Impact of three different peak picking software tools on the quality of untargeted metabolomics data.

Data quality and control parameters are becoming more important in metabolomics. For peak picking, open-source or commercial solutions are used. Other publications consider different software solutions or data acquisition types for peak picking, a combination, including proposed and new quality parameters for the process of peak picking, does not exist. This study tries to examine the performance of three different software in terms of reproducibility and quality of their output while also considering new quality parameters to gain a better understanding of resulting feature lists in metabolomics data. We saw best recovery of spiked analytes in MS-DIAL. Reproducibility over multiple projects was good among all software. The total number of features found was consistent for DDA and full scan acquisition in MS-DIAL but full scan data leading to considerably more features in MZmine and Progenesis Qi. Feature linearity proved to be a good quality parameter. Features in MS-DIAL and MZmine, showed good linearity while Progenesis Qi produced large variation, especially in full scan data. Peak width proved to be a very powerful filtering criteria revealing many features in MZmine and Progenesis Qi to be of questionable peak width. Additionally, full scan data appears to produce a disproportionally higher number of short features. This parameter is not yet available in MS-DIAL. Finally, the manual classification of true positive features proved MS-DIAL to perform significantly better in DDA data (62 % true positive) than the two other software in either mode. We showed that currently popular solutions MS-DIAL and MZmine perform well in targeted analysis of spiked analytes as well as in classic untargeted analysis. The commercially available solution Progenesis Qi does not hold any advantage over the two in terms of quality parameters, of which we proposed peak width as a new parameter and showed that already proposed parameters such as feature linearity in samples of increasing concentration are advisable to use.

Identification of Stress-Responsive Metabolites in Plants Using an Untargeted Metabolomics Approach.

Stress can affect different groups of plant metabolites and multiple signaling pathways. Untargeted metabolomics enables the collection of whole-spectrum data for the entire metabolite content present in plant tissues at that point in time. We present a thorough approach for large-scale, untargeted metabolomics of plant tissues using reverse-phase liquid chromatography connected to high-resolution mass spectrometry (LC-MS) of dilute methanolic extract. MZmine is a specialized computer software that automates the alignment and baseline modification of all derived mass peaks across all samples, resulting in precise information on the relative abundance of hundreds of metabolites reflected by thousands of mass signals. Further processing with statistic and bioinformatic techniques will provide a comprehensive perspective of the variations and connections among groups of samples.

Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies.

Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.

How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data.

Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 - score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10-4 and a relative intensity error of 2 × 10-2, adhering to a 5% error threshold (F1 - scores above 95%). For a stricter 1% error threshold (F1 - scores above 99%), an absolute m/z error of 2 × 10-5 and a relative intensity error of 2 × 10-3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.

Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data.

Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention.

Dereplication of calystegines in food plants and wild Solanum Brazilian fruits.

Calystegines are potent glycosidase inhibitors with therapeutic potential and are constituents of food and feed with potential toxic effects. This study aims to target calystegines and other nitrogenous substances in food plants. Hydroalcoholic extracts from Solanum tuberosum, Ipomoea batatas, S. lycocarpum, and fruit from S. lycopersicum, S. aethiopicum, S. paniculatum, S. crinitum, and S. acanthodes were analyzed by liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) using an acidic HILIC column. The dereplication approach included data processing using MZMine2, FBMN-GNPS, and structure elucidation and interpretation of the organized data. The calystegines A3, A5, B2, and C1 were identified, and several potential new calystegine analogues: three may correspond to new calystegines of the A-group, one glycosyl derivative of calystegine A3, and two glycosyl derivatives of the B-group. These findings help to direct the search for new calystegines. In addition, the dereplication approach enabled the annotation of 22 other nitrogen compounds.

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.

Annotation and Identification of Phytochemicals from Eleusine indica Using High-Performance Liquid Chromatography Tandem Mass Spectrometry: Databases-Driven Approach.

