Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

Vailati-Riboni M, Palombo V, Loor JJ. What are omics sciences? In: Periparturient diseases of dairy cows. Cham: Springer International Publishing; 2017. p. 1–7.

Google Scholar 

Wang R, Li B, Lam SM, Shui G. Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression. J Genet Genomics. 2020;47:69–83. https://doi.org/10.1016/j.jgg.2019.11.009.

Article  CAS  PubMed  Google Scholar 

Gallart‐Ayala H, Teav T, Ivanisevic J. Metabolomics meets lipidomics: assessing the small molecule component of metabolism. BioEssays. 2020;42. https://doi.org/10.1002/bies.202000052.

Guijas C, Montenegro-Burke JR, Warth B, Spilker ME, Siuzdak G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat Biotechnol. 2018;36:316–20. https://doi.org/10.1038/nbt.4101.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Heiles S. Advanced tandem mass spectrometry in metabolomics and lipidomics—methods and applications. Anal Bioanal Chem. 2021;413:5927–48. https://doi.org/10.1007/s00216-021-03425-1.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rakusanova S, Fiehn O, Cajka T. Toward building mass spectrometry-based metabolomics and lipidomics atlases for biological and clinical research. TrAC Trends Anal Chem. 2023;158:116825. https://doi.org/10.1016/j.trac.2022.116825.

Article  CAS  Google Scholar 

Ribbenstedt A, Ziarrusta H, Benskin JP. Development, characterization and comparisons of targeted and non-targeted metabolomics methods. PLoS One. 2018;13:e0207082. https://doi.org/10.1371/journal.pone.0207082.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gertsman I, Barshop BA. Promises and pitfalls of untargeted metabolomics. J Inherit Metab Dis. 2018;41:355–66. https://doi.org/10.1007/s10545-017-0130-7.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deschamps E, Calabrese V, Schmitz I, Hubert-Roux M, Castagnos D, Afonso C. Advances in ultra-high-resolution mass spectrometry for pharmaceutical analysis. Molecules. 2023;28:2061. https://doi.org/10.3390/molecules28052061.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chaleckis R, Meister I, Zhang P, Wheelock CE. Challenges, progress and promises of metabolite annotation for LC–MS-based metabolomics. Curr Opin Biotechnol. 2019;55:44–50. https://doi.org/10.1016/j.copbio.2018.07.010.

Article  CAS  PubMed  Google Scholar 

Guo J, Huan T. Comparison of full-scan, data-dependent, and data-independent acquisition modes in liquid chromatography–mass spectrometry based untargeted metabolomics. Anal Chem. 2020;92:8072–80. https://doi.org/10.1021/acs.analchem.9b05135.

Article  CAS  PubMed  Google Scholar 

Wang R, Yin Y, Zhu Z-J. Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology. Anal Bioanal Chem. 2019;411:4349–57. https://doi.org/10.1007/s00216-019-01709-1.

Article  CAS  PubMed  Google Scholar 

Barbier Saint Hilaire P, Rousseau K, Seyer A, Dechaumet S, Damont A, Junot C, Fenaille F. Comparative evaluation of data dependent and data independent acquisition workflows implemented on an Orbitrap Fusion for untargeted metabolomics. Metabolites. 2020;10:158. https://doi.org/10.3390/metabo10040158.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, Kanazawa M, VanderGheynst J, Fiehn O, Arita M. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12:523–6. https://doi.org/10.1038/nmeth.3393.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yin Y, Wang R, Cai Y, Wang Z, Zhu Z-J. DecoMetDIA: deconvolution of multiplexed MS/MS spectra for metabolite identification in SWATH-MS-based untargeted metabolomics. Anal Chem. 2019;91:11897–904. https://doi.org/10.1021/acs.analchem.9b02655.

Article  CAS  PubMed  Google Scholar 

Stancliffe E, Schwaiger-Haber M, Sindelar M, Patti GJ. DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution. Nat Methods. 2021;18:779–87. https://doi.org/10.1038/s41592-021-01195-3.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tada I, Chaleckis R, Tsugawa H, Meister I, Zhang P, Lazarinis N, Dahlén B, Wheelock CE, Arita M. Correlation-based deconvolution (CorrDec) to generate high-quality MS2 spectra from data-independent acquisition in multisample studies. Anal Chem. 2020;92:11310–7. https://doi.org/10.1021/acs.analchem.0c01980.

