Background: There is a continuous demand to create new, superior sensory food experiences. In the food industry, yeast-derived flavor products (YPs) are often used as ingredients in foods to create new aromas and taste qualities that are appreciated by consumers. Methods: Chicken bouillon samples containing diverse YPs were chemically and sensorially characterized using statistical multivariate analyses. The sensory evaluation was performed using quantitative descriptive analysis (QDA) by trained panelists. Thirty-four sensory attributes were scored, including odor, flavor, mouthfeel, aftertaste and afterfeel. Untargeted metabolomic profiles were obtained using stir bar sorptive extraction (SBSE) coupled to GC-MS, RPLC-MS and targeted HILIC-MS. Results: In total, 261 volatiles were detected using GC-MS, from chemical groups of predominantly aldehydes, esters, pyrazines and ketones. Random Forest (RF) modeling revealed volatiles associated with roast odor (2-ethyl-5-methyl pyrazine, 2,3,5-trimethyl-6-isopentyl pyrazine) and chicken odor (2,4-nonadienal, 2,4-decadienal, 2-acetyl furan), which could be predicted by our combined model with R2 > 0.5. In total, 2305 non-volatiles were detected for RPLC-MS and 34 for targeted HILIC-MS, where fructose-isoleucine and cyclo-leucine-proline were found to correlate with roast flavor and odor. Furthermore, a list of metabolites (glutamate, monophosphates, methionyl-leucine) was linked to umami-related flavor. This study describes a straightforward data-driven approach for studying foods with added YPs to identify flavor-impacting correlations between molecular composition and sensory perception. It also highlights limitations and preconditions for good prediction models. Overall, this study emphasizes a matrix-based approach for the prediction of food taste, which can be used to analyze foods for targeted flavor design or quality control.

Metabolomics based on hydrophilic interaction liquid chromatography (HILIC) coupled with mass spectrometry (MS) is a powerful tool for polar metabolite identification and quantification to further contribute to biomarker discovery and disease mechanism elucidation. However, matrix effect (ME), which may lead to altered ionization efficiency due to co-eluting compounds, is a significant challenge during biological analysis. Therefore, ME evaluation plays a crucial role during method development. Two approaches to evaluate ME are using stable isotope labelled-internal standards (SIL-IS) and post-column infusion (PCI) of standards. In this study, we developed an untargeted HILIC-MS method by applying four PCI standards for ME evaluation. We found PCI is a compelling approach for ME assessment compared to SIL-IS method due to its advantage in untargeted analysis. Through the ME evaluation and chromatographic performance comparison of 18 SIL standards across three columns and three different mobile phase pH conditions, our findings revealed that the BEH-Z-HILIC column operated at pH 4 with 10 mM ammonium formate exhibited minimal ME and superior performance. The method showed exceptional linearity (R-2 > 0.98), reliable repeatability (RSD < 15 %), good inter-day precision (RSD < 30 %), and acceptable recovery (>75 %) for all SIL standards. Absolute matrix effect (AME) and relative matrix effect (RME) assessment in three plasma donors revealed a high consistency between PCI and SIL-IS approaches. Finally, this method coupled with the PCI approach was applied to 40 plasma samples. Fifty endogenous compounds were detected and their AME and RME were evaluated. Results showed that many compounds experienced severe ion suppression, though their ME variation between 40 samples is low. In conclusion, PCI method is a robust alternative for monitoring ME and evaluating ME of endogenous compounds during untargeted method optimization and biological analysis.

Matrix effect is a well-known issue affecting accuracy and repeatability in metabolomics studies using liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). Post-column infusion of standards (PCIS) is a promising strategy to monitor and correct matrix effect but has been rarely reported in untargeted metabolomics. The major challenges lie in selecting appropriate PCISs and identifying the most suitable PCIS to correct the matrix effect experienced by each feature. In this study, we aim to present a method for selecting suitable PCISs for matrix effect compensation based on the artificial matrix effect (MEart) created by post-column infusion of compounds that disrupt the ESI process. Our hypothesis is that the suitable PCIS for a given analyte can be identified by comparing the PCISs' ability in MEart compensation. We evaluated this approach using 19 stable-isotopically labeled (SIL) standards spiked in plasma, urine, and feces. PCISs selected based on MEart were compared to those selected by biological matrix effect (MEbio), with 17 out of 19 SIL standards (89 %) showing consistent PCIS selection, demonstrating the effectiveness of MEart in identifying suitable PCISs. Applying MEart-selected PCISs to correct for the MEbio resulted in improved MEbio for most of the SILs affected by matrix effect and maintained MEbio for those experiencing no matrix effect. We demonstrated the efficacy of MEart in selecting suitable PCISs for MEbio correction within an LC-PCIS-MS method. Importantly, since MEart can be assessed for any detected feature, its application holds great potential for identifying suitable PCISs for matrix effect correction in untargeted metabolomics.

