Harmonized quality assurance/quality control provisions to assess completeness and robustness of MS1 data preprocessing for LC-HRMS-based suspect screening and non-targeted analysis
Chemical exposome; Contaminants of emerging concern; Data preprocessing; Exposomics; Harmonized QA/QC; High-resolution mass spectrometry; Metabolomics; Non-targeted analysis; Suspect screening analysis; Contaminants of emerging concerns; Exposomic; High resolution mass spectrometry; Non-targeted; Non-targeted analyse; Screening analysis; Suspect screening analyse; Analytical Chemistry; Spectroscopy
Résumé :
[en] Non-targeted and suspect screening analysis using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) holds great promise to comprehensively characterize complex chemical mixtures. Data preprocessing is a crucial part of the process, however, some limitations are observed: (i) peak-picking and feature extraction might be incomplete, especially for low abundant compounds, and (ii) limited reproducibility has been observed between laboratories and software for detected features and their relative quantification. We first conducted a critical review of existing solutions that could improve the reproducibility of preprocessing for LC-HRMS. Solutions include providing repositories and reporting guidelines, open and modular processing workflows, public benchmark datasets, tools to optimize the data preprocessing and to filter out false positive detections. We then propose harmonized quality assurance/quality control guidelines that would allow to assess the sensitivity of feature detection, reproducibility, integration accuracy, precision, accuracy, and consistency of data preprocessing for human biomonitoring, food and environmental communities.
Disciplines :
Sciences de l’environnement & écologie
Auteur, co-auteur :
Lennon, Sarah; Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) – UMR_S 1085, Rennes, France
Chaker, Jade; Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) – UMR_S 1085, Rennes, France
Hollender, Juliane; Swiss Federal Institute of Aquatic Science and Technology, Eawag, Dübendorf, Switzerland ; Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zürich, Switzerland
Huber, Carolin; Helmholtz Center for Environmental Research – UFZ, Department of Exposure Science, Leipzig, Germany
Schulze, Tobias; German Environment Agency (UBA), Berlin, Germany
Ahrens, Lutz; Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
Béen, Frederic; Chemistry for Environment & Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, Netherlands ; KWR Water Research Institute, Nieuwegein, Netherlands
Creusot, Nicolas; INRAE, French National Research Institute for Agriculture, Food & Environment. UR1454 EABX, Bordeaux Metabolome, France
Debrauwer, Laurent; Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France
Dervilly, Gaud; Oniris, INRAE, LABERCA, Nantes, France
Gabriel, Catherine; Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Greece ; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Greece
Guérin, Thierry; ANSES, Strategy and Programmes Department, Maisons-Alfort, France
Habchi, Baninia; INRS, Département Toxicologie et Biométrologie Laboratoire Biométrologie 1, Cedex, France
Jamin, Emilien L.; Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France
Kosjek, Tina; Jozef Stefan Institute, Department of Environmental Sciences, Ljubljana, Slovenia
Le Bizec, Bruno; Oniris, INRAE, LABERCA, Nantes, France
Meijer, Jeroen; Chemistry for Environment & Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Mol, Hans; Wageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Wageningen, Netherlands
Nijssen, Rosalie; Wageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Wageningen, Netherlands
Oberacher, Herbert; Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Innsbruck, Austria
Papaioannou, Nafsika; Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Greece ; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Greece
Parinet, Julien; ANSES, Laboratory for Food Safety, Maisons-Alfort, France
Sarigiannis, Dimosthenis; Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Greece ; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Greece
Stravs, Michael A.; Swiss Federal Institute of Aquatic Science and Technology, Eawag, Dübendorf, Switzerland ; Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zürich, Switzerland
Tkalec, Žiga; RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic ; Jozef Stefan Institute, Department of Environmental Sciences, Ljubljana, Slovenia
SCHYMANSKI, Emma ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Environmental Cheminformatics
Lamoree, Marja; Chemistry for Environment & Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Antignac, Jean-Philippe; Oniris, INRAE, LABERCA, Nantes, France
David, Arthur ; Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) – UMR_S 1085, Rennes, France
Harmonized quality assurance/quality control provisions to assess completeness and robustness of MS1 data preprocessing for LC-HRMS-based suspect screening and non-targeted analysis
This work was supported by the project Partnership for the Assessment of Risks from Chemicals (PARC) funded by the European Union research and innovation program Horizon Europe [grant numbers 101057014]. SL, JC and AD acknowledge the research infrastructure France Exposome. EJP, JK and DT acknowledge the research infrastructure RECETOX RI (LM2023069), H2020 CETOCOEN Excellence 857560 and OP RDE CZ.02.1.01/0.0/0.0/17_043/0009632).
Wang, Z., Walker, G.W., Muir, D.C.G., Nagatani-Yoshida, K., Toward a global understanding of chemical pollution: a first comprehensive analysis of national and regional chemical inventories. Environ. Sci. Technol. 54:5 (2020), 2575–2584, 10.1021/acs.est.9b06379.
