LC-MS Datasets Research Articles

MetaboLink: A web application for Streamlined Processing and Analysis of Large-Scale Untargeted Metabolomics Data.

The post-processing and analysis of large-scale untargeted metabolomics data face significant challenges due to the intricate nature of correction, filtration, imputation, and normalization steps. Manual execution across various applications often leads to inefficiencies, human-induced errors, and inconsistencies within the workflow. Addressing these issues, we introduce MetaboLink, a novel web application designed to process LC-MS metabolomics datasets combining established methodologies and offering flexibility and ease of implementation. It offers visualization options for data interpretation, an interface for statistical testing, and integration with PolySTest for further tests and with VSClust for clustering analysis. Fully functional tool is publicly available at https://computproteomics.bmb.sdu.dk/Metabolomics/. The source code is available at https://github.com/anitamnd/MetaboLink and a detailed description of the app can be found at https://github.com/anitamnd/MetaboLink/wiki. A tutorial video can be found at https://youtu.be/ZM6j10S6Z8Q. Supplementary data are available at Bioinformatics online.

HowDirty: An R package to evaluate molecular contaminants in LC-MS experiments.

Contaminants derived from consumables, reagents, and sample handling often negatively affect LC-MS data acquisition. In proteomics experiments, they can markedly reduce identification performance, reproducibility, and quantitative robustness. Here, we introduce a data analysis workflow combining MS1 feature extraction in Skyline with HowDirty, an R-markdown-based tool, that automatically generates an interactive report on the molecular contaminant level in LC-MS data sets. To facilitate the interpretation of the results, the HTML report is self-contained and self-explanatory, including plots that can be easily interpreted. The R package HowDirty is available from https://github.com/DavidGZ1/HowDirty. To demonstrate a showcase scenario for the application of HowDirty, we assessed the impact of ultrafiltration units from different providers on sample purity after filter-assisted sample preparation (FASP) digestion. This allowed us to select the filter units with the lowest contamination risk. Notably, the filter units with the lowest contaminant levels showed higher reproducibility regarding the number of peptides and proteins identified. Overall, HowDirty enables the efficient evaluation of sample quality covering a wide range of common contaminant groups that typically impair LC-MS analyses, facilitating corrective or preventive actions to minimize instrument downtime.

Non-target ROIMCR LC–MS analysis of the disruptive effects of TBT over time on the lipidomics of Daphnia magna

IntroductionThis study has investigated the temporal disruptive effects of tributyltin (TBT) on lipid homeostasis in Daphnia magna. To achieve this, the study used Liquid Chromatography–Mass Spectrometry (LC–MS) analysis to analyze biological samples of Daphnia magna treated with TBT over time. The resulting data sets were multivariate and three-way, and were modeled using bilinear and trilinear non-negative factor decomposition chemometric methods. These methods allowed for the identification of specific patterns in the data and provided insight into the effects of TBT on lipid homeostasis in Daphnia magna.ObjectivesInvestigation of how are the changes in the lipid concentrations of Daphnia magna pools when they were exposed with TBT and over time using non-targeted LC–MS and advanced chemometric analysis.MethodsThe simultaneous analysis of LC–MS data sets of Daphnia magna samples under different experimental conditions (TBT dose and time) were analyzed using the ROIMCR method, which allows the resolution of the elution and mass spectra profiles of a large number of endogenous lipids. Changes obtained in the peak areas of the elution profiles of these lipids caused by the dose of TBT treatment and the time after its exposure are analyzed by principal component analysis, multivariate curve resolution-alternative least square, two-way ANOVA and ANOVA-simultaneous component analysis.Results87 lipids were identified. Some of these lipids are proposed as Daphnia magna lipidomic biomarkers of the effects produced by the two considered factors (time and dose) and by their interaction. A reproducible multiplicative effect between these two factors is confirmed and the optimal approach to model this dataset resulted to be the application of the trilinear factor decomposition model.ConclusionThe proposed non-targeted LC–MS lipidomics approach resulted to be a powerful tool to investigate the effects of the two factors on the Daphnia magna lipidome using chemometric methods based on bilinear and trilinear factor decomposition models, according to the type of interaction between the design factors.

