Strategy for Optimizing LC-MS Data Processing in Metabolomics: A Design of Experiments Approach

Plasma metabolomics for diagnostic biomarkers on ectopic pregnancy

Metabolomics is a relatively novel omics tool to provide potential biomarkers for early diagnosis of the diseases and to insight the pathophysiology not having discussed ever before. In the present study, an ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was employed to the plasma samples of Group T1: Patients with ectopic pregnancy diagnosed using ultrasound, and followed-up with beta-hCG level (n = 40), Group T2: Patients with ectopic pregnancy diagnosed using ultrasound, underwent surgical treatment and confirmed using histopathology (n = 40), Group P: Healthy pregnant women (n = 40) in the first prenatal visit of pregnancy, Group C: Healthy volunteers (n = 40) scheduling a routine gynecological examination. Metabolite extraction was performed using 3 kDa pores - Amicon® Ultra 0.5 mL Centrifugal Filters. A gradient elution program (mobile phase composition was water and acetonitrile consisting of 0.1% formic acid) was applied using a C18 column (Agilent Zorbax 1.8 μM, 100 x 2.1 mm). Total analysis time was 25 min when the flow rate was 0.2 mL/min. The raw data was processed through XCMS - R program language edition where the optimum parameters detected using Isotopologue Parameter Optimization (IPO). The potential metabolites were identified using MetaboAnalyst 5.0 and finally 27 metabolites were evaluated to be proposed as potential biomarkers to be used for the diagnosis of ectopic pregnancy.

Multistage Detection of Tetrodotoxin Traces in Diodon hystrix Collected in El Salvador

This study describes a multistage methodology to detect minute amounts of tetrodotoxin in fishes, a plan that may be broadened to include other marine organisms. This methodology was applied to porcupinefish (Diodon hystrix) collected in Punta Chiquirín, El Salvador. A three-stage approach along with post-acquisition processing was employed, to wit: (a) Sample screening by selected reaction monitoring (HPLC-MS/MS-SRM) analyses to quickly identify possible toxin presence via a LC/MS/MS API 3200 system with a triple quadrupole; (b) HPLC-HRFTMS-full scan analyses using an ion trap-Orbitrap spectrometer combined with an MZmine 2-enhanced dereplication-like workflow to collect high-resolution mass spectra; and (c) HPLC-HRMS2 analyses. This is the first time tetrodotoxin has been reported in D. hystrix specimens collected in El Salvador.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

Turning Metabolomics Data Processing from a “Black Box” to a “White Box”

Extracting thousands of metabolic features from liquid chromatography–mass spectrometry (LC–MS)–based metabolomics data is not easy. Although many feature extraction algorithms have been developed over the past few decades, automated feature extraction is still not a “white box” process. For instance, it is challenging to quickly determine the optimal parameters for the best feature extraction outcome. It is also impossible to extract every true metabolic feature. Moreover, there is contamination from false metabolic features of different sources, such as signal noise and in-source fragmentation. Our laboratory has recently developed a suite of bioinformatics tools to address these metabolic peak-picking challenges. The goal is to improve the peak-picking outcome quality, so we can effectively obtain biological information from the metabolomics data.

Addressing big data challenges in mass spectrometry-based metabolomics

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.

Infection with the hepatitis C virus causes viral genotype-specific differences in cholesterol metabolism and hepatic steatosis

Lipids play essential roles in the hepatitis C virus (HCV) life cycle and patients with chronic HCV infection display disordered lipid metabolism which resolves following successful anti-viral therapy. It has been proposed that HCV genotype 3 (HCV-G3) infection is an independent risk factor for hepatocellular carcinoma and evidence suggests lipogenic proteins are involved in hepatocarcinogenesis. We aimed to characterise variation in host lipid metabolism between participants chronically infected with HCV genotype 1 (HCV-G1) and HCV-G3 to identify likely genotype-specific differences in lipid metabolism. We combined several lipidomic approaches: analysis was performed between participants infected with HCV-G1 and HCV-G3, both in the fasting and non-fasting states, and after sustained virological response (SVR) to treatment. Sera were obtained from 112 fasting patients (25% with cirrhosis). Serum lipids were measured using standard enzymatic methods. Lathosterol and desmosterol were measured by gas-chromatography mass spectrometry (MS). For further metabolic insight on lipid metabolism, ultra-performance liquid chromatography MS was performed on all samples. A subgroup of 13 participants had whole body fat distribution determined using in vivo magnetic resonance imaging and spectroscopy. A second cohort of (non-fasting) sera were obtained from HCV Research UK for comparative analyses: 150 treatment naïve patients and 100 non-viraemic patients post-SVR. HCV-G3 patients had significantly decreased serum apoB, non-HDL cholesterol concentrations, and more hepatic steatosis than those with HCV-G1. HCV-G3 patients also had significantly decreased serum levels of lathosterol, without significant reductions in desmosterol. Lipidomic analysis showed lipid species associated with reverse cholesterol transport pathway in HCV-G3. We demonstrated that compared to HCV-G1, HCV-G3 infection is characterised by low LDL cholesterol levels, with preferential suppression of cholesterol synthesis via lathosterol, associated with increasing hepatic steatosis. The genotype-specific lipid disturbances may shed light on genotypic variations in liver disease progression and promotion of hepatocellular cancer in HCV-G3.