Ganzleben, C., Antignac, J.-P., Barouki, R., Castaño, A., Fiddicke, U., Klánová, J., Lebret, E., Olea, N., Sarigiannis, D., Schoeters, G.R., Sepai, O., Tolonen, H., Kolossa-Gehring, M., Human biomonitoring as a tool to support chemicals regulation in the European union. Int. J. Hyg Environ. Health 220:2, Part A (2017), 94–97, 10.1016/j.ijheh.2017.01.007.
Brack, W., Aissa, S.A., Backhaus, T., Dulio, V., Escher, B.I., Faust, M., Hilscherova, K., Hollender, J., Hollert, H., Müller, C., Munthe, J., Posthuma, L., Seiler, T.-B., Slobodnik, J., Teodorovic, I., Tindall, A.J., de Aragão Umbuzeiro, G., Zhang, X., Altenburger, R., Effect-based methods are key. The European collaborative project SOLUTIONS recommends integrating effect-based methods for diagnosis and monitoring of water quality. Environ. Sci. Eur., 31(1), 2019, 10, 10.1186/s12302-019-0192-2.
Luijten, M., Vlaanderen, J., Kortenkamp, A., Antignac, J.-P., Barouki, R., Bil, W., van den Brand, A., den Braver-Sewradj, S., van Klaveren, J., Mengelers, M., Ottenbros, I., Rantakokko, P., Kolossa-Gehring, M., Lebret, E., Mixture risk assessment and human biomonitoring: lessons learnt from HBM4EU. Int. J. Hyg Environ. Health, 249, 2023, 114135, 10.1016/j.ijheh.2023.114135.
David, A., Chaker, J., Price, E.J., Bessonneau, V., Chetwynd, A.J., Vitale, C.M., Klánová, J., Walker, D.I., Antignac, J.-P., Barouki, R., Miller, G.W., Towards a comprehensive characterisation of the human internal chemical exposome: challenges and perspectives. Environ. Int., 156, 2021, 106630, 10.1016/j.envint.2021.106630.
Hollender, J., Schymanski, E.L., Ahrens, L., Alygizakis, N., Béen, F., Bijlsma, L., Brunner, A.M., Celma, A., Fildier, A., Fu, Q., Gago-Ferrero, P., Gil-Solsona, R., Haglund, P., Hansen, M., Kaserzon, S., Kruve, A., Lamoree, M., Margoum, C., Meijer, J., Merel, S., Rauert, C., Rostkowski, P., Samanipour, S., Schulze, B., Schulze, T., Singh, R.R., Slobodnik, J., Steininger-Mairinger, T., Thomaidis, N.S., Togola, A., Vorkamp, K., Vulliet, E., Zhu, L., Krauss, M., NORMAN guidance on suspect and non-target screening in environmental monitoring. Environ. Sci. Eur., 35(1), 2023, 75, 10.1186/s12302-023-00779-4.
Hollender, J., Schymanski, E.L., Singer, H.P., Ferguson, P.L., Nontarget screening with high resolution mass spectrometry in the environment: ready to go?. Environ. Sci. Technol. 51:20 (2017), 11505–11512, 10.1021/acs.est.7b02184.
Pourchet, M., Debrauwer, L., Klanova, J., Price, E.J., Covaci, A., Caballero-Casero, N., Oberacher, H., Lamoree, M., Damont, A., Fenaille, F., Vlaanderen, J., Meijer, J., Krauss, M., Sarigiannis, D., Barouki, R., Le Bizec, B., Antignac, J.-P., Suspect and non-targeted screening of chemicals of emerging concern for human biomonitoring, environmental health studies and support to risk assessment: from promises to challenges and harmonisation issues. Environ. Int., 139, 2020, 105545, 10.1016/j.envint.2020.105545.
Rampler, E., Abiead, Y.E., Schoeny, H., Rusz, M., Hildebrand, F., Fitz, V., Koellensperger, G., Recurrent topics in mass spectrometry-based metabolomics and lipidomics-standardization, coverage, and throughput. Anal. Chem. 93:1 (2021), 519–545, 10.1021/acs.analchem.0c04698.
Chaker, J., Gilles, E., Léger, T., Jégou, B., David, A., From metabolomics to HRMS-based exposomics: adapting peak picking and developing scoring for MS1 suspect screening. Anal. Chem. 93:3 (2021), 1792–1800, 10.1021/acs.analchem.0c04660.
El Abiead, Y., Milford, M., Schoeny, H., Rusz, M., Salek, R.M., Koellensperger, G., Power of mzRAPP-based performance assessments in MS1-based nontargeted feature detection. Anal. Chem. 94:24 (2022), 8588–8595, 10.1021/acs.analchem.1c05270.
Renner, G., Reuschenbach, M., Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability. Anal. Bioanal. Chem. 415:18 (2023), 4111–4123, 10.1007/s00216-023-04776-7.