Large-Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC-MS measurements.

Untargeted metabolomics is the study of all detectable small molecules, and in geroscience, metabolomics has shown great potential to describe the biological age-a complex trait impacted by many factors. Unfortunately, the sample sizes are often insufficient to achieve sufficient power and minimize potential biases caused by, for example, demographic factors. In this study, we present the analysis of biological age in ~10,000 toxicologic routine blood measurements. The untargeted screening samples obtained from ultra-high pressure liquid chromatography-quadruple time of flight mass spectrometry (UHPLC- QTOF) cover + 300 batches and + 30 months, lack pooled quality controls, lack controlled sample collection, and has previously only been used in small-scale studies. To overcome experimental effects, we developed and tested a custom neural network model and compared it with existing prediction methods. Overall, the neural network was able to predict the chronological age with an rmse of 5.88 years (r2 =0.63) improving upon the 6.15 years achieved by existing normalization methods. We used the feature importance algorithm, Shapley Additive exPlanations (SHAP), to identify compounds related to the biological age. Most importantly, the model returned known aging markers such as kynurenine, indole-3-aldehyde, and acylcarnitines along with a potential novel aging marker, cyclo (leu-pro). Our results validate the association of tryptophan and acylcarnitine metabolism to aging in a highly uncontrolled large-s cale sample. Also, we have shown that by using robust computational methods it is possible to deploy large LC-MS datasets for metabolomics studies to reduce the risk of bias and empower aging studies.

Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets

Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS are quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5%–4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.

Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.

Detection of soft-refined oils in extra virgin olive oil using data fusion approaches for LC-MS, GC-IMS and FGC-Enose techniques: The winning synergy of GC-IMS and FGC-Enose

Extra virgin olive oil (EVOO) is frequently adulterated by mixing it with soft refined oils (SROO). The differentiation of EVOO from its blends with SROO is not possible with the most common approaches, and, for this reason, the discriminating power of liquid chromatography-high resolution mass spectrometry (LC-MS), gas-chromatography ion mobility spectrometry (GC-IMS) and flash gas-chromatography electronic nose (FGC-Enose) was examined previously. Here, the combination of the above-mentioned techniques for an improvement in classification power of the methods is explored. A total of 43 commercial EVOOs and 18 illegal mixtures of SROO with EVOO were previously analysed by LC-(+/−)MS, GC-IMS and FGC-Enose. Low-level and mid-level data fusion of the four datasets were performed. The merged unique fingerprints were submitted to partial least squared discriminant analysis (PLS-DA), and the extrapolated most informative variables were used to build support vector machine (SVM) classifiers. Statistical indicators were calculated and compared to find out the best classifier. The results of PLS-DA-SVM strategies on the combination of datasets demonstrated that, after low-level data fusion, the discriminatory capability of the two merged GC-based techniques was remarkably improved as compared to the individual techniques. This indicates that merging the datasets before PLS-DA better retrieves the most informative variables and, thus, enhances group separation and classification of unknowns. The combination of LC(+/−)MS datasets, both by mid- and low-level data fusion, did not show significant enhancement in terms of discrimination of EVOO from SROO as compared to the individual LC(+)MS matrix. The low-level combination of the four datasets (LC(+/−)MS, GC-IMS, FGC-Enose) was successful, although this laborious option is not a viable path in industry quality assurance. This study primarily provides new paths for the authentication of EVOO, taking advantage of merging multimodal LC-(+/−)MS, GC-IMS and FGC-Enose data, with consequent improvement in the performances of the classification models. The most promising results were achieved by the low-level data fusion of GC-IMS and FGC-Enose data.

Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR.

Untargeted metabolomics and lipidomics LC–MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC–MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent–fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC–MS datasets by combining correlation-based parent–fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82–92% and 82–85%, respectively, were obtained for features found in the highest-rank scores (1–5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60–89%) and recall (10–88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).

