Do not just do it, do it right: urinary metabolomics--establishing clinically relevant baselines.

Review Received: 17 February 2014,

Revised: 17 March 2014,

Accepted: 25 March 2014

Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI 10.1002/bmc.3219

Do not just do it, do it right: urinary metabolomics –establishing clinically relevant baselines Drupad K. Trivedia,b* and Ray K. Ilesa,c ABSTRACT: Metabolomics is currently being adopted as a tool to understand numerous clinical pathologies. It is essential to choose the best combination of techniques in order to optimize the information gained from the biological sample examined. For example, separation by reverse-phase liquid chromatography may be suitable for biological fluids in which lipids, proteins and small organic compounds coexist in a relatively nonpolar environment, such as serum. However, urine is a highly polar environment and metabolites are often specifically altered to render them polar suitable for normal phase/ hydrophilic interaction liquid chromatography. Similarly, detectors such as high-resolution mass spectrometry (MS) may negate the need for a pre-separation but specific detection and quantification of less abundant analytes in targeted metabolomics may require concentration of the ions by methods such an ion trap MS. In addition, the inherent variability of metabolomic profiles need to be established in appropriately large sample sets of normal controls. This review aims to explore various techniques that have been tried and tested over the past decade. Consideration is given to various key drawbacks and positive alternatives published by active research groups and an optimum combination that should be used for urinary metabolomics is suggested to generate a reliable dataset for baseline studies. Copyright © 2014 John Wiley & Sons, Ltd. Keywords: chromatography; metabolomics; shotgun analysis; urine

Introduction Metabolomics using a variety of analytical technologies has attracted interest from a multitude of fields including toxicology (Liu et al., 2011; Robertson et al., 2010), plant physiology (Weckwerth, 2008; Guy et al., 2008) and biomedical/biomarker research (Koek et al., 2011; Zhang et al., 2008a, 2008b; Ramautar et al., 2011; Vinayavekhin et al., 2010; Quinones and Kaddurah-Daouk, 2009; Denery et al., 2010; Armitage & Barbas, 2014; Kamlage et al., 2004; Perez-Cornago et al., 2014; Xu et al., 2014). Improved detection capacity of various instrumental techniques in biomedical research has increased the interest in the global metabolic profiling. Recently untargeted as well as targeted metabolomics have revealed previously unknown analytes of interest, for example, Andersen et al. (2014) used liquid chromatography–mass spectrometry (LCMS) as a screening tool for estimating patient compliance and Reinke et al. (2014) demonstrated key relationships between biomarkers and pathogenesis of multiple sclerosis using metabolic profiling. Metabolomics is no longer about just discovery of biomarkers. It has evolved in recent years to study patterns in disease or a healthy state. Steffen et al. (2014) recently showed the use of metabolomics biomarkers for studying dietary patterns and Calvani et al. (2014) used nuclear magnetic resonance (NMR)-based metabolomics to establish a signature of patterns of ageing in mice – demonstrating versatile nature of metabolomics. There is an array of methods used for metabolomics studies including NMR, gas chromatography (GC) 2D electrophoresis, capillary electrophoresis (CE), high-performace liquid chromatography (HPLC)-MS (Issaq and Blonder, 2009) and a variety of mass spectrometry approaches. No single technique can be used on its own; however, owing to the limitation of

Biomed. Chromatogr. 2014

technologies available within a laboratory, protocols are often developed using a single detection technology coupled to a separation technique, for example, GCMS, HPLC MS, CE-MS. The information generated from such combined devices requires multivariate analysis and in some cases pattern-recognition software to analyse the complex data sets that arise from these techniques. The final stage in metabolomics studies is all too often an attempt to identify a single or limited set of discriminating signals to a defined molecule using NMR and/or MS in conjunction with database searching and/or reference to commercial standards (Trivedi and Iles, 2012). Biomarker discovery is often the preconceived hunt that a single new biomarker can be identified that defines the pathological condition or change. This would fit within a clinical diagnostic industry in which immunoassays to that new biomarker can fit within the current technology platforms. However, reality and the power of metabolomics lie in a more complex simultaneous detection and relative

* Correspondence to: D. K. Trivedi, Manchester Institute of Biotechnology and School of Chemistry, University of Manchester M1 7DN, UK. Email: [email protected] a

Eric Leonard Kruse Foundation for Health Research, Manchester, UK

b

Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, M1 7DN, UK

c

MAP Diagnostic Ltd, Ely, Cambridgeshire, UK Abbreviations used: DIMS, direct injection mass spectrometry; HILIC, hydrophilic interaction liquid chromatography; IRS, infrared spectroscopy; MALDI, matrix assisted laser desorption; RS, Raman spectroscopy.

