Application of Non-Targeted Screening in Metabolites: History
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Subjects: Chemistry, Analytical
Contributor:
Michael Sasse
,
Matthias Rainer

Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. 

  • non-target screening
  • LC-HRMS/MS
  • metabolites
  • annotation

1. Introduction

Natural products represent a big market, with 166 billion US-dollars of sales volume throughout the year 2019 in the US, which implies a growth of sales of 4.8% compared to 2018 [1]. This is the reason why phyto-analysis is an important field to assess metabolites in phyto samples for their possible compounds with health benefits and to control their quality in terms of contamination due to their environment, cultivation, or additives, e.g., pesticides, fertilizer, or other toxic compounds. A possible workflow for the detection of metabolites and contaminants in phyto samples using non-target screening is shown in Figure 1. Examples for the need of quality control of phyto samples are pesticides in wine and the addition of diethylene glycol in wine [2] in 1984 in Austria, which was used to simulate higher quality and to obtain a higher volume of product [3]. However, due to the Matthew effect, the detection of those contaminants is hampered [4]. This effect describes the psychological effect that decisions on targets of studies are based on occurrence in prior studies, rather than considering that additional factors may have an influence on the studied object. Those compounds represent the so-called “known knowns” and “known unknowns” described with the “Rumsfeld Quadrants” (Figure 2) by Stein [5].
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Figure 1. Workflow for non-target screening.
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Figure 2. “Rumsfeld Quadrants” showing the intersection of yes/no answers for whether analysts expect a compound to be identified in the sample (prior probability) and whether it was identified in a library search. Adapted with permission from [5].
Despite that, real new knowledge is generated by finding unexpected compounds in a sample, which are described as “unknown knowns” and “unknown unknowns”. To find those unknowns, non-target screening represents the method of choice. For this purpose, three major techniques are used to acquire spectra of samples after sample preparation: one of these techniques is nuclear magnetic resonance (NMR) [6][7], the other two techniques represent liquid chromatography coupled to a high-resolution mass spectrometer (LC-HRMS), and gas chromatography coupled to a mass spectrometer (GC-MS) [8]. The latter was used for a long period of time as gold standard to detect volatile compounds, due to its instrument standardization and, therefore, its comparability of data acquired by different laboratories . A major disadvantage of GC-MS is that polar and thermally labile compounds must be derivatized in order to analyze them [9]. In contrast, LC-HRMS/MS can be used for those compounds with minor sample preparation. In particular, the ability to obtain fragment spectra by collision-induced dissociation, after the ionization process in LC-HRMS [10], which represents a fingerprint of the compound’s structure, facilitates the identification. After the acquisition of spectra, data processing has to be conducted to separate compound spectra from each other, using retention times and precursor masses. To be able to conduct non-target screening, spectral databases in combination with search algorithms are needed to compare the acquired fragment spectra with spectra of reference standards for the identification of compounds. However, the occurrence of these reference spectra is a bottleneck. For a total amount of 129 million registered compounds [11], the biggest database for non-volatile compounds includes just more than 40,000 spectra of more than 15,000 compounds [12]. This exemplifies that only a fraction of compounds in samples can be found by comparing LC-HRMS/MS fragment spectra with databases, and more effort must be taken to expand the number of compounds in these libraries. This technique of identification was widely used for the identification of water pollutants [13][14][15][16][17] and drugs [18][19][20]. Jorge et al. published a review describing several targeted applications of LC-MS for metabolite screening [21].

