Comparison of GC-MS and GC×GC-MS in the analysis of human serum samples for biomarker discovery

Navigating freely-available software tools for metabolomics analysis

INTRODUCTION

The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.

OBJECTIVES

To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.

METHODS

The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.

RESULTS

A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.

CONCLUSION

This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

Normal-Gamma-Bernoulli Peak Detection for Analysis of Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry Data

Compared to other analytical platforms, comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC-MS) has much increased separation power for analysis of complex samples and thus is increasingly used in metabolomics for biomarker discovery. However, accurate peak detection remains a bottleneck for wide applications of GC×GC-MS. Therefore, the normal-exponential-Bernoulli (NEB) model is generalized by gamma distribution and a new peak detection algorithm using the normal-gamma-Bernoulli (NGB) model is developed. Unlike the NEB model, the NGB model has no closed-form analytical solution, hampering its practical use in peak detection. To circumvent this difficulty, three numerical approaches, which are fast Fourier transform (FFT), the first-order and the second-order delta methods (D1 and D2), are introduced. The applications to simulated data and two real GC×GC-MS data sets show that the NGB-D1 method performs the best in terms of both computational expense and peak detection performance.

Effects of Dietary Different Doses of Copper and High Fructose Feeding on Rat Fecal Metabolome

The gut microbiota plays a critical role in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). Increased fructose consumption and inadequate copper intake are two critical risk factors in the development of NAFLD. To gain insight into the role of gut microbiota, fecal metabolites, obtained from rats exposed to different dietary levels of copper with and without high fructose intake for 4 weeks, were analyzed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOF MS). In parallel, liver tissues were assessed by histology and triglyceride assay. Our data showed that high fructose feeding led to obvious hepatic steatosis in both marginal copper deficient rats and copper supplementation rats. Among the 38 metabolites detected with significant abundance alteration between groups, short chain fatty acids were markedly decreased with excessive fructose intake irrespective of copper levels. C15:0 and C17:0 long chain fatty acids, produced only by bacteria, were increased by either high copper level or high fructose intake. In addition, increased fecal urea and malic acid paralleled the increased hepatic fat accumulation. Collectively, GC × GC-TOF MS analysis of rat fecal samples revealed distinct fecal metabolome profiles associated with the dietary high fructose and copper level, with some metabolites possibly serving as potential noninvasive biomarkers of fructose induced-NAFLD.