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It can be seen from Fig. This HPLC-grade acetonitrile is therefore suited to high-sensitivity analysis with UV detection in the short-wavelength region. Moreover, organic solvents which have been processed for LCMS analysis have both UV-absorbent impurities and residual metals removed. This acts to prevent background noise specific to LCMS analysis.
When changing the organic solvent from acetonitrile to methanol, ghost peaks may be detected in gradient analysis due to the analytical conditions in the UV short wavelength range.
In this case, we recommend reconsidering the solvent grade. If the cause of the ghost peaks is unclear and causes problems in the analytical results, try the Ghost Trap DS , which removes impurities from organic solvents.
It can be seen that when acetonitrile and methanol are mixed with water in the same ratio, an acetonitrile mobile phase displays greater elution strength.
The nomogram in Fig. The separation selectivity of acetonitrile and methanol differ, but since selectivity depends on the properties of the dissolved compound, it is not the case that selectivity is always higher for one or the other.
In the separation of positional isomers, phenyl columns may be the most appropriate amongst columns for reverse-phase chromatography. Methanol and acetonitrile have different chemical properties. Methanol is a protic solvent, whereas acetonitrile is a non-protic solvent, so we know that their elution behavior will differ. Plants were grown in hydroponics with a nutrient solution adapted from Hoagland et al.
They were irrigated one to three times a week, depending on their nutrient solution uptake. Upon flowering, plants were harvested and frozen immediately with liquid nitrogen to stop any metabolic activity.
A schematic overview of the extraction procedure is given in Figure 1. Many untargeted metabolomics studies deal with analysis of leaves of different plants [ 4 ] and therefore, wheat leaves were chosen for this study. Freeze dried leaf samples were ground to a fine powder with a ball mill MM—Retsch, Haan, Germany under cooling with liquid nitrogen.
Native wheat samples were pooled and homogenized thoroughly. Immediately after this, extraction was carried out based on the extraction procedure described by Bueschl et al. Samples were vortexed for 10 s and then sonicated for 15 min. To test whether the extraction with non-acidified solvent would influence the number and type of artifact compounds, the above described extraction protocol was also applied without the addition of FA to the extraction solvent variant II.
Subsequently, they were measured as described below. As described by Bueschl et al. Instrument performance was monitored with a QC-mix containing 25 standard substances regularly injected within the measurement sequences. For data processing of the samples extracted in the additional experiment according to Bueschl et al. Briefly summarized, the MetExtract-software searched for pairs of corresponding 12 C- and 13 C-signals in each MS-scan. Different adducts e. This resulted in a list of plant-derived metabolic features.
For the detection of meth ox ylated artifact compounds, the same MetExtract II software was customized to recognize the specific isotopolog patterns of native and uniformly 13 C as well as native and D 3 -labeled compounds.
This custom-tailored version was used to process the respective samples of the variants I and II as well as the de novo experiment. Then, the detected artifact features were also merged into artifact compound groups, as described above.
The obtained list was manually verified by inspecting the raw data with Thermo Xcalibur software Version 4. Only artifact metabolites, which were present in all 5 replicates in at least one group of extracts e. If they were detected in the sample extracts of the additional experiment, they were considered false-positives of randomly co-eluting metabolites and removed from the data matrix.
Statistical data evaluation was carried out in the R environment [ 19 ] version 3. Any other feature pair also present in the feature groups were omitted. Moreover, only feature pairs detected in at least 4 out of 5 sample extracts of any experimental variant of vI or vII were used.
This data matrix is referred to as D c in the following. To distinguish artifact compounds already present in the biological samples from those that were solely formed during sample preparation with methanol or storage of extracts the samples of the de novo experiment were used. Any feature pair from D c that was detected only as the deuterium-methylated artifact M DM assigned as described above but not as the methylated artifact M M in at least 12 of the 16 de novo experiment samples was assigned to be a de novo artifact formed by meth ox ylation Figure 1.
All other artifacts were classified as being derived from a reaction with methanol. A feature pair was assigned to a specific experimental variant if it was detected in at least 4 of 5 replicates. The 2D-maps illustrating the detected artifacts used all feature pairs of D c regardless in which of the experimental variants a feature pair was detected.
To compare two experimental variants of sample extracts, all feature pairs from D c were used. For significance testing the two-sided t -test with a critical p -value of 0.
The results of this univariate comparison and significance testing were illustrated in form of volcano plots with logarithmic scales. The abundance histograms illustrate the distribution of peak areas of metabolite ions. They were calculated using the arithmetic mean values of EIC peak areas for the replicates of a particular experimental variant. The x-axis which illustrates peak area bins is shown as a logarithmic scale and the abundances were sorted into 30 different bins. Data pre-treatment comprised of replacing missing values by zero and auto-scaling of the abundances of the feature pairs data matrix D s [ 25 ] prior to calculating the PCA with the R-package ChemometricsWithR [ 26 ] pp.