2. Applications

Non-target screening can be applied in various fields of phyto-analysis. Several reviews were published describing workflows for non-target screening, and the most important steps are also mentioned in this review. These workflows can be found in references [22][23][24][25]. Kalogiouri et al. gave an overview of different efforts for the analysis of olive oils [26], in particular, distinguishing differences between extra virgin and virgin olive oil, which corresponds to its quality, by non-target metabolite profiling and the additional use of chemometrical tools. In addition, some articles were mentioned in which the origins of olive oils were discriminated with non-target screening. Another application of quality control with non-target screening in phyto samples is the determination of toxic compounds, such as natural toxins or pesticides, which could be facilitated by applying non-target screening routinely because of its high throughput capacity and relatively simple sample preparation, especially if more appropriate high-quality spectra are available in public repositories. Righetti et al. described in their review different methods for evaluating contaminations of mycotoxins originating from fungi infestation in crops [27]. Carlier et al. identified and quantified toxic compounds of sea mango [28]. An additional approach was published by Pérez-Ortega et al. [29] by using an in-house database for the annotation of contaminants, such as mycotoxins and pesticides in food samples.
The identification of pesticide residues and other contaminants in phyto samples is an important task due to their possible toxic effects, which is why these and other compounds are regulated worldwide [30]. Interesting studies were published which investigated the pesticide contamination of alcoholic beverages, such as beer and wine [31][32]. Bolaños et al. studied the presence of pesticides in 5 beer samples and 15 wine samples by acquiring data with a UHPLC–MS/MS system [2]. Due to matrix effects in wine, the wine samples were diluted. The results showed that no pesticides were present in beer samples, by several were found in several wine samples. This result corresponds to the study of Inoue et al. [33]. This study determined the number of pesticides present throughout the different steps of beer brewing by using LC-MS/MS. For this purpose, the authors spiked ground malt with more than 300 pesticides and brewed beer with this contaminated malt. The results showed that most pesticides were reduced in the wort and adsorbed onto the grain after meshing. At the end of the brewing process, only pesticides having a log P below 2 were found in the finished beer. Apart from beer, several articles on wine analysis have also been published. A review which describes different studies of untargeted wine analysis was published by Pinu et al. [34]. Ruocco et al. were able to determine the vintage and color of German and Italian wines by conducting metabolite profiling [35]. In addition, Arbulu et al. identified 411 metabolites of Graciano red wine and were able to differentiate Tempranillo and Graciano wines from each other using 15 metabolites as biomarkers [36]. Further, the authors created a database containing 2080 oenological compounds. Arapitsas et al. could differentiate six different wine cultivars by conducting chemometrical marker detection and a principal component analysis [37]. Diaz et al. could differentiate three wines of protected denominations of Spanish origin using their metabolic profile [38]. A similar approach was described by Li et al. for the differentiation of five truffle species by their metabolic profiles [39].
Most of the non-target approaches concerning phyto-analysis had the goal of identify phenolic compounds [40][41][42][43][44]. The majority of authors of these articles performed a manual annotation of the compounds with data in the literature or in spectral databases. Lin et al. conducted a profiling of oligomeric proanthocyanidins by comparing the acquired MS/MS spectra with computed fragment spectra [45]. A metabolite profiling of Rhus coriaria (Sumac) annotating metabolites with the literature was conducted by Abu-Reidah et al. [46]. Furthermore, Regazzoni et al. profiled gallotannins and flavonoids with mass spectral databases [47].
El Sayed et al. conducted a characterization of aloe vera species by comparing acquired MS/MS spectra with the literature [48]. This plant is also widely used in cosmetics, and was screened for illicit contents by Meng et al. [49]. In this study, 123 cosmetic samples were screened with Orbitrap using an in-house mass spectral database for the annotation of compounds in these samples.

This entry is adapted from the peer-reviewed paper 10.3390/separations9120415

References

  1. New Hope Network. Natural Retail Market Size and Stats|Market Overview. 2020. Available online: https://www.newhope.com/market-data-and-analysis/market-overview-2020-natural-retail-market-size-and-stats (accessed on 13 July 2021).
  2. Bolaños, P.P.; Romero-González, R.; Frenich, A.G.; Vidal, J.L.M. Application of hollow fibre liquid phase microextraction for the multiresidue determination of pesticides in alcoholic beverages by ultra-high pressure liquid chromatography coupled to tandem mass spectrometry. J. Chromatogr. A 2008, 1208, 16–24. https://doi.org/10.1016/j.chroma.2008.08.059.