Copyright © 2014 John Wiley & Sons, Ltd.

D. K. Trivedi and R. K. Iles quantification of multiple analytes: the diagnostic and exploratory dissection of a phenotypic pathology revealed in the relative changes of all those analytes and not just an absolute quantification of one.

Sample collection and preparation There is a huge variety of ‘metabolomes’ that can be studied by using various biological fluids. Bio-fluids incorporate function and phenotypes of many different areas of the body; thus, biofluids are usually complex. Furthermore, the diversity in phenotype is affected by genome and environmental factors so pathologies may display considerable variability. Within a biological system the metabolites are in a constant metabolic flux. Thus, the experimental design must be robust to obtain statistical significant results. The size of population depends on the level of biological occurrence and availability of both sample and finances. The sample set must be a representative set of the whole population. Bio-fluid samples are collected either noninvasively (urine and saliva) or invasively (serum, cerebrospinal fluid or plasma). Certain invasive sample collection techniques could affect the metabolome, for example, needle prick could stimulate the release of catecholamine and 10 other hormones if the patient suffers from fear of needles (Hamilton, 1995). During any metabolomics study the sample needs to be quenched in order to halt the dynamic biochemical processes so that a snapshot of biological composition can be obtained at the time of sample collection. Usually addition of organic solvents or buffer helps to preserve the sample (Maharjan and Ferenci, 2003), which can then be stored at 20°C to 80°C or an immediate analysis could also be carried out. The nature of the matrix and the analytes of interest should be carefully considered for a metabolomics approach. Environmental samples can be prone to microbial degradation and organic chemicals like proteins in samples may degrade with time (Suzuki et al., 2004). In order to avoid sample-to-sample variability owing to degradation and sample volatility, careful sample handling is necessary. The sample storage conditions for certain analytes may play an important role in minimizing degradation by heat, light and air (Kirchherr and Kuhn-Velten, 2006). Thus, storage of such samples at optimum temperature, in the dark and in the presence of antioxidants is essential to generate accurate metabolomics data. The sample preparation is dictated by the metabolomics approach that is being studied, for example, in metabolomics profiling, the samples can be analysed immediately following dilution or after protein removal step. The selection of diluents depend on the selected analytical technique, that is, NMR solvents are deuterated, MS solvents are volatile buffers, LC solvents are chosen based on desired polarity, etc. In contrast, in studies involving targeted metabolomics, extensive sample preparation is usually carried out to minimize interference and thus improve signal-to-noise ratio. In mass spectrometry many detergent or other agents used to halt further (quench) dynamic metabolism of the sample ex-vivo also quench the physio-chemical process of ion generation, Thus to avoid rendering a sample unsuitable and manipulating any available information from the urinary metabolome, we recommend minimal edition of solvents and detergents during sample preparation protocols (Trivedi et al., 2012).

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Analytical technologies used in metabolomics Analytical technologies used in metabolomics can be divided into two main types: separation techniques and detection techniques. The use of detection techniques like MS and NMR as standalone system is possible; however, with the aid of separation techniques, better sensitivity and resolution can be obtained.

