metabolomics analysis I. Selection of biological samples and practical aspects preceding sample preparation. TrAC - Trends Anal Chem 29:111–119. https://doi.org/10.1016/j.trac.2009.12.003

    Article  CAS  Google Scholar 

  10. Vuckovic D (2012) Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry. Anal Bioanal Chem 403:1523–1548. https://doi.org/10.1007/s00216-012-6039-y

    Article  CAS  PubMed  Google Scholar 

  11. Kim HK, Verpoorte R (2010) Sample preparation for plant metabolomics. Phytochem Anal 21:4–13. https://doi.org/10.1002/pca.1188

    Article  CAS  PubMed  Google Scholar 

  12. Chen Y, Guo J, Xing S et al (2021) Global-scale metabolomic profiling of human hair for simultaneous monitoring of endogenous metabolome, short- and long-term exposome. Front Chem 9:1–11. https://doi.org/10.3389/fchem.2021.674265

    Article  CAS  Google Scholar 

  13. Luque de Castro MD, Delgado-Povedano MM (2014) Ultrasound: a subexploited tool for sample preparation in metabolomics. Anal Chim Acta 806:74–84

    Article  CAS  Google Scholar 

  14. Buescher JM, Czernik D, Ewald JC et al (2009) Cross-platform comparison of methods for quantitative metabolomics of primary metabolism. Anal Chem 81:2135–2143

    Article  Google Scholar 

  15. Jin YY, Shi ZQ, Chang WQ et al (2018) A chemical derivatization based UHPLC-LTQ-Orbitrap mass spectrometry method for accurate quantification of short-chain fatty acids in bronchoalveolar lavage fluid of asthma mice. J Pharm Biomed Anal 161:336–343. https://doi.org/10.1016/j.jpba.2018.08.057

    Article  CAS  PubMed  Google Scholar 

  16. Wang S, Zhou L, Wang Z et al (2017) Simultaneous metabolomics and lipidomics analysis based on novel heart-cutting two-dimensional liquid chromatography-mass spectrometry. Anal Chim Acta 966:34–40. https://doi.org/10.1016/j.aca.2017.03.004

    Article  CAS  PubMed  Google Scholar 

  17. Weinert CH, Egert B, Kulling SE (2015) On the applicability of comprehensive two-dimensional gas chromatography combined with a fast-scanning quadrupole mass spectrometer for untargeted large-scale metabolomics. J Chromatogr A 1405:156–167. https://doi.org/10.1016/j.chroma.2015.04.011

    Article  CAS  PubMed  Google Scholar 

  18. van de Velde B, Guillarme D, Kohler I (2020) Supercritical fluid chromatography – Mass spectrometry in metabolomics: past, present, and future perspectives. J Chromatogr B Analyt Technol Biomed Life Sci 1161:122444. https://doi.org/10.1016/j.jchromb.2020.122444

    Article  CAS  PubMed  Google Scholar 

  19. Zhang W, Ramautar R (2021) CE-MS for metabolomics: developments and applications in the period 2018–2020. Electrophoresis 42:381–401. https://doi.org/10.1002/elps.202000203

    Article  CAS  PubMed  Google Scholar 

  20. Buescher JM, Moco S, Sauer U, Zamboni N (2010) Ultrahigh performance liquid chromatography-tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites. Anal Chem 82:4403–4412. https://doi.org/10.1021/ac100101d

    Article  CAS  PubMed  Google Scholar 

  21. McCloskey D, Gangoiti JA, Palsson BO, Feist AM (2015) A pH and solvent optimized reverse-phase ion-paring-LC–MS/MS method that leverages multiple scan-types for targeted absolute quantification of intracellular metabolites. Metabolomics 11:1338–1350. https://doi.org/10.1007/s11306-015-0790-y

    Article  CAS  Google Scholar 

  22. Tang D-Q, Zou L, Yin X-X, Ong CN (2016) HILIC-MS for metabolomics: an attractive and complementary approach to RPLC-MS. Mass Spectrom Rev 35:574–600. https://doi.org/10.1002/mas.21445

    Article  CAS  PubMed  Google Scholar 

  23. Lv W, Guo L, Zheng F et al (2020) Alternate reversed-phase and hydrophilic interaction liquid chromatography coupled with mass spectrometry for broad coverage in metabolomics analysis. J Chromatogr B Analyt Technol Biomed Life Sci 1152:122266. https://doi.org/10.1016/j.jchromb.2020.122266

