Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2022 May 24;43(18-19):1814–1821. doi: 10.1002/elps.202200066

Profiling of polar ionogenic metabolites in Polish wines by capillary electrophoresis‐mass spectrometry

Marlien van Mever 1,✉, Magdalena Fabjanowicz 2, Maricruz Mamani‐Huanca 3, Ángeles López‐Gonzálvez 3, Justyna Płotka‐Wasylka 4, Rawi Ramautar 1
PMCID: PMC9790660  PMID: 35560354

Abstract

The composition of wine is determined by a complex interaction between environmental factors, genetic factors (i.e., grape varieties), and winemaking practices (including technology and storage). Metabolomics using NMR spectroscopy, GC‐MS, and/or LC‐MS has shown to be a useful approach for assessing the origin, authenticity, and quality of various wines. Nonetheless, the use of additional analytical techniques with complementary separation mechanisms may aid in the deeper understanding of wine's metabolic processes. In this study, we demonstrate that CE‐MS is a very suitable approach for the efficient profiling of polar ionogenic metabolites in wines. Without using any sample preparation or derivatization, wine was analyzed using a 10‐min CE‐MS workflow with interday RSD values for 31 polar and charged metabolites below 3.8% and 23% for migration times and peak areas, respectively. The utility of this workflow for the global profiling of polar ionogenic metabolites in wine was evaluated by analyzing different cool‐climate Polish wine samples.

Keywords: capillary electrophoresis, mass spectrometry, metabolomics, Polish wines

Abbreviations

MVDA

multivariate data analysis

QC

quality control

SIL

stable‐isotope‐labeled

UVDA

univariate data analysis

Grapevine (Vitis vinifera) is one of the most widely cultivated fruit crops in the world, which is used to produce juice, dried fruit, and wine [1]. Wine is characterized by a complex matrix in which compounds of distinct structures and belonging to several classes can be found, such as organic acids, polyphenols, vitamins, tannins, anthocyanins, amino acids, and biogenic amines. Metabolomics is a powerful tool providing a holistic view of the unique chemical composition of small molecules (≤1500 Da) in (biological) samples resulting from metabolic processes. This approach has gained momentum for the evaluation of food quality, toxicology, and processing over the past decades [2, 3], and already has a wide application in viticulture in order to evaluate the quality, authenticity [4], factors affecting the sensory characteristic of wine, and terroir effect [5].

In Poland, the most commonly cultivated grapes are hybrid grapevine species that resulted from the crossing of European grapevines (Vitis vinifera) with North American grapevines (such as Vitis rupestis, Vitis riparia or Vitis abrusca) or Asian grapevines (such as Vitis amurensis). Crossing two or even more species of Vitis resulted in hybrid species of so‐called cold‐climate wines, which are resistant to temperatures below –30°C and also less sensitive to fungal diseases. Specific characteristic features of Polish wines include a higher acidity and lower sugar content as compared to wines from warmer regions [6].

So far, various analytical tools have been developed for metabolomics analysis of grapes and wines [7], in particular GC‐MS, LC‐MS, and NMR have been considered for this purpose. CE‐MS is an analytical approach that offers excellent selectivity for resolving a wide range of polar and charged metabolites as compared to reversed‐phase LC or hydrophilic interaction chromatography (HILIC) [8, 9]. The latter often suffers from a relatively poor retention time reproducibility, complex separation mechanisms, and long equilibration times of columns. The potential of CE‐MS for the analysis of polar ionogenic metabolites in wine has only been reported in a limited number of studies so far [10, 11, 12, 13]. Here, we propose a CE‐MS‐based analytical workflow for the untargeted profiling of polar and charged metabolites in wine and show the utility of this approach by comparative metabolic profiling of cool climate wines from Poland.

