Liquid Chromatography-mass Spectrometry Datasets Research Articles

Simulated LC-MS Data Set for Assessing the Metabolomics Data Processing Pipeline Implemented into MVAPACK.

Metabolomics commonly relies on using one-dimensional (1D) 1H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture andMycobacterium smegmatiscell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Optimal transport for automatic alignment of untargeted metabolomic data.

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites.

Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.

Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC-MS.

Region of interest (ROI) extraction is a fundamental step in analyzing metabolomic datasets acquired by liquid chromatography-mass spectrometry (LC-MS). However, noises and backgrounds in LC-MS data often affect the quality of extracted ROIs. Therefore, developing effective ROI evaluation algorithms is necessary to eliminate false positives meanwhile keep the false-negative rate as low as possible. In this study, a deep fused filter of ROIs (dffROI) was proposed to improve the accuracy of ROI extraction by combining the handcrafted evaluation metrics with convolutional neural network (CNN)-learned representations. To evaluate the performance of dffROI, dffROI was compared with peakonly (CNN-learned representation) and five handcrafted metrics on three LC-MS datasets and a gas chromatography-mass spectrometry (GC-MS) dataset. Results show that dffROI can achieve higher accuracy, better true-positive rate, and lower false-positive rate. Its accuracy, true-positive rate, and false-positive rate are 0.9841, 0.9869, and 0.0186 on the test set, respectively. The classification error rate of dffROI (1.59%) is significantly reduced compared with peakonly (2.73%). The model-agnostic feature importance demonstrates the necessity of fusing handcrafted evaluation metrics with the convolutional neural network representations. dffROI is an automatic, robust, and universal method for ROI filtering by virtue of information fusion and end-to-end learning. It is implemented in Python programming language and open-sourced at https://github.com/zhanghailiangcsu/dffROI under BSD License. Furthermore, it has been integrated into the KPIC2 framework previously proposed by our group to facilitate real metabolomic LC-MS dataset analysis.

A data mining tool for untargeted biomarkers analysis: Grapes ripening application

In metabolomics, data generated by untargeted approaches can be very complex due to the typically extensive number of features in raw data (with and without chemical relevance), dependence on raw data preprocessing methods, and lack of selective data mining tools to appropriately interpret these data. Extraction of meaningful information from these data is still a significant challenge in metabolomics. Moreover, currently available tools may overprocess the data, eliminating useful information. This work aims at proposing a data mining tool capable of dealing with metabolomics data, specifically liquid chromatography-mass spectrometry (LC-MS) to enhance the extraction of meaningful chemical information. The algorithm construction intended to be as general as possible in highlighting chemically relevant features, discarding non-informative signals specially background features.The proposed algorithm was applied to an LC-MS data set generated from the analysis of grapes collected over a developmental period encompassing a 4-month period. The algorithm outcome is a short list of features from metabolites that are worth to be further investigated, for example by HRMS fragmentation for subsequent identification. The performance of the algorithm in estimating potentially interesting features was compared with the commercial MZmine software. For this case study, the MZmine output yielded a final set of 37 features (out of 1543 initially identified) with noise features while the proposed algorithm identified 99 systematic features without noise. Also, the algorithm required 2 times less user-defined parameters when compared to MZmine. Globally, the proposed algorithm demonstrated a higher ability to pin-point features that may be associated with grapes developmental and maturation processes requiring minimal parameters definition, thus preventing user uncertainty and the compromise of experimental information.

Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection.

Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning. Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output. An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package. Supplementary data are available at Bioinformatics online.

Deep Neural Networks for Classification of LC-MS Spectral Peaks.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.

Filtering procedures for untargeted LC-MS metabolomics data

BackgroundUntargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients.ResultsUsing metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering.ConclusionsOur proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

Using Cross-Correlation with Pattern Recognition Entropy to Obtain Reduced Total Ion Current Chromatograms from Raw Liquid Chromatography-Mass Spectrometry Data

