Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

mzMatch-ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data

<p>Motivation: Stable isotope-labelling experiments have recently gained increasing popularity in metabolomics studies, providing unique insights into the dynamics of metabolic fluxes, beyond the steady-state information gathered by routine mass spectrometry. However, most liquid chromatography–mass spectrometry data analysis software lacks features that enable automated annotation and relative quantification of labelled metabolite peaks. Here, we describe mzMatch–ISO, a new extension to the metabolomics analysis pipeline mzMatch.R.</p> <p>Results: Targeted and untargeted isotope profiling using mzMatch–ISO provides a convenient visual summary of the quality and quantity of labelling for every metabolite through four types of diagnostic plots that show (i) the chromatograms of the isotope peaks of each compound in each sample group; (ii) the ratio of mono-isotopic and labelled peaks indicating the fraction of labelling; (iii) the average peak area of mono-isotopic and labelled peaks in each sample group; and (iv) the trend in the relative amount of labelling in a predetermined isotopomer. To aid further statistical analyses, the values used for generating these plots are also provided as a tab-delimited file. We demonstrate the power and versatility of mzMatch–ISO by analysing a 13C-labelled metabolome dataset from trypanosomal parasites.</p&gt

mzMatch-ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data

<p>Motivation: Stable isotope-labelling experiments have recently gained increasing popularity in metabolomics studies, providing unique insights into the dynamics of metabolic fluxes, beyond the steady-state information gathered by routine mass spectrometry. However, most liquid chromatography–mass spectrometry data analysis software lacks features that enable automated annotation and relative quantification of labelled metabolite peaks. Here, we describe mzMatch–ISO, a new extension to the metabolomics analysis pipeline mzMatch.R.</p> <p>Results: Targeted and untargeted isotope profiling using mzMatch–ISO provides a convenient visual summary of the quality and quantity of labelling for every metabolite through four types of diagnostic plots that show (i) the chromatograms of the isotope peaks of each compound in each sample group; (ii) the ratio of mono-isotopic and labelled peaks indicating the fraction of labelling; (iii) the average peak area of mono-isotopic and labelled peaks in each sample group; and (iv) the trend in the relative amount of labelling in a predetermined isotopomer. To aid further statistical analyses, the values used for generating these plots are also provided as a tab-delimited file. We demonstrate the power and versatility of mzMatch–ISO by analysing a 13C-labelled metabolome dataset from trypanosomal parasites.</p&gt

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics.

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics

Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy.

In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important

Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method

Direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry (DI nESI FT-ICR MS)offers high mass accuracy and resolution for analyzing complex metabolite mixtures. High dynamic range across a wide mass range, however, can only be achieved at the expense of mass accuracy, since the large numbers of ions entering the ICR detector induce adverse spacecharge effects. Here we report an optimized strategy for wide-scan DI nESI FT-ICR MS that increases dynamic range but maintains high mass accuracy. It comprises the collection if multiple adjacent selected ion monitoring (SIM) windows that are stitched together using novel algorithms. The final SIM-stitching method, derived from several optimization experiments, comprises 21 adjoining SIM windows each of width m/z 30 (from m/z 70 to 500; adjacent windows overlap by m/z 10) with an automated gain control (AGC) target of 1 105 charges. SIMstitching and wide-scan range (WSR; Thermo Electron)were compared using a defined standard to assess mass accuracy and a liver extract to assess peak count and dynamic range. SIM-stitching decreased the maximum mass error by 1.3- and 4.3-fold, and increased the peak count by 5.3- and 1.8-fold, versus WSR (AGC targets of 1 x 105 and 5 x 105, respectively). SIM-stitching achieved an rms mass error of 0.18 ppm and detected over 3000 peaks in liver extract. This novel approach increases metabolome coverage, has very high mass accuracy, and at 5.5 min/sample is conducive for high- throughput metabolomics