  3. Paar, M. On the History of Austrian Wine Law from 1907 to 1985. JEHL 2019, 10, 15–25.
  4. Daughton, C.G. The Matthew Effect and widely prescribed pharmaceuticals lacking environmental monitoring: Case study of an exposure-assessment vulnerability. Sci. Total Environ. 2014, 466, 315–325. https://doi.org/10.1016/j.scitotenv.2013.06.111.
  5. Stein, S. Mass spectral reference libraries: An ever-expanding resource for chemical identification. Anal. Chem. 2012, 84, 7274–7282. https://doi.org/10.1021/ac301205z.
  6. Lachenmeier, D.W.; Humpfer, E.; Fang, F.; Schütz, B.; Dvortsak, P.; Sproll, C.; Spraul, M. NMR-spectroscopy for nontargeted screening and simultaneous quantification of health-relevant compounds in foods: The example of melamine. J. Agric. Food Chem. 2009, 57, 7194–7199. https://doi.org/10.1021/jf902038j
  7. Musio, B.; Todisco, S.; Antonicelli, M.; Garino, C.; Arlorio, M.; Mastrorilli, P.; Latronico, M.; Gallo, V. Non-Targeted NMR Method to Assess the Authenticity of Saffron and Trace the Agronomic Practices Applied for Its Production. Appl. Sci. 2022, 12, 2583. https://doi.org/10.3390/app12052583.
  8. Remane, D.; Wissenbach, D.K.; Peters, F.T. Recent advances of liquid chromatography-(tandem) mass spectrometry in clini-cal and forensic toxicology—An update. Clin. Biochem. 2016, 49, 1051–1071. https://doi.org/10.1016/j.clinbiochem.2016.07.010.
  9. Oberacher, H.; Arnhard, K. Compound identification in forensic toxicological analysis with untargeted LC-MS-based tech-niques. Bioanalysis 2015, 7, 2825–2840. https://doi.org/10.4155/bio.15.193.
  10. Vijlder, T. de; Valkenborg, D.; Lemière, F.; Romijn, E.P.; Laukens, K.; Cuyckens, F. A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation. Mass Spectrom. Rev. 2018, 37, 607–629. https://doi.org/10.1002/mas.21551.
  11. Milman, B.L.; Zhurkovich, I.K. The chemical space for non-target analysis. TrAC Trends Anal. Chem. 2017, 97, 179–187. https://doi.org/10.1016/j.trac.2017.09.013.
  12. Milman, B.L.; Zhurkovich, I.K. Mass spectral libraries: A statistical review of the visible use. TrAC Trends Anal. Chem. 2016, 80, 636–640. https://doi.org/10.1016/j.trac.2016.04.024.
  13. Bade, R.; Causanilles, A.; Emke, E.; Bijlsma, L.; Sancho, J.V.; Hernandez, F.; de Voogt, P. Facilitating high resolution mass spectrometry data processing for screening of environmental water samples: An evaluation of two deconvolution tools. Sci. Total Environ. 2016, 569, 434–441. https://doi.org/10.1016/j.scitotenv.2016.06.162.
  14. Bader, T.; Schulz, W.; Kümmerer, K.; Winzenbacher, R. LC-HRMS Data Processing Strategy for Reliable Sample Comparison Exemplified by the Assessment of Water Treatment Processes. Anal. Chem. 2017, 89, 13219–13226. https://doi.org/10.1021/acs.analchem.7b03037.
  15. 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. 2017, 51, 11505–11512. https://doi.org/10.1021/acs.est.7b02184.
  16. Schymanski, E.L.; Singer, H.P.; Slobodnik, J.; Ipolyi, I.M.; Oswald, P.; Krauss, M.; Schulze, T.; Haglund, P.; Letzel, T.; Grosse, S.; et al. Non-target screening with high-resolution mass spectrometry: Critical review using a collaborative trial on water analysis. Anal. Bioanal. Chem. 2015, 407, 6237–6255. https://doi.org/10.1007/s00216-015-8681-7.