Kirwan, J.A., Gika, H., Beger, R.D., Bearden, D., Dunn, W.B., Goodacre, R., Theodoridis, G., Witting, M., Yu, L.-R., Wilson, I.D., The metabolomics quality assurance and quality control consortium (mQACC). Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management. Metabolomics, 18(9), 2022, 70, 10.1007/s11306-022-01926-3.
Oberacher, H., Sasse, M., Antignac, J.-P., Guitton, Y., Debrauwer, L., Jamin, E.L., Schulze, T., Krauss, M., Covaci, A., Caballero-Casero, N., Rousseau, K., Damont, A., Fenaille, F., Lamoree, M., Schymanski, E.L., A European proposal for quality control and quality assurance of tandem mass spectral libraries. Environ. Sci. Eur., 32(1), 2020, 43, 10.1186/s12302-020-00314-9.
Broadhurst, D., Goodacre, R., Reinke, S.N., Kuligowski, J., Wilson, I.D., Lewis, M.R., Dunn, W.B., Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics, 14(6), 2018, 72, 10.1007/s11306-018-1367-3.
Dudzik, D., Barbas-Bernardos, C., García, A., Barbas, C., Quality assurance procedures for mass spectrometry untargeted metabolomics. A review. J. Pharmaceut. Biomed. Anal. 147 (2018), 149–173, 10.1016/j.jpba.2017.07.044.
Misra, B.B., Data normalization strategies in metabolomics: current challenges, approaches, and tools. Eur. J. Mass Spectrom. 26:3 (2020), 165–174, 10.1177/1469066720918446.
Cuevas-Delgado, P., Dudzik, D., Miguel, V., Lamas, S., Barbas, C., Data-dependent normalization strategies for untargeted metabolomics-a case study. Anal. Bioanal. Chem. 412:24 (2020), 6391–6405, 10.1007/s00216-020-02594-9.
Smith, C.A., Want, E.J., O'Maille, G., Abagyan, R., Siuzdak, G., XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78:3 (2006), 779–787, 10.1021/ac051437y.
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 12:6 (2015), 523–526, 10.1038/nmeth.3393.
Schmid, R., Heuckeroth, S., Korf, A., Smirnov, A., Myers, O., Dyrlund, T.S., Bushuiev, R., Murray, K.J., Hoffmann, N., Lu, M., Sarvepalli, A., Zhang, Z., Fleischauer, M., Dührkop, K., Wesner, M., Hoogstra, S.J., Rudt, E., Mokshyna, O., Brungs, C., Ponomarov, K., Mutabdžija, L., Damiani, T., Pudney, C.J., Earll, M., Helmer, P.O., Fallon, T.R., Schulze, T., Rivas-Ubach, A., Bilbao, A., Richter, H., Nothias, L.-F., Wang, M., Orešič, M., Weng, J.-K., Böcker, S., Jeibmann, A., Hayen, H., Karst, U., Dorrestein, P.C., Petras, D., Du, X., Pluskal, T., Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat. Biotechnol. 41:4 (2023), 447–449, 10.1038/s41587-023-01690-2.
Röst, H.L., Sachsenberg, T., Aiche, S., Bielow, C., Weisser, H., Aicheler, F., Andreotti, S., Ehrlich, H.-C., Gutenbrunner, P., Kenar, E., Liang, X., Nahnsen, S., Nilse, L., Pfeuffer, J., Rosenberger, G., Rurik, M., Schmitt, U., Veit, J., Walzer, M., Wojnar, D., Wolski, W.E., Schilling, O., Choudhary, J.S., Malmström, L., Aebersold, R., Reinert, K., Kohlbacher, O., OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13:9 (2016), 741–748, 10.1038/nmeth.3959.
Misra, B.B., New software tools, databases, and resources in metabolomics: updates from 2020. Metabolomics, 17(5), 2021, 49, 10.1007/s11306-021-01796-1.
Spicer, R., Salek, R.M., Moreno, P., Cañueto, D., Steinbeck, C., Navigating freely-available software tools for metabolomics analysis. Metabolomics, 13(9), 2017, 106, 10.1007/s11306-017-1242-7.
Stanstrup, J., Broeckling, C.D., Helmus, R., Hoffmann, N., Mathé, E., Naake, T., Nicolotti, L., Peters, K., Rainer, J., Salek, R.M., Schulze, T., Schymanski, E.L., Stravs, M.A., Thévenot, E.A., Treutler, H., Weber, R.J.M., Willighagen, E., Witting, M., Neumann, S., The metaRbolomics Toolbox in bioconductor and beyond. Metabolites, 9(10), 2019, 200, 10.3390/metabo9100200.
Yu, T., Park, Y., Johnson, J.M., Jones, D.P., apLCMS–Adaptive processing of high-resolution LC/MS data. Bioinformatics 25:15 (2009), 1930–1936, 10.1093/bioinformatics/btp291.