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography–mass spectrometry (LC–MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC–MS metabolomics experiments or batches using only the features’ RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Paramounter: Direct Measurement of Universal Parameters To Process Metabolomics Data in a "White Box"

Choosing appropriate data processing parameters is critical in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. The conventional design of experiments (DOE) approach is time-consuming and provides no intuitive explanation why the selected parameters generate the best results. After studying commonly used metabolomics data processing software, this work summarized a set of universal parameters, including mass tolerance, peak height, peak width, and instrumental shift. These universal parameters are shared among different feature extraction programs and are critical to metabolic feature extraction. We then developed Paramounter, an R program that automatically measures these universal parameters from raw LC-MS-based metabolomics data prior to metabolic feature extraction. This is made possible through novel concepts of rank-based intensity sorting, zone of interest, and many others. Paramounter also translates universal parameters to software-specific parameters for data processing in different programs. Applying Paramounter is demonstrated to provide a threefold increase in the extracted metabolites compared to using default parameters in MS-DIAL-based feature extraction. Furthermore, the comparison between Paramounter, AutoTuner, and IPO showed that Paramounter generates 3.7- and 1.6-fold more true positive features than AutoTuner and IPO, respectively. Further validation of Paramounter on 11 datasets covering different sample types, data acquisition modes, and MS vendors proved that Paramounter is a convenient and robust program. Overall, the proposed universal parameters and the development of Paramounter address a critical need in metabolomics data processing, transforming metabolomics feature extraction from a "black box" to a "white box." Paramounter is freely available on GitHub (https://github.com/HuanLab/Paramounter).

Assessment of XCMS Optimization Methods with Machine-Learning Performance

The metabolomics field is under rapid development. In particular, biomarker identification and pathway analysis are growing, as untargeted metabolomics is usable for discovery research. Frequently, new processing and statistical strategies are proposed to accommodate the increasing demand for robust and standardized data. One such algorithm is XCMS, which processes raw data into integrated peaks. Multiple studies have tried to assess the effect of optimizing XCMS parameters, but it is challenging to quantify the quality of the XCMS output. In this study, we investigate the effect of two automated optimization tools (Autotuner and isotopologue parameter optimization (IPO)) using the prediction power of machine learning as a proxy for the quality of the data set. We show that optimized parameters outperform default XCMS settings and that manually chosen parameters by liquid chromatography-mass spectrometry (LC-MS) experts remain the best. Finally, the machine-learning approach of quality assessment is proposed for future evaluations of newly developed optimization methods because its performance directly measures the retained signal upon preprocessing.

Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics

Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e., computational variation) besides the well-recognized analytical and biological variations. In this work, using multiple biological samples, we investigated how experimental factors, including sample concentrations, LC separation columns, and data processing programs, contribute to computational variation. For example, we found that the peak height (PH)-based quantification is more precise when MS-DIAL was used for data processing. We further systematically compared the different patterns of computational variation between PH- and peak area (PA)-based quantitative measurements. Our results suggest that the magnitude of computational variation is highly consistent at a given concentration. Hence, we proposed a quality control (QC) sample-based correction workflow to minimize computational variation by automatically selecting PH or PA-based measurement for each intensity value. This bioinformatic solution was demonstrated in a metabolomic comparison of leukemia patients before and after chemotherapy. Our novel workflow can be effectively applied on 652 out of 915 metabolic features, and over 31% (206 out of 652) of corrected features showed distinctly changed statistical significance. Overall, this work highlights computational variation, a considerable but underinvestigated quantitative variability in omics-scale quantitative analyses. In addition, the proposed bioinformatic solution can minimize computational variation, thus providing a more confident statistical comparison among biological groups in quantitative metabolomics.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

HPLC-MS-based design of experiments approach on cocoa roasting

Modern statistical methods, such as the design of experiments and response surface methodology, are widely used to describe changes in multiparameter processes during the processing of food in both science and technology contexts. However, these approaches are described to a lesser degree in the case of cocoa roasting than other foods and processes. Our study aimed to use the design of experiments to establish a model of cocoa roasting for relevant flavor-related constituents. We have used HPLC-MS techniques to link standard process parameters with chemical compounds changing in concentration during cocoa roasting. Influence of time, temperature, the addition of water, acid, and base, on relative concentrations of procyanidin monomers, dimers, and trimers, an Amadori compound, and a peptide, was shown. High-quality models for each compound were established and validated, displaying good prediction accuracy. Such an approach could be used to optimize processing conditions for cocoa roasting in order to influence the concentration of certain chemical compounds, and in turn, improving the flavor of chocolate products.