Abstract Total ion current chromatograms (TICCs) generated by liquid chromatography-mass spectrometry (LC-MS) are prone to noise from chemical and electronic sources. This noise can severely impact the detection of analytes in a mixture. Recently, we introduced a new variable selection tool based on Pattern Recognition Entropy (PRE) that selects good quality (high signal-to-noise ratio) mass chromatograms from an LC-MS dataset and thereby creates a reduced TICC with low noise and a flat background (J. Chrom. A.2018, 1558, 21–28). PRE, which is based on Shannon’s entropy, was shown to be a straightforward and powerful shape recognition tool for this problem. However, while the chromatographic signals in the reduced TICC from PRE were well resolved, some noise remained in the TICC, which suggested that the algorithm had selected some false positives, i.e., poor quality mass chromatograms. In this paper, we report an improved version of the PRE algorithm that utilizes a second variable selection filter based on cross-correlation (CC). As a check on the ability of PRE and CC to select high quality mass chromatograms, every mass chromatogram in our data set (1451 in total) was individually inspected and rated as either high quality (green), intermediate quality (yellow), or poor quality (red). A color-coded plot of the CC value vs. the PRE value for the mass chromatograms was created, which shows that, as expected, the higher quality mass chromatograms are localized in its upper left quadrant, which corresponds to lower PRE values and higher CC values. In our original paper on this topic, we recommended a threshold of 0.5 σ for PRE, which caused the algorithm to select 151 mass chromatograms out of 1451. Of these, 98 were of high quality, 6 were of intermediate quality, and 47 were of poor quality. Using a second threshold for CC, the algorithm retains all the high and intermediate quality mass chromatograms, while removing all 47 of the poor quality ones. The resulting TICC from the PRE-CC algorithm shows less noise compared to the TICC generated from the PRE approach alone. The PRE-CC algorithm is arguably a faster, simpler and more intuitive approach as compared to the widely used CODA_DW algorithm.

Chromatographic Retention Assisted Deconvolution of Liquid Chromatography-Mass Spectrometry Chromatogram of Natural Products.

A chromatographic retention assisted denoising and peak picking algorithm (CRAD) is developed for preprocessing liquid chromatography-mass spectrometry (LC-MS) datasets of natural products. The retention behaviors of ions with the same m/z value are investigated under a series of elution conditions. The detected ions are identified as real compounds if their chromatographic retention behaviors fit well with the Snyder-Soczewinski model. Further, the ions with similar retention behaviors and isotope ratios are clustered. This method enables rapid identification of precursor ions when chemical standards or databases are unavailable. It also helps eliminate unexpected baseline disturbances and improve the resolution of LC-MS chromatograms. Unlike conventional deconvolution strategies, this method distinguishes the chemical properties of precursor ions through their dynamic retention behaviors. The algorithm is demonstrated with LC-MS datasets of control samples. In the application of such algorithms on a more complicated natural extract from Lycium ruthenicum Murr., 206 precursor ions were facilely determined.

Metabolomics approaches for the discrimination of disease suppressive soils for Rhizoctonia solani AG8 in cereal crops using 1H NMR and LC-MS

The suppression of soilborne crop pathogens such as Rhizoctonia solani AG8 may offer a sustainable and enduring method for disease control, though soils with these properties are difficult to identify. In this study, we analysed the soil metabolic profiles of suppressive and non-suppressive soils over 2 years of cereal production. We collected bulk and rhizosphere soil at different cropping stages and subjected soil extracts to liquid chromatography-mass spectrometry (LC-MS) and proton nuclear magnetic resonance spectroscopy (1H NMR) analyses. Community analyses of suppressive and non-suppressive soils using principal component analyses and predictive modelling of LC-MS and NMR datasets respectively, revealed distinct biochemical profiles for the two soil types with clustering based on suppressiveness and cropping stage. NMR spectra revealed the suppressive soils to be more abundant in sugar molecules than non-suppressive soils, which were more abundant in lipids and terpenes. LC-MS features that were significantly more abundant in the suppressive soil were identified and assessed as potential biomarkers for disease suppression. The structures of a potential class of LC-MS biomarkers were elucidated using accurate mass data and MS fragmentation spectrum information. The most abundant compound found in association with suppressive soils was confirmed to be a macrocarpal, which is an antimicrobial secondary metabolite. Our study has demonstrated the utility of environmental metabolomics for the study of disease suppressive soils, resulting in the discovery of a macrocarpal biomarker for R. solani AG8 suppressive soil which can be further studied functionally in association with suppression pot trials and microbial isolation studies.

A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data.