Hohrenk, L.L., Itzel, F., Baetz, N., Tuerk, J., Vosough, M., Schmidt, T.C., Comparison of software tools for liquid chromatography–high-resolution mass spectrometry data processing in nontarget screening of environmental samples. Anal. Chem. 92:2 (2020), 1898–1907, 10.1021/acs.analchem.9b04095.
Müller, E., Huber, C.E., Brack, W., Krauss, M., Schulze, T., Symbolic aggregate approximation improves gap filling in high-resolution mass spectrometry data processing. Anal. Chem. 92:15 (2020), 10425–10432, 10.1021/acs.analchem.0c00899.
Clark, T.N., Houriet, J., Vidar, W.S., Kellogg, J.J., Todd, D.A., Cech, N.B., Linington, R.G., Interlaboratory comparison of untargeted mass spectrometry data uncovers underlying causes for variability. J. Nat. Prod. 84:3 (2021), 824–835, 10.1021/acs.jnatprod.0c01376.
Baker, E.S., Patti, G.J., Perspectives on data analysis in metabolomics: points of agreement and disagreement from the 2018 ASMS fall workshop. J. Am. Soc. Mass Spectrom. 30:10 (2019), 2031–2036, 10.1007/s13361-019-02295-3.
Smith, R., Tostengard, A.R., Quantitative evaluation of ion chromatogram extraction algorithms. J. Proteome Res. 19:5 (2020), 1953–1964, 10.1021/acs.jproteome.9b00768.
Reuschenbach, M., Hohrenk-Danzouma, L.L., Schmidt, T.C., Renner, G., Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra. Anal. Bioanal. Chem. 414:22 (2022), 6635–6645, 10.1007/s00216-022-04224-y.
Rafiei, A., Sleno, L., Comparison of peak-picking workflows for untargeted liquid chromatography/high-resolution mass spectrometry metabolomics data analysis. Rapid Commun. Mass Spectrom. 29:1 (2015), 119–127, 10.1002/rcm.7094.
Liao, J., Zhang, Y., Zhang, W., Zeng, Y., Zhao, J., Zhang, J., Yao, T., Li, H., Shen, X., Wu, G., Zhang, W., Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data. J. Chromatogr. A, 1687, 2023, 463700, 10.1016/j.chroma.2022.463700.
Li, Z., Lu, Y., Guo, Y., Cao, H., Wang, Q., Shui, W., Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection. Anal. Chim. Acta 1029 (2018), 50–57, 10.1016/j.aca.2018.05.001.
Chen, Y., Xu, J., Zhang, R., Shen, G., Song, Y., Sun, J., He, J., Zhan, Q., Abliz, Z., Assessment of data pre-processing methods for LC-MS/MS-Based metabolomics of uterine cervix cancer. Analyst 138:9 (2013), 2669–2677, 10.1039/C3AN36818A.
Guo, J., Huan, T., Mechanistic understanding of the discrepancies between common peak picking algorithms in liquid chromatography–mass spectrometry-based metabolomics. Anal. Chem. 95:14 (2023), 5894–5902, 10.1021/acs.analchem.2c04887.
Coble, J.B., Fraga, C.G., Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery. J. Chromatogr. A 1358 (2014), 155–164, 10.1016/j.chroma.2014.06.100.
Baran, R., Untargeted metabolomics suffers from incomplete raw data processing. Metabolomics, 13(9), 2017, 107, 10.1007/s11306-017-1246-3.
Smith, R., Ventura, D., Prince, J.T., Controlling for confounding variables in MS-omics protocol: why modularity matters. Briefings Bioinf. 15:5 (2014), 768–770, 10.1093/bib/bbt049.
Li, S., Siddiqa, A., Thapa, M., Chi, Y., Zheng, S., Trackable and scalable LC-MS metabolomics data processing using Asari. Nat. Commun., 14(1), 2023, 4113, 10.1038/s41467-023-39889-1.
Houriet, J., Vidar, W.S., Manwill, P.K., Todd, D.A., Cech, N.B., How low can you go? Selecting intensity thresholds for untargeted metabolomics data preprocessing. Anal. Chem. 94:51 (2022), 17964–17971, 10.1021/acs.analchem.2c04088.
Hajjar, G., Barros Santos, M.C., Bertrand-Michel, J., Canlet, C., Castelli, F., Creusot, N., Dechaumet, S., Diémé, B., Giacomoni, F., Giraudeau, P., Guitton, Y., Thévenot, E., Tremblay-Franco, M., Junot, C., Jourdan, F., Fenaille, F., Comte, B., Pétriacq, P., Pujos-Guillot, E., Scaling-up metabolomics: current state and perspectives. TrAC, Trends Anal. Chem., 167, 2023, 117225, 10.1016/j.trac.2023.117225.
Müller, E., Huber, C., Beckers, L.-M., Brack, W., Krauss, M., Schulze, T., A data set of 255,000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods. Metabolites, 10(4), 2020, 162, 10.3390/metabo10040162.