Polar metabolites play essential roles in inflammation, immune regulation, and metabolism, and have emerged as important biomarkers in clinical research. However, accurate absolute quantification of these metabolites in complex biological matrices using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) remains challenging, due to matrix effects that compromise measurement accuracy. While (stable isotope-labeled-) internal standards ((SIL-)IS) are commonly used to correct these effects, their limited availability and high cost can hinder their applicability. In this study, we present a robust approach using HILIC-MS coupled with post-column infusion of standards (PCIS) to enable absolute quantification of polar metabolites in plasma, using calibration curves prepared in neat solution without using SIL standards. First, the optimal PCIS for each metabolite was identified based on absolute matrix effect (AME) values, and method performance was systematically evaluated in terms of linearity, precision, accuracy, and matrix effects. Results showed that the PCIS approach consistently achieved comparable results to (SIL-)IS correction for analytes with SIL standards, and superior performance for analytes without available SIL standards. Application to a study cohort demonstrated consistency between this HILIC-PCIS-MS method and Nuclear Magnetic Resonance (NMR) quantification. Validation using NIST SRM 1950 plasma confirmed high quantification accuracy (80 % - 120 %) for most metabolites using a matrix-free calibration curve combined with PCIS in HILIC-MS. In summary, PCIS offers a promising and cost-effective alternative to (SIL-)IS correction, particularly benefiting metabolites lacking SIL standards, and provides a strategy for accurately correcting matrix effects and advancing quantitative metabolomics in complex biological matrices.

The matrix effect limits the accuracy of quantitation of the otherwise popular metabolomics technique liquid chromatography coupled to mass spectrometry (LC-MS). The gold standard to correct for this phenomenon, whereby compounds coeluting with the analyte of interest cause ionization enhancement or suppression, is to quantify an analyte based on the peak area ratio with an isotopologue added to the sample as an internal standard. However, these stable isotopes are expensive and sometimes unavailable. Here, we describe an alternative approach: matrix effect correction and quantifying analytes using a signal ratio with a postcolumn infused standard (PCIS). Using an LC-MS/MS method for eight endocannabinoids and related metabolites in plasma, we provide strategies to select, optimize, and evaluate PCIS candidates. Based on seven characteristics, the structural endocannabinoid analogue arachidonoyl-2 '-fluoroethylamide was selected as a PCIS. Three methods to evaluate the PCIS correction vs no correction showed that PCIS correction improved values for the matrix effect, precision, and dilutional linearity of at least six of the analytes to within acceptable ranges. PCIS correction also resulted in parallelization of calibration curves in plasma and neat solution, for six of eight analytes even with higher accuracy than peak area ratio correction with their stable isotope labeled internal standard, i.e., the gold standard. This enables quantification based on neat solutions, which is a significant step toward absolute quantification. We conclude that PCIS has great, but so far underappreciated, potential in accurate LC-MS quantification.

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

Postcolumn infusion has been widely used to study the matrix effect of analytical methods based on liquid chromatography coupled to mass spectrometry (LC-MS). Nevertheless, this methodology is usually only applied during a method development or validation. With this application note, we aim to demonstrate that the continuous use of postcolumn infusion can be also a very useful tool to monitor the quality of LC-MS analyses and easily detect flaws in the analytical method performance. Here we propose a protocol that can be transferred to other LC-MS platforms, and we show some real situations in bioanalysis in which postcolumn infusion proved to be extremely helpful in, for example, the evaluation of a sample treatment or the detection of unexpected sources of the matrix effect.

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having “garlic-like” and “onion-like” attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix.

