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.

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MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

■ CONTENTS Established Concepts of Quantification in Metabolomics/Lipidomics 521 Recommended Absolute Quantification Approaches 521 Stable Isotope Labeling 522 Enrichment Degree and Isotopologue Distribution 522 Suite of In Vivo Synthesized Isotopically Labeled Materials 523 Applications of Stable Isotope Labeled Biomass 523 Credentialing: Isotopically Labeled Biomass for Identification 523 Isotopically Labeled Biomass for Validated Isotopologue Distribution Elucidations 524 Isotopically Labeled Biomass for Quantification 524 Harmonization and Reference Materials 526 Reference Materials and Interlaboratory Comparisons 526 Community-Based Guidelines in Metabolomics 526 Quality Control and Benchmarking 527 Nontargeted Data AnalysisIncreasing Quality by Multiple Lines of Evidence 527 Data Preprocessing 527 Elimination of Redundancies and Noise from Feature Tables 528 The Annotation Task 528 Annotation in the Field of Lipidomics 529 Retention Time and Cross Section as Orthogonal Parameter in Nontargeted Analysis 529 Retention Time for Annotation 529 Collision Cross Section Value for Compound Annotation 530 Navigating the Conflicting Goals of Metabolome Coverage and Throughput 530 Sample Preparation 530 Direct Analysis in Metabolomics and Lipidomics 531 Flow Injection-MS 531 Ultimate Throughput−Duty Cycles of Seconds Per Sample 531 ChromatographyKey Steps Toward Coverage and Throughput 532 Miniaturization of Liquid Chromatography 532 Multidimensional Chromatography 533 Dual/Parallel Chromatography 534 MS Platforms and Data Acquisition Strategies Improving Coverage, Selectivity, and Reliability 534 Toward MS-Based Multi-omics 534 Multi-omics Sample Preparation Strategies 534 Multiplatform Analysis Strategies for Metabolomics and Lipidomics 535 Merging Metabolomics and Lipidomics 536 Network Analysis and Visualization of Multiomics Workflows 536 Conclusions 538 Author Information 538 Corresponding Author 538 Authors 538 Notes 538 Biographies 538 Acknowledgments 539 References 539

Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput.

The field of high-resolution mass spectrometry has undergone a rapid progress in the last years due to instrumental improvements leading to a higher sensitivity and selectivity of instruments. A variety of qualitative screening approaches, summarized as nontarget screening, have been introduced and have successfully extended the environmental monitoring of organic micropollutants. Several automated data processing workflows have been developed to handle the immense amount of data that are recorded in short time frames by these methods. Most data processing workflows include similar steps, but underlying algorithms and implementation of different processing steps vary. In this study the consistency of data processing with different software tools was investigated. For this purpose, the same raw data files were processed with the software packages MZmine2, enviMass, Compound Discoverer, and XCMS online and resulting feature lists were compared. Results show a low coherence between different processing tools, as overlap of features between all four programs was around 10%, and for each software between 40% and 55% of features did not match with any other program. The implementation of replicate and blank filter was identified as one of the sources of observed divergences. However, there is a need for a better understanding and user instructions on the influence of different algorithms and settings on feature extraction and following filtering steps. In future studies it would be of interest to investigate how final data interpretation is influenced by different processing software. With this work we want to encourage more awareness on data processing as a crucial step in the workflow of nontarget screening.

Comparison of Software Tools for Liquid Chromatography-High-Resolution Mass Spectrometry Data Processing in Nontarget Screening of Environmental Samples.

MMEASE: Online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis.

Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the gold-standard technique to determine the full chemical diversity in biological samples However, this approach still has many limitations; notably, the difficulty of accurately estimating the number of unique metabolites profiled among the thousands of MS ion signals arising from chromatograms Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER suite, which tackles feature degeneracy and improves annotation rates We show that implementation of MS-CleanR reduces the number of signals by nearly 80% while retaining 95% of unique metabolite features Moreover, the annotation results from MS-FINDER can be ranked according to the database chosen by the user, which enhance identification accuracy Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions fostered class separation resulting from multivariate data analysis and led to annotation of 75% of the final features The full workflow was applied to metabolomic profiles from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches A group of glycosylated triterpenoids overrepresented in resistant lines were identified as candidate compounds conferring pathogen resistance MS-CleanR is implemented through a Shiny interface for intuitive use by end-users (available at https://githubcom/eMetaboHUB/MS-CleanR)

MS-CleanR: A Feature-Filtering Workflow for Untargeted LC-MS Based Metabolomics.

Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection

Corrigendum to "Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection" [ACA 1029, (2018) 50-57].

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Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection

Corrigendum to "Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection" [ACA 1029, (2018) 50-57].