For the CE‐MS methodology, we refer to our procedures described previously [14]. Briefly, CE‐MS experiments were carried out on a 7100 CE system hyphenated with a 6230 TOF, both from Agilent Technologies. CE‐MS coupling was realized via a co‐axial sheath‐liquid ESI interface equipped with a triple‐tube sprayer, and sheath‐liquid was of a mixture of water and isopropanol (50:50, v/v) with 0.03% acetic acid, which was delivered at 3 µl/min. As background electrolyte (BGE), 10% (v/v) acetic acid was used. Stable‐isotope‐labeled (SIL) histamine (5 µg/ml) was used as internal standard. CE‐MS experimental data were acquired in positive ionization mode, between 50 and 1000 m/z with an acquisition rate of 1.5 spectra/s. The following MS settings were used; nebulizer gas: 0 psi, sheath gas (nitrogen) flow rate: 11 L/min, sheath gas temperature: 100°C, ESI capillary voltage: 5500 V, fragmentor voltage: 100 V, skimmer voltage: 50 V. When in‐source fragmentation was required for identification purposes, a fragmentor voltage of 200 V was used. Data treatment and analysis was performed as described previously [15]. Wine samples were purchased from Polish wine stores and included 10 red and 10 white wine samples (see Table S1). Wine samples were ultrasonicated and filtered by a 0.45 µM cellulose filter and stored under dark conditions at room temperature (21°C).

Untargeted metabolomics of wine is a powerful tool for the assessment of wine authenticity and quality. However, before such an approach can be used for this purpose, it is important to assess the performance of CE‐MS first for the analysis of target compounds. Only with acceptable performance metrics (i.e., for repeatability: area RSD% < 25%, migration time RSD% < 10%, linear response function and representable LOD values) obtained for targeted analysis the CE‐MS method can be used for untargeted profiling of metabolites in wine. The analytical performance was evaluated using a metabolite mixture composed of 32 metabolites by considering aspects such as repeatability, response function, and LODs (Tables S2 and S3).

A linear response (and with R 2 > 0.981) for the target metabolites in the concentration range from 0.05 to 10 µM was obtained with LODs ranging from 0.002 to 0.218 µg/ml (Table S4). In comparison with previous studies reporting the use of CE‐MS for analysis of metabolites in wine, LODs were at least 2 to 12 times lower for biogenic amines (except for cadaverine) [10, 13]. This improvement is probably due to the use of different CE‐MS separation conditions, such as BGE composition and a lower sheath‐liquid flow rate as no nebulizer gas was applied [9]. Repeatability of the CE‐MS method for direct profiling of metabolites was assessed based on the consecutive analyses of wine samples spiked with metabolite standards (2.5 µg/ml). Intra‐ and interday RSD values for peak areas of all analytes were below 17% (n = 5) and 23% (n = 15) (except for cadaverine), respectively (Table S4). Migration time repeatability was assessed without internal standard correction, and was below 1.7% and 2.5% for intra‐ and interday analysis, respectively. Given that wine samples were directly analyzed by CE‐MS, the obtained figures of merits for repeatability could be considered acceptable for comparative metabolic profiling studies.

Figure 1 illustrates the applicability of CE‐MS for the direct analysis of biogenic amines and amino acids in pooled red and white wines, respectively. These compound classes could be analyzed within 10 min by using an additional pressure of 40 mbar at the CE inlet during separation, thereby still maintaining a partial separation for the isobaric isomers isoleucine and leucine (R = 0.5). In case a better separation would be required, then the use of an additional pressure could be omitted and/or a longer separation capillary could be used.

FIGURE 1.

FIGURE 1

Open in a new tab

Extracted‐ion electropherograms obtained by CE‐MS for the targeted analysis of (top) pooled white wine and (bottom) pooled red wine. Separation conditions: BGE, 10% acetic acid; sample injection volume 27.4 nl; separation voltage: 30 kV

Matrix effects were assessed using the standard addition method [16]. SIL standards were spiked into pooled wine at concentrations ranging from 0.05 to 25 µg/ml, and resulting response curves were compared to the accompanying response curves obtained when the SIL standards were dissolved in BGE (Figure S5). The slopes were different for all compounds (up to 45% lower for wine samples), indicating that all metabolites experienced a matrix effect. Therefore, for quantitative studies, calibration curves constructed in wine or a SIL internal standard for each compound needs to be used to account for matrix effects.