UPLC-Q-TOF/MS based Untargeted Metabolite and Lipid Analysis on Premature Ovarian Insufficiency Plasma Samples

Background:

Metabolomics is one of the main areas to understand cellular process at molecular level by analyzing metabolites. In recent years metabolomics has emerged as a key tool to understand molecular basis of diseases, to find diagnostic and prognostic biomarkers and develop new treatment opportunities and drug molecules.

Objective:

In this study, untargeted metabolite and lipid analysis were performed to identify potential biomarkers on premature ovarian insufficiency plasma samples. 43 POI subject plasma samples were compared with 32 healthy subject plasma samples.

Methods:

Plasma samples were pooled and extracted using chloroform:methanol:water (3:3:1 v/v/v) mixture. Agilent 6530 LC/MS Q-TOF instrument equipped with ESI source was used for analysis. A C18 column (Agilent Zorbax 1.8 μM, 50 x 2.1 mm) was used for separation of the metabolites and lipids. XCMS, an “R software” based freeware program, was used for peak picking, grouping and comparing the findings. Isotopologue Parameter Optimization (IPO) software was used to optimize XCMS parameters. The analytical methodology and data mining process were validated according to the literature.

Results:

83 metabolite peaks and 213 lipid peaks were found to be in semi-quantitatively and statistically different (fold change >1.5, p <0.05) between the POI plasma samples and control subjects.

Conclusion:

According to the results, two groups were successfully separated through principal component analysis. Among the peaks, phenyl alanine, decanoyl-L-carnitine, 1-palmitoyl lysophosphatidylcholine and PC(O-16:0/2:0) were identified through auto MS/MS and matched with human metabolome database and proposed as plasma biomarker for POI and monitoring the patients in treatment period.

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.

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

patRoon: open source software platform for environmental mass spectrometry based non-target screening

Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon, a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition, patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers.

Polycystic ovary syndrome in adolescents: Q-TOF LC/MS analysis of human plasma metabolome

Polycystic ovary syndrome (PCOS) is a hormonal disorder common among women of reproductive age. Women with PCOS may have infrequent or prolonged menstrual periods or excess male hormone levels. Metabolomics provide information on early biochemical changes in patients. Our aim was to find potential biomarkers on metabolome level to notice PCOS in adolescents and propose treatment opportunities based on our findings on metabolome level. In this study, Q-TOF LC/MS based analysis of the plasma samples of 15 healthy adolescents as control group (Group C) were compared with the plasma samples of 15 adolescents having PCOS (Group T). Raw chromatograms were processed on XCMS using Isotopologue Parameter Optimization (IPO) to optimize XCMS parameters. Finally, 2288 peaks were found but 84 of them had fold changes >1.5 based on normalized peak areas and they were statistically different (p < 0.05) between the groups. These peaks were subjected to MetaboAnalyst 4.0 - MS Peaks to Pathways utility for putative identification. The final list based on putative identification were evaluated through a clinical perspective and the statistically proved variation on the metabolite profiles of Group T and Group C presented that PCOS directly affected the lipid metabolism in the body or occurred as a result of a deformation in the lipid metabolism. Lower amount of Gamma-Tocopherol and higher amount of Coenzyme Q9, which is a product of incomplete Coenzyme Q10 biosynthesis, in the plasma samples of adolescent PCOS patients encouraged us to suggest larger randomized placebo controlled studies for Gamma-Tocopherol and Coenzyme Q10 supplements on the disease situation since our findings on metabolome level were in an accordance with the previous clinical findings.