Eleusine indica (L.) Gaertn is a perennial herb belonging to the Poaceae family. As the only species of Eleusine found abundantly in Malaysia, it is locally known as "rumput sambau" and has been traditionally used to treat various ailments including pain relief from vaginal bleeding, hastening the placenta delivery after childbirth, asthma, hemorrhoids, urinary infection, fever, and as a tonic for flu-related symptoms. A diverse array of biological activities have been reported for the plant, such as antimicrobial, cytotoxic, anticonvulsant, anti-inflammatory, analgesic, antipyretic, and hepatoprotective action. Despite many reports on its traditional uses and biological activities, limited chemical databases are available for the plant. Thus, the aims of this study were to annotate and identify the phytochemical constituents in the methanolic extract of E. indica through tandem LCMS-based analysis techniques using MZmine, GNPS, Compound Discoverer, and SIRIUS platforms. This technique managed to identify a total of 65 phytochemicals in the extract, comprising primary and secondary metabolites, and was verified by the isolation of one of the identified phytochemicals. The structural elucidation mainly using 1D and 2D NMR as well as comparison with values in the literature confirms the isolated phytochemical to be a 3-OH anomer of loliolide, a benzofuran-type of compound, which consequently increases the level of confidence in the applied technique. The research describes a useful method for the fast and simultaneous identification of phytochemicals in E. indica, contributing to the study of the chemical properties of the genus and family.

Untargeted Metabolomic Profiling of Fungal Species Populations.

This chapter describes protocols for the development of consensus chemical phenotypes or "metabolomes" of fungal populations using ultra-high pressure liquid chromatography coupled to high resolution mass spectrometry (UPLC-HRMS). Isolates are cultured using multiple media conditions to elicit the expression of diverse secondary metabolite biosynthetic gene clusters. The mycelium and spent culture media are extracted using organic solvents and profiled by ultra-high pressure chromatography coupled with a high resolution Thermo Orbitrap XL mass spectrometer with the ability to trap and fragment ions to general MS2 spectra. MS data preprocessing is explained and illustrated using the freely available software MZMine 2. Through data processing, binary matrices of mass features can be generated and then combined into a consensus secondary metabolite phenotype of all isolates grown in all media conditions. The production of consensus chemical phenotypes is useful for screening large fungal populations (both inter and intra-species populations) for isolates potentially expressing novel secondary metabolites or analogs of known secondary metabolites.

Optimization of LC-MS2 Data Acquisition Parameters for Molecular Networking Applied to Marine Natural Products.

Since the introduction of the online open-source GNPS, molecular networking has quickly become a widely applied tool in the field of natural products chemistry, with applications from dereplication, genome mining, metabolomics, and visualization of chemical space. Studies have shown that data dependent acquisition (DDA) parameters affect molecular network topology but are limited in the number of parameters studied. With an aim to optimize LC-MS2 parameters for integrating GNPS-based molecular networking into our marine natural products workflow, a design of experiment (DOE) was used to screen the significance of the effect that eleven parameters have on both Classical Molecular Networking workflow (CLMN) and the new Feature-Based Molecular Networking workflow (FBMN). Our results indicate that four parameters (concentration, run duration, collision energy and number of precursors per cycle) are the most significant data acquisition parameters affecting the network topology. While concentration and the LC duration were found to be the two most important factors to optimize for CLMN, the number of precursors per cycle and collision energy were also very important factors to optimize for FBMN.

Lipidomics of Environmental Microbial Communities. I: Visualization of Component Distributions Using Untargeted Analysis of High-Resolution Mass Spectrometry Data.

Lipids, as one of the main building blocks of cells, can provide valuable information on microorganisms in the environment. Traditionally, gas or liquid chromatography coupled to mass spectrometry (MS) has been used to analyze environmental lipids. The resulting spectra were then processed through individual peak identification and comparison with previously published mass spectra. Here, we present an untargeted analysis of MS1 spectral data generated by ultra-high-pressure liquid chromatography coupled with high-resolution mass spectrometry of environmental microbial communities. Rather than attempting to relate each mass spectrum to a specific compound, we have treated each mass spectrum as a component, which can be clustered together with other components based on similarity in their abundance depth profiles through the water column. We present this untargeted data visualization method on lipids of suspended particles from the water column of the Black Sea, which included >14,000 components. These components form clusters that correspond with distinct microbial communities driven by the highly stratified water column. The clusters include both known and unknown compounds, predominantly lipids, demonstrating the value of this rapid approach to visualize component distributions and identify novel lipid biomarkers.

A strategy for rapid discovery of traceable chemical markers in herbal products using MZmine 2 data processing toolbox: A case of Jing Liqueur.