Next, the CE‐MS workflow was used for untargeted profiling of polar ionogenic metabolites in two groups of samples, that is, red wine (n = 10) and white wine (n = 10). Quality control (QC) samples (n = 5) were prepared by pooling all the wine samples and were analyzed periodically along the sequence to evaluate the performance of the method. A total of 94 features were detected (Table 1), after removing noise signals, duplicates, adducts and fragments. Data quality was assessed by clustering QC samples measurements in an unsupervised PCA‐X model. The model showed a R 2 = 0.735, which indicated the high quality of the analysis (Figure 2) and the clustering trends of the groups were observed.

TABLE 1.

Tentative identification of metabolites observed in pooled Polish wine samples by CE‐MS after data treatment

Compound name m/z μeff in wines Formula Level ID In‐source fragments at 200 V
Glycine 76.0396 1213.0 C2H5NO2 L1
Alanine 90.0549 1079.5 C3H7NO2 L1
Serine 106.0492 828.2 C3H7NO3 L1 60.0450, 88.0393
Proline 116.0704 586.1 C5H9NO2 L1
Valine 118.0855 895.2 C5H11NO2 L1
Betaine 118.0856 522.9 C5H11NO2 L1
Threonine 120.0650 747.6 C4H9NO3 L1 74.0614, 102.0561
Pipecolic acid 130.0855 810.2 C6H11NO2 L3
Isoleucine/leucine 132.0998 872.5 C6H13NO2 L1 69.0826, 86.0982
Asparagine 133.0595 740.8 C4H8N2O3 L1 70.0317, 74.0241, 116.0366
Aspartic acid 134.0441 536.9 C4H7NO4 L1 70.0302, 74.0254, 88.0409
Glutamine 147.0759 730.9 C5H10N2O3 L3
Lysine 147.1121 1907.9 C6H14N2O2 L1 84.0826, 102.0947, 130.0877
Glutamic acid 148.0594 694.5 C5H9NO4 L1 84.0459, 102.0556, 130.0506
Methionine 150.0581 751.0 C5H11NO2S L1
Histidine 156.0761 1778.9 C6H9N3O2 L1 83.0619,110.0725
O‐Acetylhomoserine/aminoadipic acid 162.0755 568.5 C6H11NO4 L3
Phenylalanine 166.0854 691.2 C9H11NO2 L1 120.0812, 131.0429, 149.0636
Arginine 175.1187 1792.8 C6H14N4O2 L1 60.0575, 70.0669, 116.0722, 158.0944
Citrulline 176.1003 704.3 C6H13N3O3 L3
Tyrosine 182.0805 665.6 C9H11NO3 L1 123.0422, 136.0737, 147.0419, 165.0525
Cytidine 244.0928 1272.1 C9H13N3O5 L1
Nicotianamine 304.1494 444.8 C12H21N3O6 L3
Biogenic amines
Ethanolamine 62.0608 2486.7 C2H7NO L1
Putrescine 89.1073 3690.6 C4H12N2 L1
Beta‐alanine 90.0551 1968.4 C3H7NO2 L1
GABA 104.0701 1976.8 C4H9NO2 L1
Tyramine 138.0915 1652.5 C8H11NO L1 91.0536, 103.0531, 105.0442, 121.065
Amino acids and derivatives
Pyroglutamine/Dihydrothymine 129.0653 1247.2 C5H8N2O2 L3
4‐Hydroxyproline 132.0650 479.1 C5H9NO3 L1 68.0506, 86.0615, 114.0534
3‐Aminocaproic acid 132.1003 1820.9 C6H13NO2 L3
Cis‐4‐(Hydroxymethyl)‐2‐pyrrolidinecarboxylate 146.0804 734.1 C6H11NO3 L2 82.0664,100.0765,128.0713
8/3/2‐Aminooctanoic acid 160.1327 1476.4 C8H17NO2 L3
N‐Acetyl‐2,4‐diaminobutanoate/Ala‐Ala 161.0946 1338.9 C6H12N2O3 L3