Omic data are characterized by the presence of strong dependence structures that result either from data acquisition or from some underlying biological processes. Applying statistical procedures that do not adjust the variable selection step to the dependence pattern may result in a loss of power and the selection of spurious variables. The goal of this paper is to propose a variable selection procedure within the multivariate linear model framework that accounts for the dependence between the multiple responses. We shall focus on a specific type of dependence which consists in assuming that the responses of a given individual can be modelled as a time series. We propose a novel Lasso-based approach within the framework of the multivariate linear model taking into account the dependence structure by using different types of stationary processes covariance structures for the random error matrix. Our numerical experiments show that including the estimation of the covariance matrix of the random error matrix in the Lasso criterion dramatically improves the variable selection performance. Our approach is successfully applied to an untargeted LC-MS (Liquid Chromatography-Mass Spectrometry) data set made of African copals samples. Our methodology is implemented in the R package MultiVarSel which is available from the Comprehensive R Archive Network (CRAN).

A Simple Method for Peak Alignment Using Relative Retention Time Related to an Inherent Peak in Liquid Chromatography-Mass Spectrometry-Based Metabolomics.

Retention time (RT) shifts are inevitable in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics studies. RT alignment, aligning peaks originating from the same feature to an identical RT, is a necessary step prior to multivariate data analysis. Many RT alignment software packages and various algorithms have been developed. In the present study, a simple method for peak alignment was proposed using relative RT related to an inherent peak in the chromatogram, and its performance on RT alignment in untargeted metabolomics was evaluated using an LC-MS dataset acquired from plasma samples from osteoporotic rats, and compared with the common MarkerLynx XS method using different RT window. RT alignment accuracy and relative standard deviation (RSD) value of the intensity of 79 selected ions in a chromatogram were employed to assess the alignment effectiveness of Method I (0.2 RT window), Method II (0.5 RT window) and Method III (relative RT). The overall RT alignment accuracy of these three methods was 67.7%, 77.2% and 90.9%, and 44.3%, 72.2% and 82.3% of the ions had intensity RSD values <30%, respectively. The results demonstrated that Method III using relative RT significantly reduced the influence caused by RT shifts in LC-MS-based metabolomics. Furthermore, Method I and Method III were applied to discover plasma biomarkers for osteoporosis in rats. Thirteen and nineteen ions were selected as potential biomarkers, respectively. Among them, only eight common ions were found, which demonstrated that different sets of biomarkers were obtained using those two RT alignment methods. The relative RT method exhibited fewer missing values, a lower RSD of peak intensity and better performance in pattern recognition, thereby showing the great potential in future metabolomics studies.

Strategy for Screening of Antioxidant Compounds from Two Ulmaceae Species Based on Liquid Chromatography-Mass Spectrometry.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics implies that annotated metabolites can serve as potential markers of the associated bioactivities of plant extracts. Firstly, we selected Aphananthe aspera and Zelkova serrata (Family: Ulmaceae) from 16 Korean plant species based on their distinct principal component analysis (PCA) patterns in LC-MS datasets and antioxidant activity assays. Further, we chose 40% solid-phase extraction (SPE) extracts of the two species displaying the highest antioxidant activities coupled with distinct PCA patterns. Examining the metabolite compositions of the 40% SPE extracts, we observed relatively higher abundances of quercetin, kaempferol, and isorhamnetin O-glucosides for A. aspera, whereas quercetin, isorhamnetin O-glucuronides, and procyanidin dimer were relatively higher in Z. serrata. These metabolites were clearly distinguished in pathway map and displayed strong positive correlations with antioxidant activity. Further, we performed preparative high-performance liquid chromatography (prep-HPLC) analysis coupled with the 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) assay to validate their functional correlations. As a result, quercetin O-sophoroside was determined as the main antioxidant in A. aspera, while isorhamnetin O-glucuronide and procyanidin dimer were the primary antioxidants in Z. serrata. The current study suggests that the LC-MS-based untargeted metabolomics strategy can be used to illuminate subtle metabolic disparities as well as compounds associated with bioactivities.

Systematic metabolic profiling and bioactivity assays for bioconversion of Aceraceae family.