    Article  CAS  PubMed  Google Scholar 

  24. Montenegro-Burke JR, Kok BP, Guijas C et al (2021) Metabolomics activity screening of T cell–induced colitis reveals anti-inflammatory metabolites. Sci Signal 14. https://doi.org/10.1126/scisignal.abf6584

  25. Sonawane D, Kumar Sahu A, Jadav T et al (2021) Innovation in strategies for sensitivity improvement of chromatography and mass spectrometry based analytical techniques. Crit Rev Anal Chem. https://doi.org/10.1080/10408347.2021.1969887

  26. Kostiainen R, Kauppila TJ (2009) Effect of eluent on the ionization process in liquid chromatography-mass spectrometry. J Chromatogr A 1216:685–699. https://doi.org/10.1016/j.chroma.2008.08.095

    Article  CAS  PubMed  Google Scholar 

  27. Sarvin B, Lagziel S, Sarvin N et al (2020) Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions. Nat Commun 11. https://doi.org/10.1038/s41467-020-17,026-6

  28. Mahieu NG, Patti GJ (2017) Systems-level annotation of a metabolomics data set reduces 25,000 features to fewer than 1000 unique metabolites. Anal Chem. https://doi.org/10.1021/acs.analchem.7b02380

  29. Leaptrot KL, May JC, Dodds JN, McLean JA (2019) Ion mobility conformational lipid atlas for high confidence lipidomics. Nat Commun 10:985. https://doi.org/10.1038/s41467-019-08897-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Schroeder M, Meyer SW, Heyman HM et al (2020) Generation of a collision cross section library for multi-dimensional plant metabolomics using UHPLC-trapped ion mobility-MS/MS. Metabolites 10. https://doi.org/10.3390/metabo10010013

  31. Zhou Z, Luo M, Chen X et al (2020) Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics. Nat Commun 11:4334. https://doi.org/10.1038/s41467-020-18,171-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jorge TF, António C (2018) Quantification of low-abundant phosphorylated carbohydrates using HILIC-QqQ-MS/MS. pp. 71–86

    Google Scholar 

  33. Zhang R, Watson DG, Wang L et al (2014) Evaluation of mobile phase characteristics on three zwitterionic columns in hydrophilic interaction liquid chromatography mode for liquid chromatography-high resolution mass spectrometry based untargeted metabolite profiling of Leishmania parasites. J Chromatogr A 1362:168–179. https://doi.org/10.1016/j.chroma.2014.08.039

    Article  CAS  PubMed  Google Scholar 

  34. Xu S, Yang C, Yan X, Liu H (2021) Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques. Anal Bioanal Chem. https://doi.org/10.1007/s00216-021-03624-w

  35. Chetwynd AJ, David A (2018) A review of nanoscale LC-ESI for metabolomics and its potential to enhance the metabolome coverage. Talanta 182:380–390. https://doi.org/10.1016/j.talanta.2018.01.084

    Article  CAS  PubMed  Google Scholar 

  36. Chetwynd AJ, David A, Hill EM, Abdul-Sada A (2014) Evaluation of analytical performance and reliability of direct nanoLC-nanoESI-high resolution mass spectrometry for profiling the (xeno)metabolome. J Mass Spectrom 49:1063–1069. https://doi.org/10.1002/jms.3426

    Article  CAS  PubMed  Google Scholar 

  37. Kiefer P, Delmotte N, Vorholt JA et al (2010) Nanoscale ion-pair reversed-phase HPLC-MS for sensitive metabolome analysis. Anal Chem. https://doi.org/10.1021/ac102445r

  38. Liu FL, Ye TT, Ding JH et al (2021) Chemical tagging assisted mass spectrometry analysis enables sensitive determination of phosphorylated compounds in a single cell. Anal Chem 93:6848–6856. https://doi.org/10.1021/acs.analchem.1c00915

    Article  CAS  PubMed  Google Scholar 

  39. Ryan K, Rose RE, Jones DR, Lopez PA (2021) Sheath fluid impacts the depletion of cellular metabolites in cells afflicted by sorting induced cellular stress (SICS). Cytometry A cyto.a.24361. https://doi.org/10.1002/cyto.a.24361

  40. Llufrio EM, Wang L, Naser FJ, Patti GJ (2018) Sorting cells alters their redox state and cellular metabolome. Redox Biol 16:381–387. https://doi.org/10.1016/j.redox.2018.03.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Van Vranken JG, Rutter J (2016) The whole (cell) is less than the sum of its parts. Cell 166:1078–1079