Methionine sulfoxide/ethiin 166.0544 534.1 C5H11NO3S L3
N 2‐Acetyl‐ornithine/theanine 175.1068 1257.1 C7H14N2O3 L2 129.0657
Ethyl glutamate/2‐aminoheptanedioic acid/hydroxyvalerylglycine 176.0917 1403.4 C7H13NO4 L3
Amino acids and derivatives
Ethyl glutamate/2‐aminoheptanedioic acid/hydroxyvalerylglycine 176.0925 691.2 C7H13NO4 L3
N‐Hydroxy‐phenylalanine/meta‐tyrosine 182.0809 595.0 C9H11NO3 L3
Homoarginine/targinine 189.1339 1678.3 C7H16N4O2 L3
4‐(Glutamylamino) butanoate/N 2‐succinyl‐ornithine/Asp‐Val 233.1133 1019.4 C9H16N2O5 L3
γ‐Glutamyl‐pipecolic acid/(2S,2′S)‐pyrosaccharopine 259.1280 308.8 C11H18N2O5 L3
Cyclic argininosuccinic acid derivative 1 273.1189 1338.9 C10H16N4O5 L2 70.0645
N 6‐(Octanoyl)lysine 273.2158 1124.1 C14H28N2O3 L3
N 2‐Fructopyranosylarginine 337.1700 1165.5 C12H24N4O7 L3
Peptides a
Proline betaine 144.1030 1772.0 C7H13NO2 L2 72.0822, 84.0820
Ala Ser 177.0860 1262.1 C6H12N2O4 L3
Pro‐Ala 187.1071 1282.2 C8H14N2O3 L3
Gly Leu 189.1219 1213.0 C8H16N2O3 L3
Ala‐Thr 191.1010 1208.2 C7H14N2O4 L3
Leu‐Ala 203.1380 1179.6 C9H18N2O3 L3 86.0978
Thr‐Ser 207.0940 1137.8 C7H14N2O5 L3
Valyl‐Betaine 217.1527 1142.4 C10H20N2O3 L3
Val Val 217.1528 1282.2 C10H20N2O3 L3
Thr‐Val/Ser‐Leu 219.1308 1133.2 C9H18N2O4 L3
Asp‐Ser 221.0800 3057.5 C7H12N2O6 L3
Gly‐Phe 223.1088 1137.8 C11H14N2O3 L1
Val‐Ile 231.1691 1106.1 C11H22N2O3 L3
Ile‐Val 231.1695 1106.1 C11H22N2O3 L3
Leu‐Thr 233.1470 1088.3 C10H20N2O4 L3 86.0982
Ile‐Ile 245.1844 1079.5 C12H24N2O3 L3 86.0982
Asp‐Ile/Glu‐Val 247.1253 978.0 C10H18N2O5 L3
Asp‐Ile/Glu‐Val 247.1266 978.0 C10H18N2O5 L3
Leu‐Lys 260.1954 1991.6 C12H25N3O3 L3
Glu‐Leu 261.1448 1011.0 C11H20N2O5 L3
Glu Lys 276.1532 1040.6 C11H21N3O5 L3
Val Gly Leu 288.1903 1023.6 C13H25N3O4 L3
Ile‐Arg 288.2028 1968.4 C12H25N5O3 L3
Gly Thr Leu 290.1701 1006.8 C12H23N3O5 L3
Asp Cys Gly 294.0712 2834.8 C9H15N3O6S L3
Leu Ala Val 302.2053 990.3 C14H27N3O4 L3 86.0979
Other compounds
Choline 104.1069 2206.0 C5H14NO L2 60.0816
Picolinic acid/nicotinic acid 124.0394 751.0 C6H5NO2 L3
Imidazoleacetic acid/thymine 127.0495 1646.1 C5H6N2O2 L3
Adenine 136.0621 1820.9 C5H5N5 L3
Hypoxanthine 137.0449 457.9 C5H4N4O L3
Other compounds
Trigonelline 138.0539 678.4 C7H7NO2 L3
Imidazolelactic acid 157.0602 1370.8 C6H8N2O3 L2 111.0568
3‐Dehydroxycarnitine 146.1171 1583.4 C7H15NO2 L3
Carnitine 162.1123 1546.9 C7H15NO3 L3
Ethyl N‐ethylanthranilate 194.1148 1408.9 C11H15NO2 L3
MTCA 231.1117 465.8 C13H14N2O2 L2 158.0957, 214.0859
Glycylprolylhydroxyproline 286.1382 846.5 C12H19N3O5 L3
Unknown compounds
108.0654 1092.7 L4
138.0520 659.3 L4
139.6059 2129.0 L4
158.1249 1608.2 L4
160.6280 1991.6 L4
231.1116 465.8 L4
250.1750 1930.4 L4
263.1118 2980.9 L4
274.2694 1083.9 L4
285.0767 3110.0 L4
345.0877 3006.1 L4
Open in a new tab

