Analytical methods in untargeted metabolomics: state of the art in 2015.

Arnald Alonso,Sara Marsal,Antonio Juliã

doi:10.3389/fbioe.2015.00023

Abstract

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile – the metabolome – has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

Highlights

Metabolomics is the study of the metabolite composition of a cell type, tissue, or biological fluid
The results reported by several performance comparison studies using either nuclear magnetic resonance (NMR) or mass spectrometry (MS) have demonstrated that spectral alignment algorithms have a good performance irrespective of the analytical technique that has been used (MS or NMR; Van Nederkassel et al, 2006; Giskeødegård et al, 2010)
Multiway methods for longitudinal metabolomic data There is a wide range of methods that are designed to provide a comprehensive interpretation of the metabolic changes according to the organization of the analyzed samples

Summary

Introduction

Metabolomics is the study of the metabolite composition of a cell type, tissue, or biological fluid. The biomedical field is one of the most active areas of development in metabolomics, and includes the search for diagnostic and prognostic biomarkers as well as predictors of treatment response (Meyer et al, 2013; Armitage and Barbas, 2014; Julià et al, 2014). In this field, the use of metabolomics is helping to characterize the impact of key environmental factors on human health. One of the most promising applications is the characterization of gut–microbiota interactions in humans (Wikoff et al, 2009; Nicholson et al, 2012)

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Conclusion