    Article  Google Scholar 

  42. Abu-Remaileh M, Wyant GA, Kim C et al (2017) Lysosomal metabolomics reveals V-ATPase- and mTOR-dependent regulation of amino acid efflux from lysosomes. Science (80-) 358:807–813. https://doi.org/10.1126/science.aan6298

    Article  CAS  Google Scholar 

  43. Chantranupong L, Saulnier JL, Wang W et al (2020) Rapid purification and metabolomic profiling of synaptic vesicles from mammalian brain. Elife 9. https://doi.org/10.7554/eLife.59699

  44. Chen WW, Freinkman E, Wang T et al (2016) Absolute quantification of matrix metabolites reveals the dynamics of mitochondrial metabolism. Cell 166:1324–1337.e11. https://doi.org/10.1016/j.cell.2016.07.040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Xiong J, He J, Xie WP et al (2019) Rapid affinity purification of intracellular organelles using a twin strep tag. J Cell Sci 132. https://doi.org/10.1242/jcs.235390

  46. Lee WD, Mukha D, Aizenshtein E, Shlomi T (2019) Spatial-fluxomics provides a subcellular-compartmentalized view of reductive glutamine metabolism in cancer cells. Nat Commun 10. https://doi.org/10.1038/s41467-019-09352-1

  47. Christen S, Lorendeau D, Schmieder R et al (2016) Breast cancer-derived lung metastases show increased pyruvate carboxylase-dependent anaplerosis. Cell Rep 17:837–848. https://doi.org/10.1016/j.celrep.2016.09.042

    Article  CAS  PubMed  Google Scholar 

  48. Taylor MJ, Lukowski JK, Anderton CR (2021) Spatially resolved mass spectrometry at the single cell: recent innovations in proteomics and metabolomics. J Am Soc Mass Spectrom 32:872–894. https://doi.org/10.1021/jasms.0c00439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sun C, Li T, Song X et al (2019) Spatially resolved metabolomics to discover tumor-associated metabolic alterations. Proc Natl Acad Sci U S A 116:52–57. https://doi.org/10.1073/pnas.1808950116

    Article  CAS  PubMed  Google Scholar 

  50. Zang Q, Sun C, Chu X et al (2021) Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures. Anal Chim Acta 1155:338342. https://doi.org/10.1016/j.aca.2021.338342

    Article  CAS  PubMed  Google Scholar 

  51. Korte AR, Yandeau-Nelson MD, Nikolau BJ, Lee YJ (2015) Subcellular-level resolution MALDI-MS imaging of maize leaf metabolites by MALDI-linear ion trap-Orbitrap mass spectrometer. Anal Bioanal Chem 407:2301–2309. https://doi.org/10.1007/s00216-015-8460-5

    Article  CAS  PubMed  Google Scholar 

  52. Schoffelen NJ, Mohr W, Ferdelman TG et al (2018) Single-cell imaging of phosphorus uptake shows that key harmful algae rely on different phosphorus sources for growth. Sci Rep 8:17182. https://doi.org/10.1038/s41598-018-35,310-w

    Article  PubMed  PubMed Central  Google Scholar 

  53. Chen WW, Lemieux GA, Camp CH et al (2020) Spectroscopic coherent Raman imaging of Caenorhabditis elegans reveals lipid particle diversity. Nat Chem Biol 16:1087–1095. https://doi.org/10.1038/s41589-020-0565-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ahl PJ, Hopkins RA, Xiang WW et al (2020) Met-Flow, a strategy for single-cell metabolic analysis highlights dynamic changes in immune subpopulations. Commun Biol 3:1–15. https://doi.org/10.1038/s42003-020-1027-9

    Article  CAS  Google Scholar 

  55. Hanson GT, Aggeler R, Oglesbee D et al (2004) Investigating mitochondrial redox potential with redox-sensitive green fluorescent protein indicators. J Biol Chem 279:13044–13053. https://doi.org/10.1074/jbc.M312846200

    Article  CAS  PubMed  Google Scholar 

  56. Berg J, Hung YP, Yellen G (2009) A genetically encoded fluorescent reporter of ATP:ADP ratio. Nat Methods 6:161–166. https://doi.org/10.1038/nmeth.1288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Hung YP, Albeck JG, Tantama M, Yellen G (2011) Imaging cytosolic NADH-NAD(+) redox state with a genetically encoded fluorescent biosensor. Cell Metab 14:545–554. https://doi.org/10.1016/j.cmet.2011.08.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ewald JC, Reich S, Baumann S et al (2011) Engineering genetically encoded nanosensors for real-time in vivo measurements of citrate concentrations. PLoS One 6:e28245