  17. Fischer, K.; Fries, E.; Körner, W.; Schmalz, C.; Zwiener, C. New developments in the trace analysis of organic water pollu-tants. Appl. Microbiol. Biotechnol. 2012, 94, 11–28. https://doi.org/10.1007/s00253-012-3929-z.
  18. Burgard, D.A.; Banta-Green, C.; Field, J.A. Working upstream: How far can you go with sewage-based drug epidemiology? Environ. Sci. Technol. 2014, 48, 1362–1368. https://doi.org/10.1021/es4044648.
  19. Urbas, A.; Schoenberger, T.; Corbett, C.; Lippa, K.; Rudolphi, F.; Robien, W. NPS Data Hub: A web-based community driven analytical data repository for new psychoactive substances. Forensic Chem. 2018, 9, 76–81. https://doi.org/10.1016/j.forc.2018.05.003.
  20. Causanilles, A.; Kinyua, J.; Ruttkies, C.; van Nuijs, A.L.N.; Emke, E.; Covaci, A.; Voogt, P. de. Qualitative screening for new psychoactive substances in wastewater collected during a city festival using liquid chromatography coupled to high-resolution mass spectrometry. Chemosphere 2017, 184, 1186–1193. https://doi.org/10.1016/j.chemosphere.2017.06.101.
  21. Jorge, T.F.; Rodrigues, J.A.; Caldana, C.; Schmidt, R.; van Dongen, J.T.; Thomas-Oates, J.; António, C. Mass spectrome-try-based plant metabolomics: Metabolite responses to abiotic stress. Mass Spectrom. Rev. 2016, 35, 620–649. https://doi.org/10.1002/mas.21449.
  22. Bletsou, A.A.; Jeon, J.; Hollender, J.; Archontaki, E.; Thomaidis, N.S. Targeted and non-targeted liquid chromatography-mass spectrometric workflows for identification of transformation products of emerging pollutants in the aquatic environment. TrAC Trends Anal. Chem. 2015, 66, 32–44. https://doi.org/10.1016/j.trac.2014.11.009.
  23. Chaleckis, R.; Meister, I.; Zhang, P.; Wheelock, C.E. 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.
  24. Hernández, F.; Sancho, J.V.; Ibáñez, M.; Grimalt, S. Investigation of pesticide metabolites in food and water by LC-TOF-MS. TrAC Trends Anal. Chem. 2008, 27, 862–872. https://doi.org/10.1016/j.trac.2008.08.011.
  25. Pérez-Ortega, P.; Lara-Ortega, F.J.; Gilbert-López, B.; Moreno-González, D.; García-Reyes, J.F.; Molina-Díaz, A. Screening of Over 600 Pesticides, Veterinary Drugs, Food-Packaging Contaminants, Mycotoxins, and Other Chemicals in Food by Ul-tra-High Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (UHPLC-QTOFMS). Food Anal. Methods 2017, 10, 1216–1244.
  26. Kalogiouri, N.P.; Aalizadeh, R.; Dasenaki, M.E.; Thomaidis, N.S. Application of High Resolution Mass Spectrometric meth-ods coupled with chemometric techniques in olive oil authenticity studies—A review. Anal. Chim. Acta 2020, 1134, 150–173. https://doi.org/10.1016/j.aca.2020.07.029.
  27. Righetti, L.; Paglia, G.; Galaverna, G.; Dall Asta, C. Recent Advances and Future Challenges in Modified Mycotoxin Analysis: Why HRMS Has Become a Key Instrument in Food Contaminant Research. Toxins 2016, 8, 361. https://doi.org/10.3390/toxins8120361.
  28. Carlier, J.; Guitton, J.; Bévalot, F.; Fanton, L.; Gaillard, Y. The principal toxic glycosidic steroids in Cerbera manghas L. seeds: Identification of cerberin, neriifolin, tanghinin and deacetyltanghinin by UHPLC-HRMS/MS, quantification by UHPLC-PDA-MS. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2014, 962, 1–8. https://doi.org/10.1016/j.jchromb.2014.05.014.