Polar hydrophilic metabolites have been identified as important actors in many biochemical pathways. Despite continuous improvement and refinement of hydrophilic interaction liquid chromatography (HILIC) platforms, its application in global polar metabolomics has been underutilized. In this study, we aimed to systematically evaluate polar stationary phases for untargeted metabolomics by using HILIC columns (neutral and zwitterionic) that have been exploited widely in targeted approaches. To do so, high-resolution mass spectrometry was applied to thoroughly investigate selectivity, repeatability and matrix effect at three pH conditions for 9 classes of polar compounds using 54 authentic standards and plasma matrix. The column performance for utilization in untargeted metabolomics was assessed using plasma samples with diverse phenotypes. Our results indicate that the ZIC-c HILIC column operated at neutral pH exhibited several advantages, including superior performance for different classes of compounds, better isomer separation, repeatability and high metabolic coverage. Regardless of the column type, the retention of inorganic ions in plasma leads to extensive adduct formation and co-elution with analytes, which results in ion-suppression as part of the overall plasma matrix effect. In ZIC-c HILIC, the sodium chloride ion effect was particularly observed for amino acids and amine classes. Successful performance of HILIC for separation of plasma samples with different phenotypes highlights this mode of separation as a valuable approach in global profiling of plasma sample and discovering the metabolic changes associated with health and disease.

The unambiguous identification of unknown compounds is of utmost importance in the field of metabolomics. However, current identification workflows often suffer from error-sensitive methodologies, which may lead to incorrect structure annotations of small molecules. Therefore, we have developed a comprehensive identification workflow including two highly complementary techniques, i.e. liquid chromatography (LC) combined with mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), and used it to identify five taste-related retention time and m/z features in soy sauce. An off-line directed two-dimensional separation was performed in order to purify the features prior to the identification. Fractions collected during the first dimension separation (reversed phase low pH) were evaluated for the presence of remaining impurities next to the features of interest. Based on the separation between the feature and impurities, the most orthogonal second dimension chromatography (hydrophilic interaction chromatography or reversed phase high pH) was selected for further purification. Unknown compounds down to tens of micromolar concentrations were tentatively annotated by MS and structurally confirmed by MS and NMR. The mass (0.4-4.2 mu g) and purity of the isolated compounds were sufficient for the acquisition of one and two-dimensional NMR spectra. The use of a directed two-dimensional chromatography allowed for a fractionation that was tailored to each feature and remaining impurities. This makes the fractionation more widely applicable to different sample matrices than one-dimensional or fixed two-dimensional chromatography. Five proline-based 2,5-diketopiperazines were successfully identified in soy sauce. These cyclic dipeptides might contribute to taste by giving a bitter flavour or indirectly enhancing umami flavour. (C) 2020 The Author(s). Published by Elsevier B.V.

Gut microbiota and their metabolic products are increasingly being recognized as important modulators of human health. The fecal metabolome provides a functional readout of the interactions between human metabolism and the gut microbiota in health and disease. Due to the high complexity of the fecal matrix, sample preparation often introduces technical variation, which must be minimized to accurately detect and quantify gut bacterial metabolites. Here, we tested six different representative extraction methods (single-phase and liquid–liquid extractions) and compared differences due to fecal amount, extraction solvent type and solvent pH. Our results indicate that a minimum fecal (wet) amount of 0.50 g is needed to accurately represent the complex texture of feces. The MTBE method (MTBE/methanol/water, 3.6/2.8/3.5, v/v/v) outperformed the other extraction methods, reflected by the highest extraction efficiency for 11 different classes of compounds, the highest number of extracted features (97% of the total identified features in different extracts), repeatability (CV < 35%) and extraction recovery (≥70%). Importantly, optimization of the solvent volume of each step to the initial dried fecal material (µL/mg feces) offers a major step towards standardization, which enables confident assessment of the contributions of gut bacterial metabolites to human health.

A popular fragmentation technique for non-targeted analysis is called data-independent acquisition (DIA), because it provides fragmentation data for all analytes in a specific mass range. In this work, we demonstrated the strengths and weaknesses of DIA. Two types of chromatography (fractionation/3 min and hydrophilic interaction liquid chromatography (HILIC)/18 min) and three DIA protocols (variable sequential window acquisition of all theoretical mass spectra (SWATH), fixed SWATH and MSALL) were used to evaluate the performance of DIA. Our results show that fast chromatography and MSALL often results in product ion overlap and complex MS/MS spectra, which reduces the quantitative and qualitative power of these DIA protocols. The combination of SWATH and HILIC allowed for the correct identification of 20 metabolites using the NIST library. After SWATH window customization (i.e., variable SWATH), we were able to quantify ten structural isomers with a mean accuracy of 103% (91–113%). The robustness of the variable SWATH and HILIC method was demonstrated by the accurate quantification of these structural isomers in 10 highly diverse blood samples. Since the combination of variable SWATH and HILIC results in good quantitative and qualitative fragmentation data, it is promising for both targeted and untargeted platforms. This should decrease the number of platforms needed in metabolomics and increase the value of a single analysis.