Quality-by-Design Is a Tool for Quality Assurance in the Assessment of Enantioseparation of a Model Active Pharmaceutical Ingredient

The design of experiments (DoE) is one of the quality-by-design tools valued in analytical method development, not only for cost reduction and time effectiveness, but also for enabling analytical method control and understanding via a systematic workflow, leading to analytical methods with built-in quality. This work aimed at using DoE to enhance method understanding for a developed UHPLC enantioseparation of terbutaline (TER), a model chiral drug, and to define quality assurance parameters associated with using chiral mobile phase additives (CMPA). Within a response surface methodology workflow, the effect of different factors on both chiral resolution and retention was screened and optimized using Plackett-Burman and central composite designs, respectively, followed by multivariate mathematical modeling. This study was able to delimit method robustness and elucidate enantiorecognition mechanisms involved in interactions of TER with the chiral modifiers. Among many CMPAs, successful TER enantioresolution was achieved using hydroxypropyl β-cyclodextrin (HP-β-CD) added to the mobile phase as 5.4 mM HP-β-CD in 52.25 mM ammonium acetate. Yet, limited method robustness was observed upon switching between the different tested CMPA, concluding that quality can only be assured with specific minimal pre-run conditioning time with the CMPA, namely 16-column volume (60 min at 0.1 mL/min). For enantiorecognition understanding, computational molecular modeling revealed hydrogen bonding as the main binding interaction, in addition to dipole-dipole inside the CD cavity for the R enantiomer, while the S enantiomer was less interactive.

Optimized hidden target screening for very polar molecules in surface waters including a compound database inquiry

Highly polar trace organic compounds, which are persistent, mobile, and toxic (PMT) or are very persistent and very mobile (vPvM) in the aquatic environment, may pose a risk to surface water, ground water, and drinking water supplies. Despite the advances in liquid chromatography-mass spectrometry, there often exists an analytical blind spot when it comes to very polar chemicals. This study seeks to make a broad polarity range analytically accessible by means of serially coupling reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) to high-resolution mass spectrometry (HRMS). Moreover, a workflow is presented using optimized data processing of nontarget screening (NTS) data and subsequently generating candidate lists for the identification of very polar molecules via an open-access NTS platform and implemented compound database. First, key input parameters and filters of the so-called feature extraction algorithms were identified, and numerical performance indicators were defined to systematically optimize the data processing method. Second, all features from the very polar HILIC elution window were uploaded to the STOFF-IDENT database as part of the FOR-IDENT open-access NTS platform, which contains additional physicochemical information, and the features matched with potential compounds by their accurate mass. The hit list was filtered for compounds with a negative log D value, indicating that they were (very) polar. For instance, 46 features were assigned to 64 candidate compounds originating from a set of 33 samples from the Isar river in Germany. Three PMT candidates (e.g., guanylurea, melamine, and 1,3-dimethylimidazolidin-2-one) were illustratively validated using the respective reference standards. In conclusion, these findings demonstrate that polarity-extended chromatography reproducibly retards and separates (very) polar compounds from surface waters. These findings further indicate that a transparent and robust data processing workflow for nontarget screening data is available for addressing new (very) polar substances in the aqueous environment.

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods

Non-targeted mass spectrometry (MS) has become an important method over recent years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of non-targeted data sets, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection and refinement of it is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap-filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of the converted mass spectrometry files, intermediate processing files and the central file containing a table with all important information for the classified peaks.

Optimization of ion trajectories in a dynamically harmonized Fourier-transform ion cyclotron resonance cell using a design of experiments strategy

RATIONALE

With the recent introduction of the dynamically harmonized Fourier-transform ion cyclotron resonance (FT-ICR) cell, the complexity of tuning has expanded drastically, and fine-tuning of the direct current voltages is required to optimize the ion cloud movement. As this adjustment must typically be performed manually, more reliable computational methods would be useful.

METHODS

Here we propose a computational method based on a design of experiments (DoE) strategy to overcome the limits of classical manual tuning. This DoE strategy was exemplarily applied on a 12 T FT-ICR instrument equipped with a dynamically harmonized ICR cell. The chemometric approach, based on a central composite face (CCF) design, was first applied to a reference material (sodium trifluoroacetate) allowing for the evaluation of the primary cell parameters. Eight factors related to shimming and gating were identified. The summed intensity of the signal corresponding to the even harmonics was defined as one quality criterion.

RESULTS

The DoE response allowed for rapid and complete mapping of cell parameters resulting in an optimized parameter set. The new set of cell parameters was applied to the study of an ultra-complex sample: Tholins, an ultra-complex mixture that mimics the haze present on Titan, was chosen. We observed a substantial improvement in mass spectrometric performance. The sum of signals related to harmonics was decreased by a factor of three (from 4% for conventional tuning to 1.3%). Furthermore, the dynamic range was also increased, which in turn led to an increase in attributed peaks by 13%.

CONCLUSIONS

This computational procedure based on an experimental design can be applied to any other mass spectrometric parameter optimization problem. This strategy will lead to a more transparent and data-driven method development.