Goodacre, R., Broadhurst, D., Smilde, A.K., Kristal, B.S., Baker, J.D., Beger, R., Bessant, C., Connor, S., Capuani, G., Craig, A., Ebbels, T., Kell, D.B., Manetti, C., Newton, J., Paternostro, G., Somorjai, R., Sjöström, M., Trygg, J., Wulfert, F., Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3:3 (2007), 231–241, 10.1007/s11306-007-0081-3.
Considine, E.C., Thomas, G., Boulesteix, A.L., Khashan, A.S., Kenny, L.C., Critical review of reporting of the data analysis step in metabolomics. Metabolomics, 14(1), 2017, 7, 10.1007/s11306-017-1299-3.
Viant, M.R., Ebbels, T.M.D., Beger, R.D., Ekman, D.R., Epps, D.J.T., Kamp, H., Leonards, P.E.G., Loizou, G.D., MacRae, J.I., van Ravenzwaay, B., Rocca-Serra, P., Salek, R.M., Walk, T., Weber, R.J.M., Use cases, best practice and reporting standards for metabolomics in regulatory toxicology. Nat. Commun., 10, 2019, 3041, 10.1038/s41467-019-10900-y.
Peter, K.T., Phillips, A.L., Knolhoff, A.M., Gardinali, P.R., Manzano, C.A., Miller, K.E., Pristner, M., Sabourin, L., Sumarah, M.W., Warth, B., Sobus, J.R., Nontargeted analysis study reporting tool: a framework to improve research transparency and reproducibility. Anal. Chem. 93:41 (2021), 13870–13879, 10.1021/acs.analchem.1c02621.
Sud, M., Fahy, E., Cotter, D., Azam, K., Vadivelu, I., Burant, C., Edison, A., Fiehn, O., Higashi, R., Nair, K.S., Sumner, S., Subramaniam, S., Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 44:Database issue (2016), D463–D470, 10.1093/nar/gkv1042.
Haug, K., Cochrane, K., Nainala, V.C., Williams, M., Chang, J., Jayaseelan, K.V., O'Donovan, C., MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 48:D1 (2020), D440–D444, 10.1093/nar/gkz1019.
Leao, T.F., Clark, C.M., Bauermeister, A., Elijah, E.O., Gentry, E., Husband, M., Faria de Oliveira, M., Bandeira, N., Wang, M., Dorrestein, P.C., Quick-start for untargeted metabolomics analysis in GNPS. Nat. Metab. 3:7 (2021), 880–882, 10.1038/s42255-021-00429-0.
ProteomeCentral Datasets. https://proteomecentral.proteomexchange.org/. (Accessed 26 November 2023)
MetabolomeXchange. http://www.metabolomexchange.org/site/. (Accessed 26 November 2023)
Alygizakis, N.A., Oswald, P., Thomaidis, N.S., Schymanski, E.L., Aalizadeh, R., Schulze, T., Oswaldova, M., Slobodnik, J., NORMAN digital sample freezing platform: a European virtual platform to exchange liquid chromatography high resolution-mass spectrometry data and screen suspects in “digitally frozen” environmental samples. TrAC, Trends Anal. Chem. 115 (2019), 129–137, 10.1016/j.trac.2019.04.008.
Home - Digital Sample Freezing Platform. https://dsfp.norman-data.eu/(accessed 2023-November-26).
Schulze, B., Jeon, Y., Kaserzon, S., Heffernan, A.L., Dewapriya, P., O'Brien, J., Gomez Ramos, M.J., Ghorbani Gorji, S., Mueller, J.F., Thomas, K.V., Samanipour, S., An assessment of quality assurance/quality control efforts in high resolution mass spectrometry non-target workflows for analysis of environmental samples. TrAC, Trends Anal. Chem., 133, 2020, 116063, 10.1016/j.trac.2020.116063.
Giacomoni, F., Le Corguillé, G., Monsoor, M., Landi, M., Pericard, P., Pétéra, M., Duperier, C., Tremblay-Franco, M., Martin, J.-F., Jacob, D., Goulitquer, S., Thévenot, E.A., Caron, C., Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics 31:9 (2015), 1493–1495, 10.1093/bioinformatics/btu813.
Pang, Z., Zhou, G., Ewald, J., Chang, L., Hacariz, O., Basu, N., Xia, J., Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 17:8 (2022), 1735–1761, 10.1038/s41596-022-00710-w.
An Open Software Development-Based Ecosystem of R Packages for Metabolomics Data Analysis. https://doi.org/10.5281/zenodo.7936787.
Helmus, R., ter Laak, T.L., van Wezel, A.P., de Voogt, P., Schymanski, E.L., patRoon: open source software platform for environmental mass spectrometry based non-target screening. J. Cheminf., 13(1), 2021, 1, 10.1186/s13321-020-00477-w.