NMR for metabolomics NMR is used extensively for structural elucidation, studies on testing drug safety (Powers, 2009; Schnackenberg and Beger, 2008), diagnosis of diseases (Tiziani et al., 2009), identification of natural products (Halabalaki et al., 2014) and therapy monitoring (Mancini et al., 2009). The Consortium for Metabonomic Toxicology project has been able to show the applicability of NMR for urinary metabolomics (Linden and Lawhead, 1975; Lindon et al., 2005) with high sensitivity and specificity. NMR is capable of detecting a wide range of urinary analytes including sugars, ketones, organic acids, nucleosides, steroid and fatty acids. Use of NMR metabolomics is widely accepted in making pharmaceutical formulations (Sanchez et al., 2008). Shamsipur et al. (2007) used 19F NMR assay for the antipsychotic drug haloperidol in human serum and pharmaceutical formulation. Hewer et al. (2006) used 1H NMR-based metabonomic techniques to distinguish between HIV-1 positive/AIDS patients on antiretroviral treatment and HIV-1 negative individuals. NMR can be a highthroughput technique that proves helpful when analysing a few hundred samples in a day. NMR can be used to study magnetic isotopes, that is, the isotopes that possess an angular moment or angular spin that is associated with the magnetic moment. A few well-known magnetic isotopes include 13C, 1H, 19F, 14N, 17O, 31P and 33S. Routinely used nuclei for the metabolomics analysis of biological or pharmaceutical mixtures include 1H (the most sensitive), carbon-13 (13C), fluorine-19 (19F) and phosphorus-31 (31P). The energy differences between the quantized orientations of different nuclei are very small, in the range of microelectronvolts, and hence the NMR spectroscopy suffers from relatively low sensitivity. The sensitivity may not improve even with the most advanced instruments and at times requires at least micromole concentrations of the analytes (Shachar-Hill, 2002). This can be partly compensated for by the abundance of molecular information that can be obtained using typical NMR spectra (Eisenreich and Bacher, 2007). Despite the relative lack of sensitivity, the nondestructive nature of NMR analysis allows the sample to be intact for subsequent analysis by any other techniques. A combination of chemical shift, spin–spin coupling and relaxation or diffusion facilitates rapid identification of metabolites. The hydrophobicity of analytes affects the relaxation times and eventually peak broadening and peak overlapping can be seen (Wishart, 2008). The response of every proton being uniform in samples, the chemical shift in only one standard can be sufficient for quantification of urinary metabolites, but when metabolites are in very low concentrations, their detection could become a challenge using NMR. Thus, analytes present at low concentration, such as nucleosides or hydrophobic analytes like steroids or fatty acids, are not detected efficiently. Carr–Purcell– Meiboom–Gill pulse train can be used to remove resonance from macromolecules when studying small molecules in order to increase sensitivity. However, the variability in protein levels within a sample can impact metabolomic studies (Van et al., 2003) as such additional steps can increase the analysis time



Choosing correct tools for shotgun urinary metabolomics by up to 20 min per sample, hence limiting the utility of NMR as a high-throughput technique. Thus, NMR is a specific but nonselective technique and, because of its nonselective nature, all the low molecular weight compounds in the sample are detected simultaneously in a single run. The bio-fluid can be studied using magic-angle spinning technology with minimal sample preparation (Ratai et al., 2005). During sample analysis solvent suppression methods have to be implemented in order to reduce background noise from protonated solvent residues. However, this leads to loss of spectral information at that frequency and causes water resonance in the immediate surrounding areas, which leads to a further loss of information. Post-analysis data processing is required sometimes when instrument-to-instrument variations are observed to influence the metabolome (Bailey et al., 2004). NMR spectrometric reference libraries are not as large as those available for mass spectrometry. This makes it difficult to identify metabolites. Targeted NMR data can be analysed using a spectral binning approach; this is a chemometric technique in which the spectral area of interest is first selected and then metabolites in that region are identified and quantified. This targeted profiling approach will limit the possibility of new unknown biomarker investigation. Enhanced resolution can be achieved by coupling HPLC with NMR (Cloarec et al., 2007). LC-NMR to an extent overcomes the sensitivity issues of NMR alone (Alexander et al., 2006; Wann et al., 2005). HPLC-NMR has been used successful for urine-related metabolomics studies in rats (Akira et al., 2011). Solid Phase Extraction (SPE) trapping interface allows online concentration and purification of very low-level minor components (Godejohann et al., 2004; Yalçın and Yüktaş, 2006; Spraul et al., 2003). Although the comparison of NMR–spectral fingerprints is gaining popularity to understand differences between two sample sets, only a limited number of metabolomic studies have used NMR to compare human metabolomes (Lenz et al., 2003; Kohl et al., 2012).