Therapeutic effect of Moringa oleifera leaves on constipation mice based on pharmacodynamics and serum metabonomics

Ethnopharmacological relevanceMoringa oleifera is native to India, and has been introduced to China in recent years. Moringa oleifera leaves (MOL), as Ayurvedic medicine, has efficacy of Pachana karma (digestive) and Virechana karma (purgative). Folium Sennae (FS), Rhubarb (RB), Aloe vera (AV), Hemp seed (HS) are commonly used as laxatives in Traditional Chinese Medicine (TCM), which have different characteristics. However, the intensity of the diarrheal effect of MOL and its mechanism of action are unclear. Aim of the studyThe methods of pharmacology and omics were used to compare the purgative effects of MOL and FS, RB, AV, HS, and their effects on metabolomics, to analyze the purgative characteristics and related mechanisms of MOL. Materials and methodsC57BL/6J mouse model of constipation was established by feeding low-fiber food. Feces parameters and colon pathology were used to evaluate the effect of FS, RB, AV, HS and MOL. And mass spectrometry-based serum metabolomics was performed. The differential metabolites of these herbs in the treatment of constipation were obtained by OPLS-DA analysis. Furthermore, pathway analysis was conducted based on different metabolites. ResultsMoringa leaves can adjust the stool number, wet fecal weight and fecal water content to varying degrees to achieve laxative effects, and recover colon muscle thickness and mucus. Analysis of metabolomics results showed that 71 metabolites from LC-MS datasets between model group and control group were obtained. 29, 12, 44, 29 and 20 metabolites were significantly reversed by FS, RB, AV, HS, MOL compared with model group respectively. According to the metabolic pathways, RB and AV may be clustered into a similar category, and MOL, FS and HS showed similarity of metabolic characteristics. ConclusionThe purgative effect of MOL is inferior to that of FS, and stronger than that of AV, RB and HS. The metabolic pathway for constipation is more similar to that of FS. MOL has a long-lasting and mild effect of laxative, increasing defecation volume and water content of feces, and may become a fewer side effects medicine to treat constipation.

Not just shrivelling: time-series profiling of the biochemical changes in Corvina (<i>Vitis vinifera</i> L.) berries subjected to post-harvest withering

The grape berry withering process is often seen as a means to concentrate the constituents of the berry via water removal, however, recent molecular studies have indicated that the reprogramming of biosynthetic pathways that impart the unique aroma and flavour of the final wine also occur. Metabolic analysis was performed using GC-MS and LC-MS on Corvina berry dehydrated for up to 108 days. The temporal pattern of metabolic changes and time-series relationship between various classes of metabolites were investigated on berries sampled at 23-time points. Principal component analysis of both GC-MS and LC-MS datasets revealed three distinct phases of post-harvest withering; early (day 0–28), mid (day 35–66) and late (day 69–108) stages. Stress-associated amino acids such as proline, serine, ethanolamine, and leucine accumulated significantly during the mid and late stages of the drying process while phosphorylated glucose and fructose, sucrose, kestose and resveratrol increased massively across time. Unlike most of the identified metabolites, low molecular weight flavanols exhibited a consistent pattern of decline during the withering period. Our network analysis revealed that increased metabolic network connectivity occurred during the middle stage of withering, thus reflecting more coordinated metabolite changes while reduced network connectivity at early and late stages indicates minimal metabolite perturbation during these stages. The current prolonged berry withering experiment revealed the timing of metabolite interconversions in the central metabolism and provided critical clues that link the concentration effect, protein degradation, and the onset of stress-like conditions in drying berries.

MetabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets.

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

Interval estimation in multivariate curve resolution by exploiting the principles of error propagation in linear least squares