  29. Pérez-Ortega, P.; Lara-Ortega, F.J.; Gilbert-López, B.; Moreno-González, D.; García-Reyes, J.F.; Molina-Díaz, A. Screening of Over 600 Pesticides, Veterinary Drugs, Food-Packaging Contaminants, Mycotoxins, and Other Chemicals in Food by Ul-tra-High Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (UHPLC-QTOFMS). Food Anal. Methods 2017, 10, 1216–1244.
  30. Kunzelmann, M.; Winter, M.; Åberg, M.; Hellenäs, K.-E.; Rosén, J. Non-targeted analysis of unexpected food contaminants using LC-HRMS. Anal. Bioanal. Chem. 2018, 410, 5593–5602. https://doi.org/10.1007/s00216-018-1028-4.
  31. Nagatomi, Y.; Yoshioka, T.; Yanagisawa, M.; Uyama, A.; Mochizuki, N. Simultaneous LC-MS/MS analysis of glyphosate, glufosinate, and their metabolic products in beer, barley tea, and their ingredients. Biosci. Biotechnol. Biochem. 2013, 77, 2218–2221. https://doi.org/10.1271/bbb.130433.
  32. Anderson, H.E.; Santos, I.C.; Hildenbrand, Z.L.; Schug, K.A. A review of the analytical methods used for beer ingredient and finished product analysis and quality control. Anal. Chim. Acta 2019, 1085, 1–20. https://doi.org/10.1016/j.aca.2019.07.061.
  33. Inoue, T.; Nagatomi, Y.; Suga, K.; Uyama, A.; Mochizuki, N. Fate of pesticides during beer brewing. J. Agric. Food Chem. 2011, 59, 3857–3868. https://doi.org/10.1021/jf104421q.
  34. Pinu, F. Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches. Fermentation 2018, 4, 92. https://doi.org/10.3390/fermentation4040092.
  35. Ruocco, S.; Perenzoni, D.; Angeli, A.; Stefanini, M.; Rühl, E.; Patz, C.-D.; Mattivi, F.; Rauhut, D.; Vrhovsek, U. Metabolite profiling of wines made from disease-tolerant varieties. Eur. Food Res. Technol. 2019, 245, 2039–2052. https://doi.org/10.1007/s00217-019-03314-z.
  36. Arbulu, M.; Sampedro, M.C.; Gómez-Caballero, A.; Goicolea, M.A.; Barrio, R.J. Untargeted metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry for non-volatile profiling of wines. Anal. Chim. Acta 2015, 858, 32–41. https://doi.org/10.1016/j.aca.2014.12.028.
  37. Arapitsas, P.; Ugliano, M.; Perenzoni, D.; Angeli, A.; Pangrazzi, P.; Mattivi, F. Wine metabolomics reveals new sulfonated products in bottled white wines, promoted by small amounts of oxygen. J. Chromatogr. A 2016, 1429, 155–165. https://doi.org/10.1016/j.chroma.2015.12.010.
  38. Díaz, R.; Gallart-Ayala, H.; Sancho, J.V.; Nuñez, O.; Zamora, T.; Martins, C.P.B.; Hernández, F.; Hernández-Cassou, S.; Saurina, J.; Checa, A. Told through the wine: A liquid chromatography-mass spectrometry interplatform comparison reveals the influence of the global approach on the final annotated metabolites in non-targeted metabolomics. J. Chromatogr. A 2016, 1433, 90–97. https://doi.org/10.1016/j.chroma.2016.01.010.
  39. Li, X.; Zhang, X.; Ye, L.; Kang, Z.; Jia, D.; Yang, L.; Zhang, B. LC-MS-Based Metabolomic Approach Revealed the Signifi-cantly Different Metabolic Profiles of Five Commercial Truffle Species. Front. Microbiol. 2019, 10, 2227. https://doi.org/10.3389/fmicb.2019.02227.