Mass spectrometry for metabolomics MS is a very sensitive and robust technique in spite of the diverse range of small molecules present and therefore represents a very capable method for metabolomics analysis. It has both logistical and analytical advantages over NMR but NMR’s key limitations is sensitivity, which makes it usable only when the compounds are present at a high concentration. Mass spectrometry can be used as a sensitive method for specific metabolite detection, down to attomole levels, as well as for structural identification. Compounds can be directly identified by their m/z values from direct injected into MS, or can be pre-separated using chromatography or electrophoresis techniques prior to injection. Chromatography coupled to MS provides better detection, identification and quantification as resolution is enhanced and signal quenching by abundant ion species or matrix is greatly reduced. In MS, a charged analyte ion is subjected to an electromagnetic fields which determine its passage to a detector. Mass spectrometers are split into two functional parts, an ionization chamber and a mass analyser. The way the electromagnetic field is designed to interact with charged ion is always a function of the mass to charge ratio of that ion (m/z). Thus in mass sector instruments the ions are deflected along a circular path in a radius that is directly proportional to the m/z. In quadrupole


mass spectrometer the accelerated ion flight is focused on to the detector at specific electromagnetic frequencies and the mass analysers sweep across m/z range, recording hits on the detector at specific calibrated electromagnetic frequencies. In time-of-flight detectors, the time it takes for the ions to travel along a long flight tube and recorded at a detector is proportional to their m/z. In ion trap analysers generated ions are collected in an electromagnetic trap (increasing their concentration) and then expelled at specific m/z to collide with a detector (see Table 1). Ionization methods are equally as variable: in magnetic sector analysers the gas-phased ions of the analytes are formed by electron ionization (hard ionization). A high-energy beam of electrons displaces an electron from the molecules to form a radical cation/anion (or molecular ion). Unstable molecular ions are fragmented further into smaller ions. This is a rather harsh and destructive method not suitable for larger organic molecules and certainly not proteins as the fragmentation is too destructive. More gentle (or soft) ionization are used the most common being electrospray ionization (ESI) and matrix assisted laser desorption (MALDI): in ESI compounds in a solvent are forced through a nano-bore capillary which has a high voltage potential difference applied. The nano-spray that is ejected is highly charged and as the solvent rapidly evaporates the remaining compounds are left multiply charged. In MALDI samples are mixed with concentrated heterocyclic compounds and allowed to crystalize as a small dry spot. A laser is fired at the spot and the energy absorbed by the heterocyclic matrix, which forms a reactive charged gas plume. Sample compounds are also thrown into that reactive matrix gas plume and charge is transferred to the compounds in a nondestructive reaction (see Table 2). The advantages of ESI and MALDI is that large molecules can be ionized without being destructively fragmented. MALDI tends to form predominately singly charged large molecules, although double- and triple-charged forms are found, while ESI forms a range of highly charged forms of the molecule and analyser/detector data has to be processed (or deconjugated) for interpretation as mass spectra. MADI is particular suitable for ionization of large proteins. The wide range of available ionization and analysers, as well as the option of using hybrid instrument incorporating sequential mass analysis, means that the type of MS used depends on

Table 1. Different mass analysers that are used in various combinations to provide better selectivity Analyser Magnetic sector Electric sector Quadrupole

Ion trap Time of flight Fourier transform orbitrap Fourier transform ion cyclotron resonance


Principle Momentum of ions Kinetic energy of ions Trajectory stability of ion in oscillating electric fields to separate ions based on their m/z values Resonance frequency of ion trapped in 2D or 3D Flight time of ion Resonance frequency of ion Resonance frequency of ion


D. K. Trivedi and R. K. Iles Table 2. Ionization methods for mass spectrometry Ionization

Principle and features

Electron bombardment Hard Ionization – suitable for small compounds and high resolution of isotopes Electrospray (ESI) Soft Ionization – giving multiple charges suitable for coupling to pre-separation by liquid chromatography Matrix assisted laser Soft ionization – giving limited desorption (MALDI) charge transfer suitable for analysis of large proteins and other macro-molecules

the nature of analysis to be carried out. Tandem mass spectrometry can be used to identify analytes by their chemical structures, not just the m/z of the parent molecule, as the second mass analyser will take a specific ion and break it further to more basic and recognized masses. In addition, triple quadrupole instruments can be used for detection and absolute quantification of analytes at trace levels using reference compounds.