Plants are an important and inexhaustible source of bioactive molecules in food, medicine, agriculture, and industry. In this study, we performed systematic liquid chromatography–mass spectrometry (LC-MS)-based metabolic profiling coupled with antioxidant assays for indigenous plant family extracts. Partial least-squares discriminant analysis of LC-MS datasets for the extracts of 34 plant species belonging to the families Aceraceae, Asteraceae, and Rosaceae showed that these species were clustered according to their respective phylogenies. In particular, seven Aceraceae species were clearly demarcated with higher average antioxidant activities, rationalizing their application for bioconversion studies. On the basis of further evaluation of the interspecies variability of metabolic profiles and antioxidant activities among Aceraceae family plants, we found that Acer tataricum (TA) extracts were clearly distinguished from those of other species, with a higher relative abundance of tannin derivatives. Further, we detected a strong positive correlation between most tannin derivatives and the observed higher antioxidant activities. Following Aspergillus oryzae-mediated fermentative bioconversion of Acer plant extracts, we observed a time-correlated (0–8 days) linear increase in antioxidant phenotypes for all species, with TA having the highest activity. Temporal analysis of the MS data revealed tannin bioconversion mechanisms with a relatively higher abundance of gallic acid (m/z 169) accumulated at the end of 8 days, particularly in TA. Similarly, quercetin precursor (glycoside) metabolites were also transformed to quercetin aglycones (m/z 301) in most Acer plant extracts. The present study underscores the efficacy of fermentative bioconversion strategies aimed at enhancing the quality and availability of bioactive metabolites from plant extracts.

PD38-07 MULTIPLATFORM METABOLOMICS REVEALS PREDICTIVE PROSTATE CANCER RECURRENCE PHENOTYPES FOLLOWING RADICAL PROSTATECTOMY

You have accessJournal of UrologyProstate Cancer: Localized: Surgical Therapy V1 Apr 2018PD38-07 MULTIPLATFORM METABOLOMICS REVEALS PREDICTIVE PROSTATE CANCER RECURRENCE PHENOTYPES FOLLOWING RADICAL PROSTATECTOMY Callan Brownfield, Chaevien Clendinen, David Gaul, Rebecca Arnold, Arthur Edison, Facundo Fernandez, and John Petros Callan BrownfieldCallan Brownfield More articles by this author , Chaevien ClendinenChaevien Clendinen More articles by this author , David GaulDavid Gaul More articles by this author , Rebecca ArnoldRebecca Arnold More articles by this author , Arthur EdisonArthur Edison More articles by this author , Facundo FernandezFacundo Fernandez More articles by this author , and John PetrosJohn Petros More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1754AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES By far the most commonly used technologies in metabolomics are Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS). The structural diversity of metabolites, however, dictates that maximum metabolome coverage can only be achieved by applying multiple complementary technologies simultaneously. Here, we use fused 1H NMR and LC-MS (HILIC, RP, +ve, -ve) datasets to determine the alterations in metabolic pathways indicative of prostate cancer (PCa) recurrence following prostatectomy. A priori prediction of the likelihood of PCa recurrence prior to prostatectomy would allow avoiding unnecessary surgery and associated complications in patients not likely to be cured by prostatectomy. We provide strong evidence that it is feasible to predict recurrence after radical prostatectomy based on the patient metabolic phenotype prior to surgery. METHODS De-identified serum obtained prior to planned prostatectomy was aliquoted and specifically prepared for either NMR, HILIC-MS, or RP-MS. MS experiments were performed using an Ultimate3000 LC system coupled to a Q-Exactive HF equipped with a HESI source in both +ve and -ve mode. NMR experiments were performed on a Bruker Avance III HD 600 MHz system equipped with a triple resonance 5 mm cold probe. All MS data were processed using Progenesis QI. Database searching NMR peaks and accurate mass and MS2 matching were used to produce a list of tentative IDs. Lipid IDs were obtained via Lipidsearch using accurate MS and MS2. Multivariate data analysis was done using MATLAB and the PLS Toolbox. All patients had long term post-prostatectomy follow-up and were categorized as recurrent or non-recurrent based on standard criteria, including biochemical, clinical and/or radiologic recurrence. RESULTS Five datasets resulted from our multiplatform approach. HILIC +ve and -ve mode and NMR datasets were used to provide coverage for predominately polar metabolites, while reverse phase (RP) +ve and -ve mode datasets involved predominantly non-polar metabolites. HILIC +ve and -ve datasets provided over 190 uniquely identified metabolites, with 50 unique additional metabolites obtained from NMR. The biggest changes were observed in purine and TCA cycle metabolites. Though some metabolites showed no significant differences, recurrence was shown to have increased metabolism purine metabolism. Patients without post-operative cancer recurrence had significantly higher serum glucose prior to surgery while those that recurred following surgery had significantly higher pre-operative lactate. In most cases, information between these HILIC-MS and NMR data were consistent and correlations between like metabolites across different platforms were significant. Most small molecules identified in the HILIC and NMR datasets were not covered by the RP datasets. RP +ve and -ve mode provided information on lipidome alterations. Over 450 lipids were identified from both positive and negative mode LC-MS experiments. The most common altered lipids being triglycerides (TG), about half of which increased in recurrence. Across all chain lengths and saturations, recurrent patients showed more significantly changed lipids. Though some lipids were seen in the HILIC and NMR datasets, lipid coverage in the RP datasets were much greater and inclusive.Feature selection from the fused dataset employing a genetic algorithm yielded 29 features that distinguished between patients that had recurrence or went into remission following surgery with approximately 99 percent accuracy and 98 percent specificity. Lipids, lactate, and phenylalanine were among the features that underlie the separation between the two groups. The use of all platforms was vital in characterizing the metabolic phenotype underlying PCa recurrence. CONCLUSIONS A multiplatform analytical approach provides in-depth views of PCa metabolome and exceptionally accurate prediction of recurrence from a single pre-operative blood sample. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e738-e739 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Callan Brownfield More articles by this author Chaevien Clendinen More articles by this author David Gaul More articles by this author Rebecca Arnold More articles by this author Arthur Edison More articles by this author Facundo Fernandez More articles by this author John Petros More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Curatr: a web application for creating, curating and sharing a mass spectral library.