Interval estimation in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is a challenging problem. Several algorithms including Bayesian, Monte-Carlo, bootstrap and jackknife resampling approaches have been proposed previously to address this problem in MCR. In the present contribution, constructing the confidence intervals (CIs) in MCR-ALS resolved profiles using the principles of error propagation in linear least squares (LS) parameter estimation is proposed. In MCR-ALS, every set of profiles in the row subspace (scores) can be considered as coefficients for estimating their counterparts in the columns subspace (loadings) and vice versa. Therefore, it should be possible to calculate the CIs for these coefficients (scores or loadings) using the principles of error propagation in LS. The confidence intervals of the coefficients in linear regression are directly related to the inverse of the matrix of sums of squares and sums of cross products of independent variables and to the standard deviation of the residuals. This idea has been successfully applied in this work for the construction of the CIs of the resolved profiles in the MCR-ALS algorithm. The proposed approach is named ‘Confidence Intervals based on Least Squares’ (CILS). The weighted version of this approach has also been implemented and named as CIWLS. The latter method can be used for handling datasets with a known type of error structure. The performances of the CILS and CIWLS approaches are evaluated in this work for the calculation of the CIs for several simulated three component LC-MS and LC-DAD datasets, with different homo- and heteroscedastic noise levels. The CIs obtained by CILS and CIWLS are compared with those obtained by the Monte-Carlo noise addition method. The empirical coverage probabilities of the CIs are also computed to check if the calculated CIs achieve the nominal confidence level. The average standard errors (ASE) are used as measures of the level of uncertainty (precision) in the recovered profiles. The results obtained in this work revealed that the CIs obtained by the CILS method are in agreement with those obtained by the Monte-Carlo approach. The main advantage of the CILS method is that the calculations are faster and require less computation time. Finally, the performance of the CIWLS algorithm was assessed in the analysis of a real environmental dataset for the source apportionment of particulate matter (PM) in air samples collected in Northern Spain. The results obtained by the CIWLS method were in agreement with those previously reported for this real dataset and the CIs of the contribution and composition profiles of the PM10 sources were properly estimated.

High sensitivity and specificity feature detection in liquid chromatography–mass spectrometry data: A deep learning framework

Feature detection is a crucial pre-processing step for high-resolution liquid chromatography-mass spectrometry (LC-MS) data analysis. Typical practices based on thresholds or rigid mathematical assumptions can cause ineffective performance in detecting low abundance and non-ideal distributed compounds. We herein introduce a novel feature detection method based on deep learning named SeA-M2Net that considers feature detection as an image-based object detection task. By fully employing raw data directly, and integrating all related factors (e.g., LC elution, charge state, and isotope distribution) with two-dimensional pseudo color images to calculate the probability of the presence of the compound, low abundance compounds can be well preserved and observed. More importantly, SeA-M2Net, with deep multilevel and multiscale structures focuses on compound pattern detection in a learned method instead of assuming a mathematical parametric model. All parameters in SeA-M2Net are learned from data in the training procedure, thus allowing for maximum flexibility of pattern distribution deformation. The algorithm is tested on several LC–MS datasets of multiple biological samples obtained from different instruments with varied experimental settings. We demonstrate the superiority of the new approach in handling complex compound patterns (e.g., low abundance, overlapping regions, LC shifts, and missing values). Our experiments indicate that SeA-M2Net outperforms widely used detection methods in terms of detection accuracy.

Feature Extraction for LC–MS via Hierarchical Density Clustering

Liquid chromatography coupled with mass spectrometry (LC–MS) is a popular analytical platform for metabolomic studies. Accurate and sensitive feature detection is a key step before further analysis. It is still challenging due to the large quantity and high complexity of LC–MS data sets. Pure ion chromatogram (PIC) consists of ions produced from metabolite without interferences. Therefore, hierarchical density-based spatial clustering of applications with noise (HDBSCAN) was applied to extract PICs from LC to MS data sets in this study. Since metabolites generate high-density and continuous ions in both m/z and elution time axes, HDBSCAN can cluster ions of the same metabolite into the same group and avoid the definition of m/z tolerance. Compared to centWave and PITracer, the proposed method achieved higher recall and comparable levels of precision for feature detection on simulated, MM48 and Arabidopsis thaliana (L.) Heynh data sets. It was implemented in Python and opensourced at http://www.github.com/zmzhang/HPIC .

ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets

BackgroundThe analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each “feature” (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.ResultsHere, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications.ConclusionsThe methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel.

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”.