AutoTuner: High Fidelity and Robust Parameter Selection for Metabolomics Data Processing

Untargeted metabolomics experiments provide a snapshot of cellular metabolism but remain challenging to interpret due to the computational complexity involved in data processing and analysis. Prior to any interpretation, raw data must be processed to remove noise and to align mass-spectral peaks across samples. This step requires selection of dataset-specific parameters, as erroneous parameters can result in noise inflation. While several algorithms exist to automate parameter selection, each depends on gradient descent optimization functions. In contrast, our new parameter optimization algorithm, AutoTuner, obtains parameter estimates from raw data in a single step as opposed to many iterations. Here, we tested the accuracy and the run-time of AutoTuner in comparison to isotopologue parameter optimization (IPO), the most commonly used parameter selection tool, and compared the resulting parameters’ influence on the properties of feature tables after processing. We performed a Monte Carlo experiment to test the robustness of AutoTuner parameter selection and found that AutoTuner generated similar parameter estimates from random subsets of samples. We conclude that AutoTuner is a desirable alternative to existing tools, because it is scalable, highly robust, and very fast (∼100–1000× speed improvement from other algorithms going from days to minutes). AutoTuner is freely available as an R package through BioConductor.

Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time

This paper presents a framework for optimizing injection molding process parameters for minimum product cycle time subjected to constraints on the product defects. Two product defects, namely, volumetric shrinkage and warpage, as well as seven process parameters including injection speed, injection pressure, cooling time, packing pressure, mold temperature, packing time, and melt temperature, were considered. Injection molding experiments were conducted on specifically chosen test points and results were used to compute the volumetric shrinkage and warpage (at each test point). Thereafter, three relationships between the product cycle time (one relationship), the two product defects (two relationships), and the injection molding parameters were constructed using the kriging technique. An optimization problem to minimize the product cycle time (described by the first relationship) subject to constraints on the product defects (described by the latter two relationships) was then formulated. A combination set of points between the lower and upper extreme values of acceptable product defect was generated to serve as constraints for the two product defects. The optimization problem was then solved using the Fmincon function, available in the Matlab optimization toolbox. A plot of the optimization results revealed an appreciable tradeoff between the cycle time and the two product defects. To validate the optimization, an additional injection molding experiment was conducted for one of the optimization results. Results from the additional experiment showed reasonably close agreement with simulation optimization results differing in the cycle time, the warpage and volumetric shrinkage by 6.7%, 3.2%, and 8%, respectively.

Development of an LC-MS multivariate nontargeted methodology for differential analysis of the peptide profile of Asian hornet venom (Vespa velutina nigrithorax): application to the investigation of the impact of collection period variation

Insect venom is a highly complex mixture of bioactive compounds, containing proteins, peptides, and small molecules. Environmental factors can alter the venom composition and lead to intraspecific variation in its bioactivity properties. The investigation of discriminating compounds caused by variation impacts can be a key to manage sampling and explore the bioactive compounds. The present study reports the development of a peptidomic methodology based on UHPLC-ESI-QTOF-HRMS analysis followed by a nontargeted multivariate analysis to reveal the profile variance of Vespa velutina venom collected in different conditions. The reliability of the approach was enhanced by optimizing certain XCMS data processing parameters and determining the sample peak threshold to eliminate the interfering features. This approach demonstrated a good repeatability and a criterion coefficient of variation (CV) > 30% was set for deleting nonrepeatable features from the matrix. The methodology was then applied to investigate the impact of collection period variation. PCA and PLS-DA models were used and validated by cross-validation and permutation tests. A slight discrimination was found between winter and summer hornet venom in two successive years with 10 common discriminating compounds. Graphical abstract.

Chemometrics and Related Fields in Python

The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

doepipeline: a systematic approach to optimizing multi-level and multi-step data processing workflows

BACKGROUND

Selecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration for each tool in the pipeline. Because of the complexity and difficulty of predicting outcomes, in practice parameters are often left at default settings or set based on personal or peer experience obtained in a trial and error fashion. To allow for the reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed.

RESULTS

We present doepipeline, a novel approach to optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently spans the entire search space, then optimized in the subsequent phase using response surface designs and OLS modeling. Doepipeline was used to optimize parameters in four use cases; 1) de-novo assembly, 2) scaffolding of a fragmented genome assembly, 3) k-mer taxonomic classification of Oxford Nanopore Technologies MinION reads, and 4) genetic variant calling. In all four cases, doepipeline found parameter settings that produced a better outcome with respect to the characteristic measured when compared to using default values. Our approach is implemented and available in the Python package doepipeline.