Objective: The quality evaluation of herbal products remains a big challenge. Traceable markers are the core concept of the authentication of herbal products. However, the discovery of traceable markers is labor-intensive and time-consuming. The aim of this study is to develop a convenient approach to rapidly screen the traceable markers for herbal product authentication. Methods: Commercial Jing Liqueur and its 22 species of herbal ingredients were analyzed using HPLC-QTOF-MS and GC-MS to characterize nonvolatile and volatile chemicals. The acquired data were imported into MZmine 2 software for mass detection, chromatogram building, deconvolution and alignment. The aligned data were exported into a csv file and then traceable markers were selected using the built-in filter function in Excel. Finally, the traceable markers were identified by searching against online databases or publications, some of which were confirmed by reference standards. Results: A total of 288 chemical features transferred from herbal materials to Jing Liqueur product were rapidly screened out. Among them, 52 markers detected by HPLC-QTOF-MS were annotated, while nine volatile markers detected by GC-MS were annotated. Moreover, 30 of these markers were confirmed by comparing with reference standards. A chemical fingerprint consisting of traceable markers was finally generated to ensure the authentication and quality consistency of Jing Liqueur. Conclusion: A strategy for rapid discovery of traceable markers in herbal products using MZmine 2 software was developed.

Metabolomics Data Preprocessing Using ADAP and MZmine 2.

The informatics pipeline for making sense of untargeted LC-MS or GC-MS data starts with preprocessing the raw data. Results from data preprocessing undergo statistical analysis and subsequently mapped to metabolic pathways for placing untargeted metabolomics data in the biological context. ADAP is a suite of computational algorithms that has been developed specifically for preprocessing LC-MS and GC-MS data. It consists of two separate computational workflows that extract compound-relevant information from raw LC-MS and GC-MS data, respectively. Computational steps include construction of extracted ion chromatograms, detection of chromatographic peaks, spectral deconvolution, and alignment. The two workflows have been incorporated into the cross-platform and graphical MZmine 2 framework and ADAP-specific graphical user interfaces have been developed for using ADAP with ease. This chapter summarizes the algorithmic principles underlying key steps in the two workflows and illustrates how to apply ADAP to preprocess LC-MS and GC-MS data.

Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values.

BACKGROUND: Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. RESULTS: LMN uses a ranking system (1-3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine. CONCLUSIONS: LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.

Extraction and cleansing of data for a non-targeted analysis of high-resolution mass spectrometry data of wastewater.

We provide a workflow to extract unidentified signals from chromatography-high resolution mass spectrometry (LC-HRMS) data of wastewater samples as a pre-step of a non-targeted analysis of dissolved organic matter (DOM). We provide detailed methodology on data processing and cleanup using MS processing software MZmine 2 and an own set of functions in R developed for wastewater analysis. The processing involves signal extraction, linear mass correction, reduction of noise, grouping of isotopologues, molecular formula assignment and merging of replicates. The article contains software settings and reasoning behind the choice of data extraction options. The supplementary information contains a script for the correction of signal masses using internal standards and templates of internal standard lists. We included a reproducible example as an R notebook with data cleansing workflow and data exported from MZmine. The data were used according to the described methodology in the article "A non-targeted high-resolution mass spectrometry data analysis of dissolved organic matter in wastewater treatment" by Verkh et al., 2018. •Includes a linear mass correction algorithm for LC-HRMS signals.•Describes a pipeline of non-targeted processing of LC-HRMS data of wastewater using free software.•Provides tests and reasons for parameter choice in non-targeted LC-HRMS wastewater data extraction.

Suspect and untargeted screening of bisphenol S metabolites produced by in vitro human liver metabolism.

Bisphenol S (BPS) is increasingly used as substitute for bisphenol A, resulting in higher potential of human exposure to this compound. Yet, information on the human metabolism of BPS is limited. Hence, current biomonitoring studies rely only on the measurement of BPS itself, leading to a potential underestimation of assessing human exposure to this emerging contaminant. The aims of this study were to investigate the in vitro metabolic pathways of BPS using human liver microsomes and cytosol fractions and propose in vitro metabolites for evaluation in pharmacokinetics studies. Liquid chromatography coupled to quadrupole time-of-flight high-resolution mass spectrometry was used for the screening, identification, and structural elucidation of Phase I and II metabolites of BPS for the first time. Metabolite identification was performed using two complementary workflows: suspect and untargeted screening. Two Phase I metabolites were formed through hydroxylation of the phenolic rings. Four Phase II metabolites were formed through conjugation with glucuronic acid or sulfate. Three of these metabolites, namely dihydroxy-BPS, hydroxy-BPS-glucuronide and hydroxy-BPS-sulfate were identified and structurally elucidated for the first time. As such, we provide an expanded set of in vitro biotransformation products of BPS, which can potentially support a reliable assessment of BPS exposure in future biomonitoring studies.