Triterpene Acid and Phenolics from Ancient Apples of Friuli Venezia Giulia as Nutraceutical Ingredients: LC-MS Study and In Vitro Activities

Triterpene acid and phenolic constituents from nine ancient varieties of apple (Malus domestica) fruits cultivated in Fanna, Friuli Venezia Giulia region, northeast Italy, were analyzed and compared with four commercial apples (‘Golden Delicious’, ‘Red Delicious’, ‘Granny Smith’ and ‘Royal Gala’). Total phenolic and flavonoid contents were measured by spectrophotometric assays. The quali-quantitative fingerprint of secondary metabolites including triterpene acid was obtained by LC-DAD-(ESI)-MS and LC-(APCI)-MS, respectively. Based on the two LC-MS datasets, multivariate analysis was used to compare the composition of ancient fruit varieties with those of four commercial apples. Significant differences related mainly to the pattern of triterpene acids were found. Pomolic, euscaphyc, maslinic and ursolic acids are the most abundant triterpene in ancient varieties pulps and peels, while ursolic and oleanolic acids were prevalent in the commercial fruits. Also, the content of the phenolic compounds phloretin-2-O-xyloglucoside and quercetin-3-O-arabinoside was greater in ancient apple varieties. The antioxidant (radical scavenging, reducing power, metal chelating and phosphomolybdenum assays) and enzyme inhibitory effects (against cholinesterase, tyrosinase, amylase and glucosidase) of the samples were investigated in vitro. Antioxidant assays showed that the peels were more active than pulps. However, all the samples exhibited similar enzyme inhibitory effects. Ancient Friuli Venezia Giulia apple cultivars can be a source of chlorogenic acid and various triterpene acids, which are known for their potential anti-inflammatory activity and beneficial effects on lipid and glucose metabolism. Our results make these ancient varieties suitable for the development of new nutraceutical ingredients.

Incorporating In-Source Fragment Information Improves Metabolite Identification Accuracy in Untargeted LC-MS Data Sets.

In-source fragmentation occurs as a byproduct of electrospray ionization. We find that ions produced as a result of in-source fragmentation often match fragment ions produced during MS/MS fragmentation, and we take advantage of this phenomenon in a novel algorithm to analyze LC-MS metabolomics data sets. Our approach organizes coeluting MS1 features into a single peak group and then identifies in-source fragments among coeluting features using MS/MS spectral libraries. We tested our approach using previously published data of verified metabolites and compared the results to features detected by other mainstream metabolomics tools. Our results indicate that considering in-source fragment information as a part of the identification process increases the annotation quality, allowing us to leverage MS/MS data in spectrum libraries even if MS/MS scans were not collected.

Metabolomics and antioxidant activity of the leaves of Prunus dulcis Mill. (Italian cvs. Toritto and Avola)

Prunus dulcis leaves have been reported to exert some biological activity, in particular potent free radical-scavenging capacity, but so far there is limited information on their chemical composition. With the aim to achieve deep insight on the chemical constituents of the leaves of P. dulcis cultivars “Toritto” and “Avola”, the most appreciated in Italy, an approach based on liquid chromatography-mass spectrometry (LC–MS) combined with isolation and structure elucidation of pure compounds by Nuclear Magnetic Resonance (NMR) analysis was carried out. Results allowed to detect in cv. Toritto leaves phenolics, terpenoids and a cyanogenic glycoside. Successively, various solvent systems were chosen to afford different extracts and an approach based on principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) from LC–MS data sets highlighted eco-friendly methods as selective in extracting phenolics and glycosides. Comparison of LC–MS profiles of the MeOH extracts of cv. Toritto and cvs. Avola (Pizzuta, Fascionello and Romana) leaves and evaluation of their phenolic contents and antioxidant activity were also carried out.

LPPtiger software for lipidome-specific prediction and identification of oxidized phospholipids from LC-MS datasets

Oxidized phospholipids (oxPLs) have been recently recognized as important mediators of various and often controversial cellular functions and stress responses. Due to the low concentrations in vivo, oxPL detection is mostly performed by targeted mass spectrometry. Although significantly improving the sensitivity, this approach does not provide a comprehensive view on oxPLs required for understanding oxPL functional activities. While capable of providing information on the diversity of oxPLs, the main challenge of untargeted lipidomics is the absence of bioinformatics tools to support high-throughput identification of previously unconsidered, oxidized lipids. Here, we present LPPtiger, an open-source software tool for oxPL identification from data-dependent LC-MS datasets. LPPtiger combines three unique algorithms to predict oxidized lipidome, generate oxPL spectra libraries, and identify oxPLs from tandem MS data using parallel processing and a multi-scoring identification workflow.