Ramus, C., Hovasse, A., Marcellin, M., Hesse, A.-M., Mouton-Barbosa, E., Bouyssié, D., Vaca, S., Carapito, C., Chaoui, K., Bruley, C., Garin, J., Cianférani, S., Ferro, M., Van Dorssaeler, A., Burlet-Schiltz, O., Schaeffer, C., Couté, Y., Gonzalez de Peredo, A., Benchmarking quantitative label-free LC–MS data processing workflows using a complex spiked proteomic standard dataset. J. Proteonomics 132 (2016), 51–62, 10.1016/j.jprot.2015.11.011.
Myers, O.D., Sumner, S.J., Li, S., Barnes, S., Du, X., Detailed investigation and comparison of the XCMS and MZmine 2 chromatogram construction and chromatographic peak detection methods for preprocessing mass spectrometry metabolomics data. Anal. Chem. 89:17 (2017), 8689–8695, 10.1021/acs.analchem.7b01069.
Henning, J., Tostengard, A., Smith, R., A peptide-level fully annotated data set for quantitative evaluation of precursor-aware mass spectrometry data processing algorithms. J. Proteome Res. 18:1 (2019), 392–398, 10.1021/acs.jproteome.8b00659.
Schulze, B., van Herwerden, D., Allan, I., Bijlsma, L., Etxebarria, N., Hansen, M., Merel, S., Vrana, B., Aalizadeh, R., Bajema, B., Dubocq, F., Coppola, G., Fildier, A., Fialová, P., Frøkjær, E., Grabic, R., Gago-Ferrero, P., Gravert, T., Hollender, J., Huynh, N., Jacobs, G., Jonkers, T., Kaserzon, S., Lamoree, M., Le Roux, J., Mairinger, T., Margoum, C., Mascolo, G., Mebold, E., Menger, F., Miège, C., Meijer, J., Moilleron, R., Murgolo, S., Peruzzo, M., Pijnappels, M., Reid, M., Roscioli, C., Soulier, C., Valsecchi, S., Thomaidis, N., Vulliet, E., Young, R., Samanipour, S., Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis. Sci. Data, 8(1), 2021, 223, 10.1038/s41597-021-01002-w.
Eliasson, M., Rännar, S., Madsen, R., Donten, M.A., Marsden-Edwards, E., Moritz, T., Shockcor, J.P., Johansson, E., Trygg, J., Strategy for optimizing LC-MS data processing in metabolomics: a design of experiments approach. Anal. Chem. 84:15 (2012), 6869–6876, 10.1021/ac301482k.
Zheng, H., Clausen, M.R., Dalsgaard, T.K., Mortensen, G., Bertram, H.C., Time-saving design of experiment protocol for optimization of LC-MS data processing in metabolomic approaches. Anal. Chem. 85:15 (2013), 7109–7116, 10.1021/ac4020325.
Kiefer, K., Du, L., Singer, H., Hollender, J., Identification of LC-HRMS nontarget signals in groundwater after source related prioritization. Water Res., 196, 2021, 116994, 10.1016/j.watres.2021.116994.
Dom, I., Biré, R., Hort, V., Lavison-Bompard, G., Nicolas, M., Guérin, T., Extended targeted and non-targeted strategies for the analysis of marine toxins in mussels and oysters by (LC-HRMS). Toxins, 10(9), 2018, 375, 10.3390/toxins10090375.
Hu, M., Krauss, M., Brack, W., Schulze, T., Optimization of LC-orbitrap-HRMS acquisition and MZmine 2 data processing for nontarget screening of environmental samples using design of experiments. Anal. Bioanal. Chem. 408:28 (2016), 7905–7915, 10.1007/s00216-016-9919-8.
Manier, S.K., Keller, A., Meyer, M.R., Automated optimization of XCMS parameters for improved peak picking of liquid chromatography–mass spectrometry data using the coefficient of variation and parameter sweeping for untargeted metabolomics. Drug Test. Anal. 11:6 (2019), 752–761, 10.1002/dta.2552.
Uppal, K., Soltow, Q.A., Strobel, F.H., Pittard, W.S., Gernert, K.M., Yu, T., Jones, D.P., xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data. BMC Bioinf., 14(1), 2013, 15, 10.1186/1471-2105-14-15.
Ju, R., Liu, X., Zheng, F., Zhao, X., Lu, X., Lin, X., Zeng, Z., Xu, G., A graph density-based strategy for features fusion from different peak extract software to achieve more metabolites in metabolic profiling from high-resolution mass spectrometry. Anal. Chim. Acta 1139 (2020), 8–14, 10.1016/j.aca.2020.09.029.
Brodsky, L., Moussaieff, A., Shahaf, N., Aharoni, A., Rogachev, I., Evaluation of peak picking quality in LC−MS metabolomics data. Anal. Chem. 82:22 (2010), 9177–9187, 10.1021/ac101216e.
Libiseller, G., Dvorzak, M., Kleb, U., Gander, E., Eisenberg, T., Madeo, F., Neumann, S., Trausinger, G., Sinner, F., Pieber, T., Magnes, C.I.P.O., A tool for automated optimization of XCMS parameters. BMC Bioinf., 16, 2015, 118, 10.1186/s12859-015-0562-8.