aContains multiple identification options.

Abbreviation: MTCA, (1xi,3xi)‐1,2,3,4‐tetrahydro‐1‐methyl‐beta‐carboline‐3‐carboxylic acid.

FIGURE 2.

FIGURE 2

Open in a new tab

SIMCA‐P software (Version 17, Umetrics, Sartorius Stedim Biotech) was used to perform multivariate analysis models. (A) Principal component analysis (PCA‐X) score plot with an explained variance R 2 = 0.735, using non‐normalized samples wines (gray color) and quality control (QC, blue circle). (B) Orthogonal partial least‐squares‐discriminant analysis (OPLS‐DA) analysis of variation between red wine and white wine samples (R 2 = 0.947, Q 2 = 0.782) and CV‐ANOVA p‐value = 1.06 × 10–03. (C) Plot corresponding to the cross‐validation for the OPLS‐DA model

The compounds that play the most significant role in the discrimination between wines of different types and origin are amino acids. Key amino acids include proline, which is the most abundant amino acid in grapes. Its level is determined by the grape variety and aromatic amino acid phenylalanine, whose level depends on grape variety and alcoholic fermentation, where it is used by bacteria, fungi, and yeast to produce a highly polar aromatic alcohol, phenethyl alcohol [17]. A supervised Orthogonal partial least‐squares‐discriminant analysis model was built to evaluate the metabolic differences between white and red wine (Figure 2). The model showed a high grade of discrimination between the groups of samples and good quality parameters (R 2 = 0.947, Q 2 = 0.782). The model was validated by CV‐ANOVA (p‐value = 1.06 × 10–03) and by the cross‐validation leaving 1/3 out approach, showing a prediction accuracy of 100% (Figure 2). In order to identify the features that were statistically significant by multivariate data analysis (MVDA) analysis, the confidence intervals of Jack‐Knife, correlation p(corr) > |0.5|, and variables importance in projection (VIP) > 1 were calculated. Additionally, the univariate data analysis (UVDA) statistical analysis was performed in order to obtain the statistical significance of each compound in the comparison of both groups. Metabolites with p < 0.05 were selected as significant metabolites using the Mann–Whitney U test; for correction of comparisons, the Benjamini–Hochberg method was applied to all p‐values to control the false discovery rate (FDR) at the q = 0.05 level. The overall statistical analysis revealed 45 metabolic features as significantly different between the groups (Table S6). The annotation of these features was performed by m/z search in the FooDB database and CMM, a search tool that integrates different databases (Kegg, HMDB, LipidMaps, METLIN, NPAtlas, KNApSAcK, MINE) and an in‐house library [18]. The annotation was carried out considering mass accuracy (20 ppm as maximum error as recommended [19]), electrophoretic mobility (<5% error to effective mobility database [9, 20]), isotopic pattern, and adduct formation (confidence level 3). This was considered as tentative annotation. Additionally, in order to increase this confidence level, in‐source fragmentation was performed (confidence level 2) [15], and when available, some metabolites were identified using commercial standards (confidence level 1). For unknown metabolites, only m/z was considered (confidence level 4).