    Article  CAS  Google Scholar 

  59. Peroza EA, Boumezbeur A-H, Zamboni N (2015) Rapid, randomized development of genetically encoded FRET sensors for small molecules. Analyst 140:4540–4548. https://doi.org/10.1039/C5AN00707K

    Article  CAS  PubMed  Google Scholar 

  60. Smith DF, Podgorski DC, Rodgers RP et al (2018) 21 Tesla FT-ICR mass spectrometer for ultrahigh-resolution analysis of complex organic mixtures. Anal Chem 90:2041–2047. https://doi.org/10.1021/acs.analchem.7b04159

    Article  CAS  PubMed  Google Scholar 

  61. Kind T, Fiehn O (2007) Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8:105. https://doi.org/10.1186/1471-2105-8-105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kim S, Chen J, Cheng T et al (2021) PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Res 49:D1388–D1395. https://doi.org/10.1093/nar/gkaa971

    Article  CAS  PubMed  Google Scholar 

  63. Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87:1123–1124. https://doi.org/10.1021/ed100697w

    Article  CAS  Google Scholar 

  64. Gabrielson SW (2018) SciFinder. J Med Libr Assoc 106:1481. https://doi.org/10.5195/JMLA.2018.515

    Article  Google Scholar 

  65. Hastings J, De Matos P, Dekker A et al (2013) The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 41:456–463. https://doi.org/10.1093/nar/gks1146

    Article  CAS  Google Scholar 

  66. Kanehisa M, Goto S, Sato Y et al (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42:D199–D205. https://doi.org/10.1093/nar/gkt1076

    Article  CAS  PubMed  Google Scholar 

  67. Chong J, Soufan O, Li C et al (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46:W486–W494. https://doi.org/10.1093/nar/gky310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Afendi FM, Okada T, Yamazaki M et al (2012) KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research. Plant Cell Physiol 53:1–12. https://doi.org/10.1093/pcp/pcr165

    Article  CAS  Google Scholar 

  69. Van Der Hooft JJJ, Vervoort J, Bino RJ et al (2011) Polyphenol identification based on systematic and robust high-resolution accurate mass spectrometry fragmentation. Anal Chem 83:409–416. https://doi.org/10.1021/ac102546x

    Article  CAS  PubMed  Google Scholar 

  70. Bonner R, Hopfgartner G (2019) SWATH data independent acquisition mass spectrometry for metabolomics. TrAC - Trends Anal Chem 120. https://doi.org/10.1016/j.trac.2018.10.014

  71. Cho K, Schwaiger-Haber M, Naser FJ et al (2021) Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics. Anal Chim Acta 1149:338210. https://doi.org/10.1016/j.aca.2021.338210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Meier F, Beck S, Grassl N et al (2015) Parallel Accumulation–Serial Fragmentation (PASEF): multiplying sequencing speed and sensitivity by synchronized scans in a trapped ion mobility device. J Proteome Res 14:5378–5387. https://doi.org/10.1021/acs.jproteome.5b00932

    Article  CAS  PubMed  Google Scholar 

  73. Horai H, Arita M, Kanaya S et al (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45:703–714

    Article  CAS  Google Scholar 

  74. Guijas C, Montenegro-Burke JR, Domingo-Almenara X et al (2018) METLIN: a technology platform for identifying knowns and unknowns. Anal Chem 90:3156–3164. https://doi.org/10.1021/acs.analchem.7b04424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Wang M, Carver JJ, Phelan VV et al (2016) Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 34:828–837. https://doi.org/10.1038/nbt.3597

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Blaženović I, Kind T, Ji J, Fiehn O (2018) Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 8:31. https://doi.org/10.3390/metabo8020031

    Article  CAS  PubMed Central  Google Scholar 

  77. Borges RM, Colby SM, Das S et al (2021) Quantum chemistry calculations for metabolomics. Chem Rev 121:5633–5670. https://doi.org/10.1021/acs.chemrev.0c00901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Dührkop K, Fleischauer M, Ludwig M et al (2019) SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods 16. https://doi.org/10.1038/s41592-019-0344-8

