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Abstract
Metabolomics is a young field of knowledge that arises linked to other omics such as genomics, transcriptomics, and proteomics. This discipline seeks to understand the performance of metabolites, identifying, quantifying them, and thus understanding its mechanism of action. This new branch of omics science shows high potential, due to its noninvasive character and its close relation with phenotype. Several techniques have been developed to study the metabolome of biological samples, fundamentally nuclear magnetic resonance (NMR), mass spectrometry (MS) and vibrational spectrometry (VS) or a combination of several techniques. These techniques are focused to separate, detect, characterize, and quantify metabolites, as well as elucidate their structures and their function on the metabolic pathways they are involved. However, due to the complexity of the metabolome, in most cases it is necessary to apply several of these techniques to understand completely the whole scenery. This review is aimed to offer a summary of the current knowledge of these analytical techniques for metabolomics and their application to different fields as environmental, food or health sciences. Each technique shows different advantages and drawbacks depending on their technical characteristics and limitations, some factors, such as the aim of the study or the nature of the biological sample will condition the choice. Regarding their applications, NMR has been employed specially to identify new compounds and elucidate structures. The use of MS has gained popularity because of its versatility, easiness to be coupled to separation techniques and its high sensitivity. Whereas VS is widely employed for in situ studies, due to its nondestructive character. Metabolomics applications in different science fields are growing each year, due to advances in analytical techniques and combination with other omics that allow to increase the comprehension of metabolic processes. Further development of analytical tools is necessary to continue exploiting all the possibilities of metabolomics.
Metabolomics seeks to understand the performance of metabolites and its mechanism of action
Different metabolomics techniques have been developed and improved in the last years
Metabolomics applications cover clinical, pharmaceuticals and food and environmental sciences
This review is aimed to offer a summary of the current knowledge of these analytical techniques
Highlights
| Abbreviations Generic | ||
| m/z | = | Mass-to-Charge |
| UV | = | Ultraviolet |
| Compounds | ||
| AA | = | Amino Acids |
| CH | = | Carbohydrates |
| FA | = | Fatty Acids |
| LI | = | Lipids |
| P | = | Proteins |
| Techniques | ||
| AIMS | = | Aspiration Ion Mobility Spectrometry |
| APCI | = | Atmospheric Pressure Chemical Ionization |
| APPI | = | Atmospheric Pressure Photo Ionization |
| CCS | = | Collisional Cross Section |
| CE | = | Capillary Electrophoresis |
| CEC | = | Capillary Electro-Chromatography |
| DAD | = | Photodiode-Array Detector |
| DTIMS | = | Drift-Tube Ion Mobility Spectrometry |
| EI | = | Electron Impact |
| ESI | = | Electrospray Ionization |
| FAIMS | = | Field-Asymmetric Waveform Ion Mobility Spectrometry |
| FT–ICR | = | Fourier Transform–Ion Cyclotron Resonance |
| FTIR | = | Fourier-Transform Infrared Spectroscopy |
| GC | = | Gas Chromatography |
| HILIC | = | Hydrophilic Interaction Chromatography |
| HPLC | = | High-Performance Liquid Chromatography |
| HRMAS | = | High-Resolution Magic-Angle Spinning |
| HRMS | = | High Resolution Mass Spectrometry |
| IM | = | Ion Mobility |
| IMS | = | Ion Mobility Spectrometry |
| IR | = | Infrared |
| LC | = | Liquid Chromatography |
| LIF | = | Laser-Induced Fluorescence |
| MALDI | = | Matrix‐Assisted Laser Desorption/Ionization |
| mGWAS | = | Genome Wide Application Studies with Metabolomics |
| MS | = | Mass Spectrometry |
| nESI | = | Nano Electrospray Ionization |
| NIR | = | Near Infrared |
| NMR | = | Nuclear Magnetic Resonance |
| QqQ | = | Triple Quadrupole |
| RPLC | = | Reversed-Phase Liquid Chromatography |
| RS | = | Raman Spectroscopy |
| SERS | = | Surface Enhanced Raman Spectroscopy |
| SFC | = | Supercritical Fluid Chromatography |
| SP | = | Spectroscopic |
| TOF | = | Time Of Flight Detector |
| TWIMS | = | Traveling-Wave Ion Mobility Spectrometry |
| UHPLC | = | Ultra-High-Performance Liquid Chromatography |
| UPLC | = | Ultra-Performance Liquid Chromatography |
| VS | = | Vibrational Spectroscopy |
Acknowledgements
The research leading to these results received financial support from Programa de Cooperación Interreg V-A España—Portugal (POCTEP) 2014–2020 (projects Ref.: 0181_NANOEATERS_01_E and Ref: 0377_IBERPHENOL_6_E); MICINN supporting the Ramón&Cajal grant for M.A. Prieto (RYC-2017-22891); Xunta de Galicia and University of Vigo supporting the post-doctoral grant for M. Fraga-Corral (ED481B-2019/096) and the pre-doctoral grants for A.G. Pereira (ED481A-2019/0228) and P. García-Oliveira (ED481A-2019/295); Xunta de Galicia through the Axudas Conecta Peme supported the IN852A 2018/58 NeuroFood Project; the company AlgaMar (www.algamar.com); EcoChestnut Project (Erasmus + KA202) that supports the work of M. Carpena; Ibero-American Program on Science and Technology (CYTED - AQUA-CIBUS, P317RT0003); the Bio Based Industries Joint Undertaking (JU) under grant agreement No 888003 UP4HEALTH Project (H2020-BBI-JTI-2019), the JU receives support from the European Union’s Horizon 2020 research and innovation program and the Bio Based Industries Consortium.
Disclosure statement
Authors have nothing to declare and confirm that there is no conflict of interest.