  40. Spínola, V.; Pinto, J.; Castilho, P.C. Identification and quantification of phenolic compounds of selected fruits from Madeira Island by HPLC-DAD-ESI-MS(n) and screening for their antioxidant activity. Food Chem. 2015, 173, 14–30. https://doi.org/10.1016/j.foodchem.2014.09.163.
  41. Kolniak-Ostek, J. Identification and quantification of polyphenolic compounds in ten pear cultivars by UPLC-PDA-Q/TOF-MS. J. Food Compos. Anal. 2016, 49, 65–77. https://doi.org/10.1016/j.jfca.2016.04.004.
  42. Simirgiotis, M.J.; Bórquez, J.; Schmeda-Hirschmann, G. Antioxidant capacity, polyphenolic content and tandem HPLC-DAD-ESI/MS profiling of phenolic compounds from the South American berries Luma apiculata and L. chequén. Food Chem. 2013, 139, 289–299. https://doi.org/10.1016/j.foodchem.2013.01.089.
  43. Bastos, K.X.; Dias, C.N.; Nascimento, Y.M.; Da Silva, M.S.; Langassner, S.M.Z.; Wessjohann, L.A.; Tavares, J.F. Identification of Phenolic Compounds from Hancornia speciosa (Apocynaceae) Leaves by UHPLC Orbitrap-HRMS. Molecules 2017, 22, 143. https://doi.org/10.3390/molecules22010143.
  44. Bertin, R.L.; Gonzaga, L.V.; Da Borges, G.S.C.; Azevedo, M.S.; Maltez, H.F.; Heller, M.; Micke, G.A.; Tavares, L.B.B.; Fett, R. Nutrient composition and, identification/quantification of major phenolic compounds in Sarcocornia ambigua (Amaran-thaceae) using HPLC–ESI-MS/MS. Food Res. Int. 2014, 55, 404–411. https://doi.org/10.1016/j.foodres.2013.11.036.
  45. Lin, L.-Z.; Sun, J.; Chen, P.; Monagas, M.J.; Harnly, J.M. UHPLC-PDA-ESI/HRMSn profiling method to identify and quantify oligomeric proanthocyanidins in plant products. J. Agric. Food Chem. 2014, 62, 9387–9400. https://doi.org/10.1021/jf501011y.
  46. Abu-Reidah, I.M.; Ali-Shtayeh, M.S.; Jamous, R.M.; Arráez-Román, D.; Segura-Carretero, A. HPLC-DAD-ESI-MS/MS screening of bioactive components from Rhus coriaria L. (Sumac) fruits. Food Chem. 2015, 166, 179–191. https://doi.org/10.1016/j.foodchem.2014.06.011.
  47. Regazzoni, L.; Arlandini, E.; Garzon, D.; Santagati, N.A.; Beretta, G.; Maffei Facino, R. A rapid profiling of gallotannins and flavonoids of the aqueous extract of Rhus coriaria L. by flow injection analysis with high-resolution mass spectrometry as-sisted with database searching. J. Pharm. Biomed. Anal. 2013, 72, 202–207. https://doi.org/10.1016/j.jpba.2012.08.017.
  48. El Sayed, A.M.; Ezzat, S.M.; El Naggar, M.M.; El Hawary, S.S. In vivo diabetic wound healing effect and HPLC–DAD–ESI–MS/MS profiling of the methanol extracts of eight Aloe species. Rev. Bras. Farmacogn. 2016, 26, 352–362. https://doi.org/10.1016/j.bjp.2016.01.009.
  49. Meng, X.; Bai, H.; Guo, T.; Niu, Z.; Ma, Q. Broad screening of illicit ingredients in cosmetics using ultra-high-performance liquid chromatography-hybrid quadrupole-Orbitrap mass spectrometry with customized accurate-mass database and mass spectral library. J. Chromatogr. A 2017, 1528, 61–74. https://doi.org/10.1016/j.chroma.2017.11.004.
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