Metabolomics is emerging as an important field in life sciences. However, a weakness of current mass spectrometry (MS) based metabolomics platforms is the time-consuming analysis and the occurrence of severe matrix effects in complex mixtures. To overcome this problem, we have developed an automated and fast fractionation module coupled online to MS. The fractionation is realized by the implementation of three consecutive high performance solid-phase extraction columns consisting of a reversed phase, mixed-mode anion exchange, and mixed-mode cation exchange sorbent chemistry. The different chemistries resulted in an efficient interaction with a wide range of metabolites based on polarity, charge, and allocation of important matrix interferences like salts and phospholipids. The use of short columns and direct solvent switches allowed for fast screening (3 min per polarity). In total, 50 commonly reported diagnostic or explorative biomarkers were validated with a limit of quantification that was comparable with conventional LC-MS(/MS). In comparison with a flow injection analysis without fractionation, ion suppression decreased from 89% to 25%, and the sensitivity was 21 times higher. The validated method was used to investigate the effects of circadian rhythm and food intake on several metabolite classes. The significant diurnal changes that were observed stress the importance of standardized sampling times and fasting states when metabolite biomarkers are used. Our method demonstrates a fast approach for global profiling of the metabolome. This brings metabolomics one step closer to implementation into the clinic.

In the past few years, the gut microbiome has been shown to play an important role in various disorders including in particular cardiovascular diseases. Especially the metabolite trimethylamine-N-oxide (TMAO), which is produced by gut microbial metabolism, has repeatedly been associated with an increased risk for cardiovascular events. Here we report a fast liquid chromatography tandem mass spectrometry (LC-MS/MS) method that can analyze the five most important gut metabolites with regards to TMAO in three minutes. Fast liquid chromatography is unconventionally used in this method as an on-line cleanup step to remove the most important ion suppressors leaving the gut metabolites in a cleaned flow through fraction, also known as negative chromatography. We compared different blood matrix types to recommend best sampling practices and found citrated plasma samples demonstrated lower concentrations for all analytes and choline concentrations were significantly higher in serum samples. We demonstrated the applicability of our method by investigating the effect of a standardized liquid meal (SLM) after overnight fasting of 25 healthy individuals on the gut metabolite levels. The SLM did not significantly change the levels of gut metabolites in serum.

The human metabolome provides a direct physiological read-out of an individual's actual health state and includes biomarkers that may predict disease or response to a treatment. The discovery and validation of these metabolomic biomarkers requires large-scale cohort studies, typically involving thousands of samples. This analytical challenge drives novel technological developments to enable faster, cheaper, and more comprehensive metabolomic analysis: more for less. This review summarises recent (2012–2018) developments towards this goal in all aspects of the analytical workflow, in relation to NMR but primarily to mass spectrometry (MS). Recent trends include miniaturisation and automation of extraction techniques, online coupling to fast analysis methods including direct infusion ion mobility MS, integrated microfluidic devices, and sharing and standardizing metabolomics software and data. The technological advances in metabolomics support its widespread application, integration with other -omics fields, and ultimately disease prediction and precision medicine.