Article  CAS  PubMed  Google Scholar 

Alseekh S, Aharoni A, Brotman Y, Contrepois K, D’Auria J, Ewald J, Ewald CJ, Fraser PD, Giavalisco P, Hall RD, Heinemann M, Link H, Luo J, Neumann S, Nielsen J, Perez de Souza L, Saito K, Sauer U, Schroeder FC, Schuster S, Siuzdak G, Skirycz A, Sumner LW, Snyder MP, Tang H, Tohge T, Wang Y, Wen W, Wu S, Xu G, Zamboni N, Fernie AR. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat Methods. 2021;18:747–56. https://doi.org/10.1038/s41592-021-01197-1.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hoskisson PA, Seipke RF. Cryptic or silent? The known unknowns, unknown knowns, and unknown unknowns of secondary metabolism. mBio. 2020;11. https://doi.org/10.1128/mBio.02642-20.

Little JL, Cleven CD, Brown SD. Identification of “known unknowns” utilizing accurate mass data and chemical abstracts service databases. J Am Soc Mass Spectrom. 2011;22:348–59. https://doi.org/10.1007/s13361-010-0034-3.

Article  CAS  PubMed  Google Scholar 

Little JL, Williams AJ, Pshenichnov A, Tkachenko V. Identification of “known unknowns” utilizing accurate mass data and ChemSpider. J Am Soc Mass Spectrom. 2012;23:179–85. https://doi.org/10.1007/s13361-011-0265-y.

Article  CAS  PubMed  Google Scholar 

Perez De Souza L, Alseekh S, Brotman Y, Fernie AR. Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Rev Proteomics. 2020;17:243–55. https://doi.org/10.1080/14789450.2020.1766975.

Article  CAS  PubMed  Google Scholar 

Phelan VV. Feature-based molecular networking for metabolite annotation. 2020;227–243.

Nothias L-F, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, Protsyuk I, Ernst M, Tsugawa H, Fleischauer M, Aicheler F, Aksenov AA, Alka O, Allard P-M, Barsch A, Cachet X, Caraballo-Rodriguez AM, Da Silva RR, Dang T, Garg N, Gauglitz JM, Gurevich A, Isaac G, Jarmusch AK, Kameník Z, Bin KK, Kessler N, Koester I, Korf A, Le Gouellec A, Ludwig M, Martin HC, McCall L-I, McSayles J, Meyer SW, Mohimani H, Morsy M, Moyne O, Neumann S, Neuweger H, Nguyen NH, Nothias-Esposito M, Paolini J, Phelan VV, Pluskal T, Quinn RA, Rogers S, Shrestha B, Tripathi A, van der Hooft JJJ, Vargas F, Weldon KC, Witting M, Yang H, Zhang Z, Zubeil F, Kohlbacher O, Böcker S, Alexandrov T, Bandeira N, Wang M, Dorrestein PC. Feature-based molecular networking in the GNPS analysis environment. Nat Methods. 2020;17:905–8. https://doi.org/10.1038/s41592-020-0933-6.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Quinn RA, Nothias L-F, Vining O, Meehan M, Esquenazi E, Dorrestein PC. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol Sci. 2017;38:143–54. https://doi.org/10.1016/j.tips.2016.10.011.

Article  CAS  PubMed  Google Scholar 

Olivon F, Elie N, Grelier G, Roussi F, Litaudon M, Touboul D. MetGem software for the generation of molecular networks based on the t-SNE algorithm. Anal Chem. 2018;90:13900–8. https://doi.org/10.1021/acs.analchem.8b03099.

Article  CAS  PubMed  Google Scholar 

Elie N, Santerre C, Touboul D. Generation of a molecular network from electron ionization mass spectrometry data by combining MZmine2 and MetGem software. Anal Chem. 2019;91:11489–92. https://doi.org/10.1021/acs.analchem.9b02802.

Article  CAS  PubMed  Google Scholar 

Neto FC, Raftery D. Expanding urinary metabolite annotation through integrated mass spectral similarity networking. Anal Chem. 2021;93:12001–10. https://doi.org/10.1021/acs.analchem.1c02041.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Le Daré B, Ferron P-J, Allard P-M, Clément B, Morel I, Gicquel T. New insights into quetiapine metabolism using molecular networking. Sci Rep. 2020;10:19921. https://doi.org/10.1038/s41598-020-77106-x.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hayes RN, Gross ML. [10] Collision-induced dissociation. 1990;237–263.

Martín-Sómer A, Yáñez M, Gaigeot M-P, Spezia R. Unimolecular fragmentation induced by low-energy collision: statistically or dynamically driven? J Phys Chem A. 2014;118:10882–93. https://doi.org/10.1021/jp5076059.

Article  CAS  PubMed  Google Scholar 

Zhou Z, Luo M, Chen X, Yin Y, Xiong X, Wang R, Zhu Z-J. Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics. Nat Commun. 2020;11:4334. https://doi.org/10.1038/s41467-020-18171-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Calabrese V, Schmitz-Afonso I, Prevost C, Afonso C, Elomri A. Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts. Anal Bioanal Chem. 2022;414:4103–18. https://doi.org/10.1007/s00216-022-04059-7.

Article  CAS  PubMed  Google Scholar 

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