  79. Tsugawa H, Cajka T, Kind T et al (2015) MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12:523–526. https://doi.org/10.1038/nmeth.3393

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Barreiro JC, Tiritan ME, Bezerra Cass Q (2021) Challenges and innovations in chiral drugs in an environmental and bioanalysis perspective. Trends Anal Chem 142:116326. https://doi.org/10.1016/j.trac.2021.116326

    Article  CAS  Google Scholar 

  81. Moco S, Bino RJ, De Vos RCH, Vervoort J (2007) Metabolomics technologies and metabolite identification. TrAC Trends Anal Chem 26:855–866. https://doi.org/10.1016/j.trac.2007.08.003

    Article  CAS  Google Scholar 

  82. Wolfender JL, Nuzillard JM, Van Der Hooft JJJ et al (2019) Accelerating metabolite identification in natural product research: toward an ideal combination of liquid chromatography-high-resolution tandem mass spectrometry and NMR profiling, in silico databases, and chemometrics. Anal Chem 91:704–742. https://doi.org/10.1021/acs.analchem.8b05112

    Article  CAS  PubMed  Google Scholar 

  83. Creek DJ, Dunn WB, Fiehn O et al (2014) Metabolite identification: are you sure? And how do your peers gauge your confidence? Metabolomics 10:350–353. https://doi.org/10.1007/s11306-014-0656-8

    Article  CAS  Google Scholar 

  84. Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis: Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3:211–221. https://doi.org/10.1007/s11306-007-0082-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Spicer RA, Salek R, Steinbeck C (2017) Comment: A decade after the metabolomics standards initiative it’s time for a revision. Sci Data 4:2–4. https://doi.org/10.1038/sdata.2017.138

    Article  Google Scholar 

  86. Alseekh S, Aharoni A, Brotman Y et al (2021) Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat Methods 18:747–756. https://doi.org/10.1038/s41592-021-01197-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Cao G, Song Z, Hong Y et al (2020) Large-scale targeted metabolomics method for metabolite profiling of human samples. Anal Chim Acta 1125:144–151. https://doi.org/10.1016/j.aca.2020.05.053

    Article  CAS  PubMed  Google Scholar 

  88. Khamis MM, Adamko DJ, El-Aneed A (2019) Strategies and challenges in method development and validation for the absolute quantification of endogenous biomarker metabolites using liquid chromatography-tandem mass spectrometry. Mass Spectrom Rev:31–52. https://doi.org/10.1002/mas.21607

  89. Amador-Noguez D, Brasg IA, Feng X-J et al (2011) Metabolome remodeling during the acidogenic-solventogenic transition in Clostridium acetobutylicum. Appl Environ Microbiol 77:7984–7997. https://doi.org/10.1128/AEM.05374-11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Kirkwood JS, Legette LL, Miranda CL et al (2013) A metabolomics-driven elucidation of the anti-obesity mechanisms of xanthohumol. J Biol Chem 288(19000–19):013. https://doi.org/10.1074/jbc.M112.445452

    Article  CAS  Google Scholar 

  91. Kloehn J, Lunghi M, Varesio E et al (2021) Untargeted metabolomics uncovers the essential lysine transporter in Toxoplasma gondii. Metabolites 11:476. https://doi.org/10.3390/metabo11080476

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Dang L, White DW, Gross S et al (2009) Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462:739–744. https://doi.org/10.1038/nature08617

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Buescher JM, Driggers EM (2016) Integration of omics: more than the sum of its parts. Cancer Metab 4:4. https://doi.org/10.1186/s40170-016-0143-y

    Article  PubMed  PubMed Central  Google Scholar 

  94. Canzler S, Schor J, Busch W et al (2020) Prospects and challenges of multi-omics data integration in toxicology. Arch Toxicol 94:371–388

    Article  CAS  Google Scholar 

  95. Canzler S, Hackermüller J (2020) multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data. BMC Bioinformatics 21:1–13. https://doi.org/10.1186/s12859-020-03910-x

    Article  Google Scholar 

  96. Cavill R, Kamburov A, Ellis JK et al (2011) Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells. PLoS Comput Biol 7:e1001113. https://doi.org/10.1371/journal.pcbi.1001113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Wieder C, Frainay C, Poupin N et al (2021) Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis. bioRxiv 2021.05.24.445406

    Google Scholar 

  98. Huang DW, Sherman BT, Tan Q et al (2007) DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 35. https://doi.org/10.1093/nar/gkm415