Delabriere, A., Warmer, P., Brennsteiner, V., Zamboni, N.S.L.A.W., A scalable and self-optimizing processing workflow for untargeted LC-MS. Anal. Chem. 93:45 (2021), 15024–15032, 10.1021/acs.analchem.1c02687.
McLean, C., Kujawinski, E.B., AutoTuner: high fidelity and robust parameter selection for metabolomics data processing. Anal. Chem. 92:8 (2020), 5724–5732, 10.1021/acs.analchem.9b04804.
Guo, J., Shen, S., Huan, T., Paramounter: direct measurement of universal parameters to process metabolomics data in a “white box.”. Anal. Chem. 94:10 (2022), 4260–4268, 10.1021/acs.analchem.1c04758.
Guo, J., Shen, S., Xing, S., Chen, Y., Chen, F., Porter, E.M., Yu, H., Huan, T., EVA: evaluation of metabolic feature fidelity using a deep learning model trained with over 25000 extracted ion chromatograms. Anal. Chem. 93:36 (2021), 12181–12186, 10.1021/acs.analchem.1c01309.
Want, E.J., Wilson, I.D., Gika, H., Theodoridis, G., Plumb, R.S., Shockcor, J., Holmes, E., Nicholson, J.K., Global metabolic profiling procedures for urine using UPLC–MS. Nat. Protoc. 5:6 (2010), 1005–1018, 10.1038/nprot.2010.50.
Ju, R., Liu, X., Zheng, F., Zhao, X., Lu, X., Zeng, Z., Lin, X., Xu, G., Removal of false positive features to generate authentic peak table for high-resolution mass spectrometry-based metabolomics study. Anal. Chim. Acta 1067 (2019), 79–87, 10.1016/j.aca.2019.04.011.
Fraisier-Vannier, O., Chervin, J., Cabanac, G., Puech, V., Fournier, S., Durand, V., Amiel, A., André, O., Benamar, O.A., Dumas, B., Tsugawa, H., Marti, G., MS-CleanR: a feature-filtering workflow for untargeted LC–MS based metabolomics. Anal. Chem. 92:14 (2020), 9971–9981, 10.1021/acs.analchem.0c01594.
Pirttilä, K., Balgoma, D., Rainer, J., Pettersson, C., Hedeland, M., Brunius, C., Comprehensive peak characterization (CPC) in untargeted LC–MS analysis. Metabolites, 12(2), 2022, 137, 10.3390/metabo12020137.
Gloaguen, Y., Kirwan, J.A., Beule, D., Deep learning-assisted peak curation for large-scale LC-MS metabolomics. Anal. Chem. 94:12 (2022), 4930–4937, 10.1021/acs.analchem.1c02220.
Kantz, E.D., Tiwari, S., Watrous, J.D., Cheng, S., Jain, M., Deep neural networks for classification of LC-MS spectral peaks. Anal. Chem. 91:19 (2019), 12407–12413, 10.1021/acs.analchem.9b02983.
Chetnik, K., Petrick, L., Pandey, G., MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data. Metabolomics, 16(11), 2020, 117, 10.1007/s11306-020-01738-3.
Albóniga, O.E., González, O., Alonso, R.M., Xu, Y., Goodacre, R., Optimization of XCMS parameters for LC–MS metabolomics: an assessment of automated versus manual tuning and its effect on the final results. Metabolomics, 16(1), 2020, 14, 10.1007/s11306-020-1636-9.
Guo, J., Yu, H., Xing, S., Huan, T., Addressing big data challenges in mass spectrometry-based metabolomics. Chem. Commun. 58:72 (2022), 9979–9990, 10.1039/D2CC03598G.
Giné, R., Capellades, J., Badia, J.M., Vughs, D., Schwaiger-Haber, M., Alexandrov, T., Vinaixa, M., Brunner, A.M., Patti, G.J., Yanes, O., HERMES: a molecular-formula-oriented method to target the metabolome. Nat. Methods 18:11 (2021), 1370–1376, 10.1038/s41592-021-01307-z.
Fakouri Baygi, S., Kumar, Y., Barupal, D.K., IDSL.IPA characterizes the organic chemical space in untargeted LC/HRMS data sets. J. Proteome Res. 21:6 (2022), 1485–1494, 10.1021/acs.jproteome.2c00120.
Woldegebriel, M., Derks, E., Artificial neural network for probabilistic feature recognition in liquid chromatography coupled to high-resolution mass spectrometry. Anal. Chem. 89:2 (2017), 1212–1221, 10.1021/acs.analchem.6b03678.
Wang, R., Lu, M., An, S., Wang, J., Yu, C., 3D-MSNet: a point cloud-based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data. Bioinformatics, 39(5), 2023, btad195, 10.1093/bioinformatics/btad195.
Melnikov, A.D., Tsentalovich, Y.P., Yanshole, V.V., Deep learning for the precise peak detection in high-resolution LC–MS data. Anal. Chem. 92:1 (2020), 588–592, 10.1021/acs.analchem.9b04811.