Shotgun Sphingolipid Analysis of Human Aqueous Humor.

This protocol provides a step-by-step guide to shotgun sphingolipid analysis of aqueous humor. We describe the Bligh and Dyer crude lipid extraction method and electrospray ionization tandem mass spectrometry (ESI-MS/MS) coupled with MZmine 2.21 data processing for identification and ratiometric quantitation of sphingosine, sphingosine-1-phosphate, sphingomyelin, and ceramide.

Bioinformatics Pertinent to Lipid Analysis in Biological Samples.

Electrospray ionization mass spectrometry has revolutionized the way lipids are studied. In this work, we present a tutorial for analyzing class-specific lipid spectra obtained from a triple quadrupole mass spectrometer. The open-source software MZmine 2.21 is used, coupled with LIPID MAPS databases. Here, we describe the steps for lipid identification, ratiometric quantification, and briefly address the differences to the analyses when using direct infusion versus tandem liquid chromatography-mass spectrometry (LC-MS). We also provide a tutorial and equations for quantification of lipid amounts using synthetic lipid standards and normalization to a protein amount.

Post-acquisition spectral stitching. An alternative approach for data processing in untargeted metabolomics by UHPLC-ESI(-)-HRMS.

INTRODUCTION: In the case of the MS-based metabolomics, the large number of false positives remains a fundamental issue. OBJECTIVE: The aim of this study was to develop a new strategy, which highlights the number of the reliable features i.e. the detected features that correspond to a consistent peak according to chromatographic and mass spectrometric criteria. METHOD: For the analysis blood samples from 20 chickens, which were administrated with naringin and 9 samples from control, were analyzed by UHPLC-HRMS (Orbitrap Velos). Two methodologies have been compared for data processing. In the first one (classical approach), all data in the 100-900 m/z mass-to charge range were included for the data processing procedure whereas for the newly developed methodology, the data were shred in 100Da slices generating 8 datasets, which have been then subjected to the downstream MS data processing. Each dataset was treated separately and the m/z_tR features obtained by either VIP's or t-test values were merged and used as the input for the construction of the general model. RESULTS: The new methodology resulted to a 4-fold increase of the peaks that could be considered chromatographically and mass spectrometrically valid. CONCLUSION: A new strategy was reported on the detection of chromatographically reliable features during a metabolomic approach. The shredding of the LC-MS chromatograms into multiple m/z ranges increased the number of the identified chromatographically reliable features.

Lethal and sub-lethal effects of triclosan toxicity to the earthworm Eisenia fetida assessed through GC-MS metabolomics.

Triclosan (TCS) is a ubiquitous contaminant in municipal biosolids, which has also been detected in soils and earthworms sampled from agricultural fields amended with biosolids. The goal of this study was to evaluate the toxicity of TCS to earthworms using a metabolomics-based approach for an improved interpretation of toxicity. Toxicity of TCS was assessed using the OECD Method 207 filter paper contact test measuring the endpoints of weight loss, mortality, and ten metabolites determined by GC-MS. Eight earthworms were exposed as individual replicates to six concentrations of triclosan (0, 0.0001, 0.001, 0.01, 0.1, and 1mg TCS cm-2) on filter paper, with mortality assessed after 6, 24 and 48h. Mortalities were first observed at 24h, with 100% mortality in the 1 and 0.1mgcm-2 treatments. Worms at 1mgcm-2 lost most of their coelomic fluid before they could be sampled. The 48h LC50 for triclosan was estimated to be 0.006 and 0.008mgcm-2 by a linear and logistic model, respectively. Based on the LC50, triclosan is relatively more toxic to earthworms than a number of other emerging contaminants, but is less toxic than other chlorophenols and many pesticides. Alanine, valine, leucine, serine, phenylalanine, putrescine, spermidine, mannitol, and inositol were significantly different between treatments, although changes were most often associated with mortality rather than triclosan exposure. An increase in putrescine and decrease in amino acids, polyols, and spermidine were associated with mortality, suggesting decomposition had begun. Principal components analysis did not reveal evidence of metabolic impacts at sub-lethal concentrations. However, there were changes in the pattern of correlations between metabolite pairs in surviving worms at both 0.0001 and 0.001mgcm-2 exposure compared to the control.