  99. Kanehisa M, Goto S, Sato Y et al (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42:199–205. https://doi.org/10.1093/nar/gkt1076

    Article  CAS  Google Scholar 

  100. Cerami EG, Gross BE, Demir E et al (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39. https://doi.org/10.1093/nar/gkq1039

  101. Kuhn M, von Mering C, Campillos M et al (2008) STITCH: Interaction networks of chemicals and proteins. Nucleic Acids Res 36. https://doi.org/10.1093/nar/gkm795

  102. Kanehisa Laboratories (2021) KEGG metabolic pathways. https://www.genome.jp/pathway/map01100. Accessed 12 Oct 2021

  103. Michal G (2014) Metabolic pathways map part 1. http://biochemical-pathways.com/#/map/1. Accessed 12 Oct 2021

  104. King ZA, Dräger A, Ebrahim A et al (2015) Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Comput Biol 11:e1004321. https://doi.org/10.1371/journal.pcbi.1004321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Darzi Y, Letunic I, Bork P, Yamada T (2018) iPath3.0: interactive pathways explorer v3. Nucleic Acids Res 46:W510–W513. https://doi.org/10.1093/nar/gky299

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121. https://doi.org/10.1038/nprot.2009.203

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. https://doi.org/10.1038/nbt.1614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Segrè D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci 99:15112–15117. https://doi.org/10.1073/pnas.232349399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Patil KR, Nielsen J (2005) Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci 102:2685–2689. https://doi.org/10.1073/pnas.0406811102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Cakir T, Patil KR, Onsan Z iI et al (2006) Integration of metabolome data with metabolic networks reveals reporter reactions. Mol Syst Biol 2:50

    Article  Google Scholar 

  111. Jha AK, Huang SC-C, Sergushichev A et al (2015) Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42:419–430. https://doi.org/10.1016/j.immuni.2015.02.005

    Article  CAS  PubMed  Google Scholar 

  112. Li S, Park Y, Duraisingham S et al (2013) Predicting network activity from high throughput metabolomics. PLoS Comput Biol 9:e1003123. https://doi.org/10.1371/journal.pcbi.1003123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Bateman A, Martin M-J, Orchard S et al (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480–D489. https://doi.org/10.1093/nar/gkaa1100

    Article  CAS  Google Scholar 

  114. Weatherly CA, Du S, Parpia C et al (2017) d-Amino acid levels in perfused mouse brain tissue and blood: a comparative study. ACS Chem Nerosci 8:1251–1261. https://doi.org/10.1021/acschemneuro.6b00398

    Article  CAS  Google Scholar 

  115. Bennett BD, Kimball EH, Gao M et al (2009) Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat Chem Biol 5:593–599

    Article  CAS  Google Scholar 

  116. Fendt S-M, Buescher JM, Rudroff F et al (2010) Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity. Mol Syst Biol 6:356. https://doi.org/10.1038/msb.2010.11

    Article  PubMed  PubMed Central  Google Scholar 

  117. Fuhrer T, Zampieri M, Sévin DC et al (2017) Genomewide landscape of gene–metabolome associations in Escherichia coli. Mol Syst Biol 13(907):10.15252/msb.20167150

    Google Scholar 

  118. Zamboni N, Kümmel A, Heinemann M (2008) anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics 9:199. https://doi.org/10.1186/1471-2105-9-199

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Oliveira AP, Dimopoulos S, Busetto AG et al (2015) Inferring causal metabolic signals that regulate the dynamic TORC 1 -dependent transcriptome. Mol Syst Biol 11(1–16):10.15252/msb.20145475

    Google Scholar 

  120. Piazza I, Kochanowski K, Cappelletti V et al (2018) A map of protein-metabolite interactions reveals principles of chemical communication. Cell 172:358–372.e23. https://doi.org/10.1016/j.cell.2017.12.006

    Article  CAS  PubMed  Google Scholar 

  121. Digital Science & Research Solutions, Inc. (2021) dimensions.ai. https://app.dimensions.ai/discover/publication. Accessed 22 July 2021

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Acknowledgments

The authors thank Stefan Christen for critical discussion of the manuscript.

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Moco, S., Buescher, J.M. (2023). Metabolomics: Going Deeper, Going Broader, Going Further. In: Skirycz, A., Luzarowski, M., Ewald, J.C. (eds) Cell-Wide Identification of Metabolite-Protein Interactions. Methods in Molecular Biology, vol 2554. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2624-5_11

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