Background: High-throughput simultaneous quantitative and qualitative (Quan/Qual) analysis is attractive to combine targeted with non-targeted analysis, e.g. in pharmacometabolomics and drug metabolism studies. This study aimed to investigate the possibilities and limitations of high-throughput Quan/Qual analysis by ultra-high performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS), to develop a widely applicable Quan/Qual UHPLC-HRMS method and to provide recommendations for Quan/Qual method development. Methods: A widely applicable 4.25-min UHPLC method for small-molecules was used to investigate and optimize mass spectrometric parameters of a Synapt G2S for Quan/Qual analysis. The method was applied on a rat metabolomics study investigating the effect of the fasting state and administration of a dosing vehicle on the rat plasma metabolic profile. Results: Highly important parameters for high-throughput Quan/Qual analysis were the scan mode and scan rate. A negative correlation was found between the amount of qualitative information that a method can provide and its quantitative performance (accuracy, precision, sensitivity, linear dynamic range). The optimal balance was obtained using the MSE scan mode with a short scan time of 30 ms. This 4.25-min Quan/Qual analysis method enabled quantification with accuracy and precision values <= 20% at the lowest quality control (QC) level and <= 15% at higher QC levels for 16 out of 19 tested analytes. It provided both parent m/z values and fragmentation spectra for compound identification with limited loss of chromatographic resolution and it revealed biologically relevant metabolites in its application to the metabolomics study. Conclusion: Quan/Qual method development requires balancing between the amount of qualitative data, the quality of the quantitative data and the analysis time. Recommendations are provided for MS resolution, scan mode, scan rate, smoothing and peak integration in Quan/Qual method development and analysis. (C) 2018 The Author(s). Published by Elsevier B.V.

An error in Table 2 concerning the structure of bendamustine and its metabolites M3, M4 and HP2 and the internal standard BM-IS has now been corrected, by placing the bis(2-chloroethyl)amino group (or its hydrolysed derivative in case of HP2) at the 5-position of the benzimidazole moiety.

Lenvatinib is an oral, multiple receptor tyrosine kinase inhibitor. Preclinical drug metabolism studies showed unique metabolic pathways for lenvatinib in monkeys and rats. A human mass balance study demonstrated that lenvatinib related material is mainly excreted via feces with a small fraction as unchanged parent drug, but little is reported about its metabolic fate. The objective of the current study was to further elucidate the metabolic pathways of lenvatinib in humans and to compare these results to the metabolism in rats and monkeys. To this end, we used plasma, urine and feces collected in a human mass balance study after a single 24 mg (100 μCi) oral dose of 14C-lenvatinib. Metabolites of 14C-lenvatinib were identified using liquid chromatography (high resolution) mass spectrometry with off-line radioactivity detection. Close to 50 lenvatinib-related compounds were detected. In humans, unchanged lenvatinib accounted for 97 % of the radioactivity in plasma, and comprised 0.38 and 2.5 % of the administered dose excreted in urine and feces, respectively. The primary biotransformation pathways of lenvatinib were hydrolysis, oxidation and hydroxylation, N-oxidation, dealkylation and glucuronidation. Various combinations of these conversions with modifications, including hydrolysis, gluthathione/cysteine conjugation, intramolecular rearrangement and dimerization, were observed. Some metabolites seem to be unique to the investigated species (human, rat, monkey). Because all lenvatinib metabolites in human plasma were at very low levels compared to lenvatinib, only lenvatinib is expected to contribute to the pharmacological effects in humans.

Lenvatinib is an orally available multi-targeted tyrosine kinase inhibitor with anti-angiogenic and antitumor activity. To get more insight into the disposition of lenvatinib, a mass balance study was performed in patients with advanced solid tumors. A single oral 24 mg (100 μCi) dose of (14)C-lenvatinib was administered to six patients, followed by collection of blood, plasma, urine and feces for 7 to 10 days. The collected material was analyzed for total radioactivity, unchanged lenvatinib and selected metabolites. The safety and antitumor effect of a daily oral dose of 24 mg non-labeled lenvatinib were assessed in the extension phase of the study. Peak plasma concentrations of lenvatinib and total radioactivity were reached 1.6 and 1.4 h after administration, respectively, and their terminal phase half-lifes were 34.5 and 17.8 h, respectively. Unchanged lenvatinib systemic exposure accounted for 60 % of the total radioactivity in plasma. Peak concentrations of the analyzed metabolite were over 700-fold lower than the peak plasma concentration of lenvatinib. Ten days after the initial dose, the geometric mean (± CV) recovery of administered dose was 89 % ±10 %, with 64 % ±11 % recovered in feces and 25 % ±18 % in urine. Unchanged lenvatinib in urine and feces accounted for 2.5 % ±68 % of the administered dose, indicating a major role of metabolism in the elimination of lenvatinib. In conclusion, lenvatinib is rapidly absorbed and extensively metabolized, with subsequent excretion in urine and, more predominantly, in feces. Additionally, lenvatinib showed acceptable safety and preliminary antitumor activity.