Interestingly, cyclic argininosuccinic acid was found to be one of the metabolites responsible for the classification of the two sets of wine samples, expressed more in white wines. This compound is found in a dynamic equilibrium with argininosuccinic acid in its open form; at an acidic pH as is the case in wines, cyclic argininosuccinic acid predominates [21].

Overall, a CE‐MS workflow for the direct profiling of polar ionogenic metabolites in wine is proposed and a proof‐of‐principle study utilizing white and red cool‐climate wines originating from Poland revealed the potential of this approach for assessing wine authenticity and quality in follow‐up studies.

CONFLICT OF INTEREST

The authors have declared no conflict of interest.

Supporting information

Supporting information

Click here for additional data file. (654.4KB, docx)

ACKNOWLEDGMENTS

The authors of this work acknowledge the financial support of the Vidi grant scheme of the Netherlands Organization of Scientific Research (NWO Vidi 723.016.003). The authors of this work would like to gratefully acknowledge the support for their research provided by the Polish National Agency for Academic Exchange through the project no. PPI/PRO/2019/1/00009. M.M‐H would like to acknowledge the CEU‐ International Doctoral School (CEINDO) grant and the CEINDO‐SANTANDER research mobility grant.

van Mever M, Fabjanowicz M, Mamani‐Huanca M, López‐Gonzálvez Á, Płotka‐Wasylka J, Ramautar R. Profiling of polar ionogenic metabolites in Polish wines by capillary electrophoresis‐mass spectrometry capillary electrophoresis‐mass spectrometry. Electrophoresis. 2022;43:1814–1821. 10.1002/elps.202200066

Marlien van Mever and Magdalena Fabjanowicz are equally contributing first authors.

Justyna Płotka‐Wasylka and Rawi Ramautar are equally contributing last authors.

Color online: See article online to view Figures 1 and 2 in color.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