We have developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries from liquid-chromatography mass spectrometry datasets. Curatr handles datasets from single or multiplexed standards and extracts chromatographic profiles and potential fragmentation spectra for multiple adducts. An intuitive interface helps users to select high quality spectra that are stored along with searchable molecular information, the providence of each standard and experimental metadata. Curatr supports exports to several standard formats for use with third party software or submission to repositories. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library http://curatr.mcf.embl.de. Source code and example data are at http://github.com/alexandrovteam/curatr/. palmer@embl.de. Supplementary data are available at Bioinformatics online.

Structure Annotation and Quantification of Wheat Seed Oxidized Lipids by High-Resolution LC-MS/MS.

Lipid oxidation is a process ubiquitous in life, but the direct and comprehensive analysis of oxidized lipids has been limited by available analytical methods. We applied high-resolution liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MS/MS) to quantify oxidized lipids (glycerides, fatty acids, phospholipids, lysophospholipids, and galactolipids) and implemented a platform-independent high-throughput-amenable analysis pipeline for the high-confidence annotation and acyl composition analysis of oxidized lipids. Lipid contents of 90 different naturally aged wheat (Triticum aestivum) seed stocks were quantified in an untargeted high-resolution LC-MS experiment, resulting in 18,556 quantitative mass-to-charge ratio features. In a posthoc liquid chromatography-tandem mass spectrometry experiment, high-resolution MS/MS spectra (5 mD accuracy) were recorded for 8,957 out of 12,080 putatively monoisotopic features of the LC-MS data set. A total of 353 nonoxidized and 559 oxidized lipids with up to four additional oxygen atoms were annotated based on the accurate mass recordings (1.5 ppm tolerance) of the LC-MS data set and filtering procedures. MS/MS spectra available for 828 of these annotations were analyzed by translating experimentally known fragmentation rules of lipids into the fragmentation of oxidized lipids. This led to the identification of 259 nonoxidized and 365 oxidized lipids by both accurate mass and MS/MS spectra and to the determination of acyl compositions for 221 nonoxidized and 295 oxidized lipids. Analysis of 15-year aged wheat seeds revealed increased lipid oxidation and hydrolysis in seeds stored in ambient versus cold conditions.

Butia spp. (Arecaceae) LC-MS-Based Metabolomics for Species and Geographical Origin Discrimination.

The metabolic variability of fruit from Butia spp. (Arecaceae) genotypes from different geographical locations was characterized using untargeted metabolomics by liquid chromatography-mass spectrometry (LC-MS) followed by multivariate data analyses. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) from LC-MS data sets showed a clear distinction among Butia catarinensis, Butia odorata, Butia paraguayensis, and Butia yatay. The major metabolites that contributed to species discrimination were primary metabolites including sugars and organic acids and specialized metabolites such as tetrahydroxy-trans-stilbene and rutin. B.odorata fruit from Tapes, RS, Brazil, showed a high content of organic acids and flavonoids, whereas B.odorata fruits from Capão do Leão, RS, Brazil, showed a high sugar content. The results demonstrate that LC-ESI-qToF-MS-based metabolic profiling coupled with chemometric analysis can be used to discriminate among Butia species and between geographical origins of B.odorata and to identify primary and specialized metabolites responsible for the discrimination.