The ellipses were calculated with the co-variance matrices of the first and second principal components PC1 and PC2 of the respective experimental variants using the R-package ellipses [ 27 ]. In this study two solvent mixtures commonly used in metabolomics experiments were used to evaluate the formation of methanol-derived meth ox ylation artifacts during sample extraction and storage of the generated extracts. Since methylated metabolites are frequently found as natural constituents of biological samples, this type of artifact is difficult to recognize by common untargeted metabolomics approaches.
Here, the use of deuterated methanol and acidified deuterated methanol for extraction, subsequent LC-HRMS measurement and automated data processing by MetExtract II enabled for the first time the global unbiased assessment of artifacts which can be attributed to meth ox ylations of natural metabolites by the solvent methanol. Both primary and secondary metabolites of different chemical compound classes such as small carboxylic acids, fatty acids, phenols, carotenoids and pyrrols were among the putatively affected metabolites.
Together with more than half of those artifacts having been formed by reaction with methanol de novo during extraction or storage and the fact that artifact abundances showed distributions similar to those of all other plant metabolites, our study demonstrates that great care must be taken when it comes to annotation and biological interpretation of methylated compounds.
The large scale tentative identification of the methanol-derived artifacts was complicated by multiple putative precursor candidates and needs more detailed studies in the future. We hope however that the provided list of all detected artifact features can still be of general help for the reliable evaluation of other studies in the field of plant metabolomics.
The following are available online at www. National Center for Biotechnology Information , U. Journal List Metabolites v. Published online Dec Author information Article notes Copyright and License information Disclaimer. Received Oct 31; Accepted Dec This article has been cited by other articles in PMC. Associated Data Supplementary Materials metabolitess Abstract Many metabolomics studies use mixtures of acidified methanol and water for sample extraction.
Keywords: untargeted metabolomics, stable isotopic labeling SIL , acidification, sample storage, plant metabolomics. Introduction Typical metabolomics studies comprise of several steps such as cultivation, harvest, quenching of sample material e. Results 2. Data Structure and Overview of Results This study aimed to evaluate the extent of solvent artifact formation during extraction with aqueous methanol MeOH and subsequent short-term storage.
Open in a separate window. Figure 1. Figure 2. Figure 3. Intensities of Solvent Artifacts The detected artifact compound abundances were then subjected to a univariate significance testing using the immediate measurement 0 dpe and measurement after one week 7 dpe to investigate the extent of the change in their intensity during storage of extracts Figure 4. Figure 4.
Table 1 Overview of all detected artifacts, illustrated by the most abundant feature per artifact compound. Discussion 3. Discussion of General Results In untargeted metabolomics, the majority of the detected metabolites are unknown prior to analysis and most of them also remain unknown or only annotated after data evaluation [ 13 ].
Short Term Storage of Sample Extracts Especially large metabolomics studies with many experimental variants and biological or technical replicates and QC samples might require long measurement sequences. Materials and Methods 4. Sample Preparation and Extraction of Plant Material A schematic overview of the extraction procedure is given in Figure 1. Statistical Data Evaluation Statistical data evaluation was carried out in the R environment [ 19 ] version 3.
Selection of De novo Artifact Compounds To distinguish artifact compounds already present in the biological samples from those that were solely formed during sample preparation with methanol or storage of extracts the samples of the de novo experiment were used.
Venn Diagrams A feature pair was assigned to a specific experimental variant if it was detected in at least 4 of 5 replicates. Feature Map The 2D-maps illustrating the detected artifacts used all feature pairs of D c regardless in which of the experimental variants a feature pair was detected. Univariate Comparison between Metabolite Abundances of Two Variants To compare two experimental variants of sample extracts, all feature pairs from D c were used.
Abundance Histograms The abundance histograms illustrate the distribution of peak areas of metabolite ions. Conclusions In this study two solvent mixtures commonly used in metabolomics experiments were used to evaluate the formation of methanol-derived meth ox ylation artifacts during sample extraction and storage of the generated extracts.
Supplementary Materials The following are available online at www. Click here for additional data file. Author Contributions C. Conflicts of Interest The authors declare no conflict of interest. References 1. Mushtaq M. Extraction for metabolomics: Access to the metabolome. DeHaven C. Software techniques for enabling high-throughput analysis of metabolomic datasets. In: Roessner U. InTech; Rijeka, Croatia: Villas-Boas S. Metabolome Analysis: An Introduction. Doppler M.
Stable isotope-assisted evaluation of different extraction solvents for untargeted metabolomics of plants. Maltese F. Solvent derived artifacts in natural products chemistry. Minor withanolides from Physalis philadelphica : Structures, quinone reductase induction activities and liquid chromatography lc -ms-ms investigation as artifacts.
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