REFERENCES

  • 1. Torregrosa L, Vialet S, Adivèze A, Iocco‐Corena P, Thomas MR. Grapevine (Vitis vinifera L.). Methods Mol Biol. 2015;1224:177–94. [DOI] [PubMed] [Google Scholar]
  • 2. Utpott M, Rodrigues E, Rios ADO, Mercali GD, Flôres SH. Metabolomics: an analytical technique for food processing evaluation. Food Chem. 2022;366:130685. [DOI] [PubMed] [Google Scholar]
  • 3. Herrero M, Simó C, García‐Cañas V, Ibáñez E, Cifuentes A. Foodomics: MS‐based strategies in modern food science and nutrition. Mass Spectrom Rev. 2012;31(1):49–69. [DOI] [PubMed] [Google Scholar]
  • 4. Tzachristas A, Dasenaki M, Aalizadeh R, Thomaidis NS, Proestos C. LC‐MS based metabolomics for the authentication of selected Greek white wines. Microchem J. 2021;169:106543. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Alañón ME, Pérez‐Coello MS, Marina ML. Wine science in the metabolomics era. TrAC Trends Anal Chem. 2015;74:1–20. [Google Scholar]
  • 6. Tarko T, Duda‐Chodak A, Sroka P, Satora P, Jurasz Eż. Polish wines: characteristics of cool‐climate wines. J Food Compost Anal. 2010;23:463–8. [Google Scholar]
  • 7. Pinu F. Grape and wine metabolomics to develop new insights using untargeted and targeted approaches. Fermentation. 2018;4:92. [Google Scholar]
  • 8. Kuehnbaum NL, Kormendi A, Britz‐Mckibbin P. Multisegment injection‐capillary electrophoresis‐mass spectrometry: a high‐throughput platform for metabolomics with high data fidelity. Anal Chem. 2013;85:10664. [DOI] [PubMed] [Google Scholar]
  • 9. Drouin N, Pezzatti J, Gagnebin Y, González‐Ruiz V, Schappler J, Rudaz S. Effective mobility as a robust criterion for compound annotation and identification in metabolomics: Toward a mobility‐based library. Anal Chim Acta. 2018;1032:178–87. [DOI] [PubMed] [Google Scholar]
  • 10. Woźniakiewicz M, Woźniakiewicz A, Nowak PM, Kłodzińska E, Namieśnik J, Płotka‐Wasylka J. CE‐MS and GC‐MS as “green” and complementary methods for the analysis of biogenic amines in wine. Food Anal Methods. 2018;11:2614–27. [Google Scholar]
  • 11. Acunha T, Simó C, Ibáñez C, Gallardo A, Cifuentes A. Anionic metabolite profiling by capillary electrophoresis–mass spectrometry using a noncovalent polymeric coating. J Chromatogr A. 2016;1428:326–35. [DOI] [PubMed] [Google Scholar]
  • 12. Daniel D, Dos Santos VB, Vidal DTR, Do Lago CL. Determination of biogenic amines in beer and wine by capillary electrophoresis–tandem mass spectrometry. J Chromatogr A. 2015;1416:121–8. [DOI] [PubMed] [Google Scholar]
  • 13. Simó C, Moreno‐Arribas MV, Cifuentes A. Ion‐trap versus time‐of‐flight mass spectrometry coupled to capillary electrophoresis to analyze biogenic amines in wine. J Chromatogr A. 2008;1195:150–6. [DOI] [PubMed] [Google Scholar]
  • 14. van Mever M, Willacey CCW, Zhang W, Drouin N, Christina AE, Lindenburg PW, et al. Profiling acidic metabolites by capillary electrophoresis‐mass spectrometry in low numbers of mammalian cells using a novel chemical derivatization approach. Anal Sci Adv. 2022;3:3–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Mamani‐Huanca M, De La Fuente AG, Otero A, Gradillas A, Godzien J, Barbas C, et al. Enhancing confidence of metabolite annotation in capillary electrophoresis‐mass spectrometry untargeted metabolomics with relative migration time and in‐source fragmentation. J Chromatogr A. 2021;1635:461758. [DOI] [PubMed] [Google Scholar]
  • 16. Hewavitharana AK, Tan SK, Shaw PN. Strategies for the detection and elimination of matrix effects in quantitative LC‐MS analysis. LC GC. 2014;32:54. [Google Scholar]
  • 17. Gougeon L, Da Costa G, Guyon F, Richard T. 1H NMR metabolomics applied to Bordeaux red wines. Food Chem. 2019;301:125257. [DOI] [PubMed] [Google Scholar]
  • 18. Gil‐De‐La‐Fuente A, Godzien J, Saugar S, Garcia‐Carmona R, Badran H, Wishart DS, et al. CEU mass mediator 3.0: a metabolite annotation tool. J Proteome Res. 2019;18:797–802. [DOI] [PubMed] [Google Scholar]
  • 19. Zhu Z‐J, Schultz AW, Wang J, Johnson CH, Yannone SM, Patti GJ, et al. Liquid chromatography quadrupole time‐of‐flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat Protoc. 2013;8:451–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Drouin N, Van Mever M, Zhang W, Tobolkina E, Ferre S, Servais A‐C, et al. Capillary electrophoresis‐mass spectrometry at trial by metabo‐ring: effective electrophoretic mobility for reproducible and robust compound annotation. Anal Chem. 2020;92:14103–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Mamani‐Huanca M, Gradillas A, López‐Gonzálvez Á, Barbas C. Unraveling the cyclization of l ‐argininosuccinic acid in biological samples: a study via mass spectrometry and NMR spectroscopy. Anal Chem. 2020;92:12891–9. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting information

Click here for additional data file. (654.4KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Electrophoresis are provided here courtesy of Wiley

RESOURCES