Bueschl, C., Doppler, M., Varga, E., Seidl, B., Flasch, M., Warth, B., Zanghellini, J., PeakBot: machine-learning-based chromatographic peak picking. Bioinformatics 38:13 (2022), 3422–3428, 10.1093/bioinformatics/btac344.
Samanipour, S., O'Brien, J.W., Reid, M.J., Thomas, K.V., Self adjusting algorithm for the nontargeted feature detection of high resolution mass spectrometry coupled with liquid chromatography profile data. Anal. Chem. 91:16 (2019), 10800–10807, 10.1021/acs.analchem.9b02422.
Woldegebriel, M., Vivó-Truyols, G., Probabilistic model for untargeted peak detection in LC–MS using bayesian statistics. Anal. Chem. 87:14 (2015), 7345–7355, 10.1021/acs.analchem.5b01521.
Liu, Y., Yang, Y., Chen, W., Shen, F., Xie, L., Zhang, Y., Zhai, Y., He, F., Zhu, Y., Chang, C., DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis. Nat. Commun., 14(1), 2023, 8188, 10.1038/s41467-023-43909-5.
Skoraczyński, G., Gambin, A., Miasojedow, B. Alignstein, Optimal transport for improved LC-MS retention time alignment. GigaScience, 11, 2022, giac101, 10.1093/gigascience/giac101.
Vitale, C.M., Lommen, A., Huber, C., Wagner, K., Garlito Molina, B., Nijssen, R., Price, E.J., Blokland, M., van Tricht, F., Mol, H.G.J., Krauss, M., Debrauwer, L., Pardo, O., Leon, N., Klanova, J., Antignac, J.-P., Harmonized quality assurance/quality control provisions for nontargeted measurement of urinary pesticide biomarkers in the HBM4EU multisite SPECIMEn study. Anal. Chem. 94:22 (2022), 7833–7843, 10.1021/acs.analchem.2c00061.
Place, B.J., Ulrich, E.M., Challis, J.K., Chao, A., Du, B., Favela, K., Feng, Y.-L., Fisher, C.M., Gardinali, P., Hood, A., Knolhoff, A.M., McEachran, A.D., Nason, S.L., Newton, S.R., Ng, B., Nuñez, J., Peter, K.T., Phillips, A.L., Quinete, N., Renslow, R., Sobus, J.R., Sussman, E.M., Warth, B., Wickramasekara, S., Williams, A.J., An introduction to the benchmarking and publications for non-targeted analysis working group. Anal. Chem. 93:49 (2021), 16289–16296, 10.1021/acs.analchem.1c02660.
Knolhoff, A.M., Premo, J.H., Fisher, C.M., A proposed quality control standard mixture and its uses for evaluating nontargeted and suspect screening LC/HR-MS method performance. Anal. Chem. 93:3 (2021), 1596–1603, 10.1021/acs.analchem.0c04036.
Adams, K.J., Pratt, B., Bose, N., Dubois, L.G., St John-Williams, L., Perrott, K.M., Ky, K., Kapahi, P., Sharma, V., MacCoss, M.J., Moseley, M.A., Colton, C.A., MacLean, B.X., Schilling, B., Thompson, J.W., Skyline for small molecules: a unifying software package for quantitative metabolomics. J. Proteome Res. 19:4 (2020), 1447–1458, 10.1021/acs.jproteome.9b00640.
Chaker, J., Gilles, E., Monfort, C., Chevrier, C., Lennon, S., David, A. Scannotation, A suspect screening tool for the rapid pre-annotation of the human LC-HRMS-based chemical exposome. Environ. Sci. Technol., 2023, 10.1021/acs.est.3c04764.
Celma, A., Ahrens, L., Gago-Ferrero, P., Hernández, F., López, F., Lundqvist, J., Pitarch, E., Sancho, J.V., Wiberg, K., Bijlsma, L., The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment. Chemosphere, 280, 2021, 130799, 10.1016/j.chemosphere.2021.130799.
Gabelica, V., Shvartsburg, A.A., Afonso, C., Barran, P., Benesch, J.L.P., Bleiholder, C., Bowers, M.T., Bilbao, A., Bush, M.F., Campbell, J.L., Campuzano, I.D.G., Causon, T., Clowers, B.H., Creaser, C.S., De Pauw, E., Far, J., Fernandez-Lima, F., Fjeldsted, J.C., Giles, K., Groessl, M., Hogan Jr, C.J., Hann, S., Kim, H.I., Kurulugama, R.T., May, J.C., McLean, J.A., Pagel, K., Richardson, K., Ridgeway, M.E., Rosu, F., Sobott, F., Thalassinos, K., Valentine, S.J., Wyttenbach, T., Recommendations for reporting ion mobility mass spectrometry measurements. Mass Spectrom. Rev. 38:3 (2019), 291–320, 10.1002/mas.21585.