CONCLUSIONS

Our proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation in doepipeline makes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code of doepipeline is available at https://github.com/clicumu/doepipeline and it can be installed through conda-forge.

Mass Spectrometry-Based Non-targeted Metabolic Profiling for Disease Detection: Recent Developments

Mass spectrometry (MS) plays an important role in seeking biomarkers for disease detection. High-quality quantitative data is needed for accurate analysis of metabolic perturbations in patients. This article describes recent developments in MS-based non-targeted metabolomics research with applications to the detection of several major common human diseases, focusing on study cohorts, MS platforms utilized, statistical analyses and discriminant metabolite identification. Potential disease biomarkers recently discovered for type 2 diabetes, cardiovascular disease, hepatocellular carcinoma, breast cancer and prostate cancer through metabolomics are summarized, and limitations are discussed.

A new optimization strategy for MALDI FTICR MS tissue analysis for untargeted metabolomics using experimental design and data modeling

Ultra-high-resolution imaging mass spectrometry using matrix-assisted laser desorption ionization (MALDI) MS coupled to a Fourier transform ion cyclotron resonance (FTICR) mass analyzer is a powerful technique for the visualization of small molecule distribution within biological tissues. The FTICR MS provides ultra-high resolving power and mass accuracy that allows large molecular coverage and molecular formula assignments, both essential for untargeted metabolomics analysis. These performances require fine optimizations of the MALDI FTICR parameters. In this context, this study proposes a new strategy, using experimental design, for the optimization of ion transmission voltages and MALDI parameters, for tissue untargeted metabolomics analysis, in both positive and negative ionization modes. These experiments were conducted by assessing the effects of nine factors for ion transmission voltages and four factors for MALDI on the number of peaks, the weighted resolution, and the mean error within m/z 150-1000 mass range. For this purpose, fractional factorial designs were used with multiple linear regression (MLR) to evaluate factor effects and to optimize parameter values. The optimized values of ion transmission voltages (RF amplitude TOF, RF amplitude octopole, frequency transfer optic, RF frequency octopole, deflector plate, funnel 1, skimmer, funnel RF amplitude, time-of-flight, capillary exit), MALDI parameters (laser fluence, number of laser shots), and detection parameters (data size, number of scans) led to an increase of 32% and 18% of the number of peaks, an increase of 8% and 39% of the resolution, and a decrease of 56% and 34% of the mean error in positive and negative ionization modes, respectively. Graphical abstract.

Automated optimization of XCMS parameters for improved peak picking of liquid chromatography-mass spectrometry data using the coefficient of variation and parameter sweeping for untargeted metabolomics

Accurate peak picking and further processing is a current challenge in the analysis of untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) data. The optimization of these processes is crucial to obtain proper results. This study investigated and optimized the detection of peaks by XCMS, a widely used R package for peak picking and processing of high-resolution LC-MS metabolomics data by their coefficient of variation using neat standard solutions of drug like compounds. The obtained results were additionally verified by using fortified pooled plasma samples. Settings of the mass spectrometer were optimized by recommendations in literature to enable a reliable detection of the investigated analytes. XCMS parameters were evaluated using a comprehensive parameter sweeping approach. The optimization steps were statistically evaluated and further visualized after principal component analysis (PCA). Concerning the lower concentrated solution in methanol samples, the optimization of both mass spectrometer and XCMS parameters improved the median coefficient of variation from 24% to 7%, retention time fluctuation from 9.3 seconds to 0.54 seconds, and fluctuation of the mass to charge ratio (m/z) from m/z 0.00095 to m/z 0.00028. The number of parent compounds and their related species annotated by CAMERA increased from 88 to 113 while the total amount of features decreased from 3282 to 428. Optimized MS settings such as increased resolution led to a higher specificity of peak picking. PCA supported these findings by showing the best clustering of samples after optimization of both mass spectrometer and XCMS parameters. The results implied that peak picking needs to be individually adapted for the experimental set up. Reducing unwanted variation in the data set was most successful after combining high resolving power with strict peak picking settings.

Application of metabolomics in toxicity evaluation of traditional Chinese medicines

Traditional Chinese medicines (TCM) have a long history of use because of its potential complementary therapy and fewer adverse effects. However, the toxicity and safety issues of TCM have drawn considerable attention in the past two decades. Metabolomics is an “omics” approach that aims to comprehensively analyze all metabolites in biological samples. In agreement with the holistic concept of TCM, metabolomics has shown great potential in efficacy and toxicity evaluation of TCM. Recently, a large amount of metabolomic researches have been devoted to exploring the mechanism of toxicity induced by TCM, such as hepatotoxicity, nephrotoxicity, and cardiotoxicity. In this paper, the application of metabolomics in toxicity evaluation of bioactive compounds, TCM extracts and TCM prescriptions are reviewed, and the potential problems and further perspectives for application of metabolomics in toxicological studies are also discussed.

Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics

Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.

Global omics strategies to investigate the effect of cyclodextrin nanoparticles on MCF-7 breast cancer cells

Cyclodextrins (CD) are natural macrocyclic oligosaccharides linked by α(1,4) glycosidic bonds. Hydrophobic cavity of CDs are able to incorporate small molecules, ions, macromolecules which makes them excellent delegates for forming nanoparticulate carriers upon chemical modification to render amphiphilicity to CDs. In this study, blank 6OCaproβCD nanoparticle was prepared and administered to MCF-7 breast cancer cells. The effects of these nanoparticles on the cells were investigated in depth through biochemical and proteomic tests following 48 h of incubation. Proteomics studies revealed that apoptosis-related protein levels of hnRNP and CBX1 were increased while HDGF was not affected supporting the idea that 6OCaproβCD nanoparticles prevent cell proliferation. Gene expression studies were generally in correlation with protein levels since gene expression was significantly stimulated while protein levels were lower compared to the control group suggesting that a post-transcriptional modification must have occurred. Furthermore, 6OCaproβCD was observed to not trigger multidrug resistance as proved with RT-PCR that effectuates another exquisite characteristic of 6OCaproβCD nanoparticle as carrier of chemotherapeutic drugs. Metabolomic pathways of CD effect on MCF7 cells were elucidated with HMDB as serine biosynthesis, transmembrane transport of small molecules, metabolism of steroid hormones, estrogen biosynthesis and phospholipid biosynthesis. In conclusion, 6OCaproβCD is a promising nanoparticulate carrier for chemotherapeutic drugs with intrinsic apoptotic effect to be employed in treatment of breast cancer and further studies should be conducted in order to comprehend the exact mechanism of action.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

With Guide of Spike-in Experiment for Optimizing Workflow of LC-MS data Processing in Metabolomics

A systematical study was performed to investigate the processing workflow of LC-MS-based metabolomics data by optimizing parameter settings in XCMS software and comparing different preprocessing methods. Here we use a spike-in experiment combining with design of experiment (DoE) approaches for optimizing XCMS software parameters. A trusted index, which was based on accuracy evaluation of the spike-in data, was employed to assess the optimizing process. After optimizing the XCMS setting, the trusted index was improved from 3.67 to 30 and positive rate of spike-in standards also increased from 20% to 100%. Moreover, different data preprocessing methods, such as normalization, different scaling methods were also investigated on spike-in data since they were found to affect the outcome of the data analysis and ions features identification. Accordingly, UN-normalization and Pareto scaling were chosen as appropriate preprocessing methods to deal with LC-MS data through the evaluation of match index (mainly applied multivariate statistics methods). Finally, the optimized workflow was applied to experimental samples that acquired from metabolomics experiment and analyzed randomly with spike-in sample, which indicated a better applicability in formal metabolomics experiment. It is concluded that the proposed data processing workflow could be used as feasible approach for improving the quality of LC-MS-based metabolomics data and ensured the veracity of metabolites identification in data processing procedures to a certain extent.

An overview of experimental designs in HPLC method development and validation

Chemometric approaches have been increasingly viewed as precious complements to high performance liquid chromatographic practices, since a large number of variables can be simultaneously controlled to achieve the desired separations. Moreover, their applications may efficiently identify and optimize the significant factors to accomplish competent results through limited experimental trials. The present manuscript discusses usefulness of various chemometric approaches in high and ultra performance liquid chromatography for (i) methods development from dissolution studies and sample preparation to detection, considering the progressive substitution of traditional detectors with tandem mass spectrometry instruments and the importance of stability indicating assays (ii) method validation through screening and optimization designs. Choice of appropriate types of experimental designs so as to either screen the most influential factors or optimize the selected factors' combination and the mathematical models in chemometry have been briefly recalled and the advantages of chemometric approaches have been emphasized. The evolution of the design of experiments to the Quality by Design paradigm for method development has been reviewed and the Six Sigma practice as a quality indicator in chromatography has been explained. Chemometric applications and various strategies in chromatographic separations have been described.

Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing

Untargeted metabolomics by liquid chromatography-mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson's correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website ( www.metabolomicsworkbench.org ), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu .

Designing Successful Proteomics Experiments

Because proteomics experiments are so complex they can readily fail, and do so without clear cause. Using standard experimental design techniques and incorporating quality control can greatly increase the chances of success. This chapter introduces the relevant concepts and provides examples specific to proteomic workflows. Applying these notions to design successful proteomics experiments is straightforward. It can help identify failure causes and greatly increase the likelihood of inter-laboratory reproducibility.

Comprehensive Optimization of LC-MS Metabolomics Methods Using Design of Experiments (COLMeD)

INTRODUCTION

Both reverse-phase and HILIC chemistries are deployed for liquid-chromatography mass spectrometry (LC-MS) metabolomics analyses, however HILIC methods lag behind reverse-phase methods in reproducibility and versatility. Comprehensive metabolomics analysis is additionally complicated by the physiochemical diversity of metabolites and array of tunable analytical parameters.

OBJECTIVE

Our aim was to rationally and efficiently design complementary HILIC-based polar metabolomics methods on multiple instruments using Design of Experiments (DoE).

METHODS

We iteratively tuned LC and MS conditions on ion-switching triple quadrupole (QqQ) and quadrupole-time-of-flight (qTOF) mass spectrometers through multiple rounds of a workflow we term COLMeD (Comprehensive optimization of LC-MS metabolomics methods using design of experiments). Multivariate statistical analysis guided our decision process in the method optimizations.

RESULTS

LC-MS/MS tuning for the QqQ method on serum metabolites yielded a median response increase of 161.5% (p<0.0001) over initial conditions with a 13.3% increase in metabolite coverage. The COLMeD output was benchmarked against two widely used polar metabolomics methods, demonstrating total ion current increases of 105.8% and 57.3%, with median metabolite response increases of 106.1% and 10.3% (p<0.0001 and p<0.05 respectively). For our optimized qTOF method, 22 solvent systems were compared on a standard mix of physiochemically diverse metabolites, followed by COLMeD optimization, yielding a median 29.8% response increase (p<0.0001) over initial conditions.

CONCLUSIONS

The COLMeD process elucidated response tradeoffs, facilitating improved chromatography and MS response without compromising separation of isobars. COLMeD is efficient, requiring no more than 20 injections in a given DoE round, and flexible, capable of class-specific optimization as demonstrated through acylcarnitine optimization within the QqQ method.

Optimization of LC-Orbitrap-HRMS acquisition and MZmine 2 data processing for nontarget screening of environmental samples using design of experiments

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a well-established technique for nontarget screening of contaminants in complex environmental samples. Automatic peak detection is essential, but its performance has only rarely been assessed and optimized so far. With the aim to fill this gap, we used pristine water extracts spiked with 78 contaminants as a test case to evaluate and optimize chromatogram and spectral data processing. To assess whether data acquisition strategies have a significant impact on peak detection, three values of MS cycle time (CT) of an LTQ Orbitrap instrument were tested. Furthermore, the key parameter settings of the data processing software MZmine 2 were optimized to detect the maximum number of target peaks from the samples by the design of experiments (DoE) approach and compared to a manual evaluation. The results indicate that short CT significantly improves the quality of automatic peak detection, which means that full scan acquisition without additional MS² experiments is suggested for nontarget screening. MZmine 2 detected 75-100 % of the peaks compared to manual peak detection at an intensity level of 10⁵ in a validation dataset on both spiked and real water samples under optimal parameter settings. Finally, we provide an optimization workflow of MZmine 2 for LC-HRMS data processing that is applicable for environmental samples for nontarget screening. The results also show that the DoE approach is useful and effort-saving for optimizing data processing parameters. Graphical Abstract ᅟ.

Discrimination of Polygonatum species and identification of novel markers using (1) H NMR- and UPLC/Q-TOF MS-based metabolite profiling

BACKGROUND

Rhizomes of Polygonatum species are commonly used as herbal supplements in Asia. They have different medicinal effects by species but have been misused and mixed owing to their similar taste and smell. Therefore accurate and reliable analytical methods to discriminate between Polygonatum species are required.

RESULTS

In this study, global and targeted metabolite profiling using (1) H nuclear magnetic resonance ((1) H NMR) spectroscopy and ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS) was applied to discriminate between different Polygonatum species. Partial least squares discriminant analysis (PLS-DA) models were used to classify and predict species of Polygonatum. Cross-validation derived from PLS-DA revealed good predictive accuracy. Polygonatum species were classified into unique patterns based on K-means clustering analysis. 4-Hydrobenzoic acid and trigonelline were identified as novel marker compounds and quantified accurately.

CONCLUSION

The results demonstrate that metabolite profiling approaches coupled with chemometric analysis can be used to classify and discriminate between different species of various herbal medicines. © 2015 Society of Chemical Industry.