Direct injection mass spectrometry Direct injection mass spectrometry (DIMS) is a high-throughput technique that can be used to infuse samples directly into MS (Biasioli et al., 2011). Various ionization and analyser combinations are used for DIMS. ESI and related spray techniques (such as atmospheric pressure chemical ionization) has meant that polar as well as nonpolar metabolites can be detected. Protontransfer-reaction MS is used for volatile organic compounds (Lindinger and Jordan, 1998; De Gouw et al., 2003), and selected ion-flow-tube MS is also used for all volatile compounds (Olivares et al., 2011; Storer et al., 2011). However, by far the simplest method of direct mass spectrometry is via MALDI. Here the biological microliter quantities of a bio-fluid sample are mixed with a suitable matrix, crystalized and soft ionization results from laser exposure. However, the matrix signal is overwhelming so only masses far higher than the matrix m/z can be detected so small molecule metabolomics are not suitable for this techniques. Nevertheless, the resolution and accuracy of these MS approaches are continually improving to meet the standards required to allow a valid metabolomics study to be carried out. In DIMS, as already refered to, the matrix plays an important role in enhancement or suppression of ionization of a sample owing to the concentration of the sample. If any matrix effect is suspected it could be overcome by using isotopic analogues of metabolites of interest as internal standards. New methods of sample injections into a mass spectrometer have helped nullify matrix effects such that desorption electrospray ionization and extractive electrospray ionization are the preferred methods for small to medium-sized (~4000 m/z) molecule mass spectral analysis (Chen et al., 2006; Gu et al., 2007; Pan et al., 2007). The metabolites can be relatively easily ionized in both positive and negative modes and, hence, dual ionization mode can be used while carrying out metabolic profiling (Lei et al., 2011). ESI is a soft ionization technique suitable for DIMS and coupled to a chromatographic separation if required (Schroder, 1996).


Biological samples can be analysed with relatively good specificity and sensitivity by infusing the sample into an MS equipped with an ESI source. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) has additional capability to give accurate mass resolution and sensitivity. However, structural isomers of the same molecular weight cannot be resolved using FTICR-MS. The generated mass spectra in DIMS can be tricky to interpret if many metabolites contributing to the response of single mass range are present in the sample. This may be overcome by use of ToF MS and algorithms in order to classify and compare different mass spectra. The m/z vs intensity matrices generated in these mass spectra can be easily exported to external multivariate analysis software for further statistical analysis.

Vibrational spectroscopy Raman spectroscopy (RS) and infrared spectroscopy (IRS) have been widely used for metabolic fingerprinting without the use of any derivatizing reagents and without destructive ionization of the sample (Li et al., 2012). The vibrations and rotations of molecular functional groups are measured using optical spectroscopy. The electronic excitation, change in vibration and/or change in rotations take place when the sample is irradiated and energy exchange occurs. IRS uses the IR region of the wavelength spectrum whereas RS uses a monochromatic beam (visible light or UV region). The transition event depends on the type of irradiation. Vibrational spectroscopy is not as sensitive as MS and does not facilitate the identification of analytes. Fourier transform-infrared is widely used form of IRS. The Raman-effect is very weak and hence a highly sensitive and optimized system is required for detection of unknown metabolites. The sample may overheat through the intense laser radiation if the system is not optimized. This can cause the sample to degrade which in turn will give meaningless Raman spectra. The fingerprinting analysis is relatively inexpensive using vibrational spectroscopy and the sample preparation and analysis is extremely quick. Thus, IRS and RS act as complimentary to each other for detection.

Liquid chromatography Liquid chromatography (LC) is one of the most popular chromatographic techniques used for metabolite identification and quantification as the complexity of the sample is reduced by separation before detection (Xiao et al., 2012). LC is a versatile separation technique that can be used for separation of wide range of molecules. The urinary metabolites with a wide range of polarity require more than one type of column chemistry, that is, the use of orthogonal techniques for separation of polar as well as nonpolar metabolites. Normal phase chromatography is based on using a polar stationary phase such as silica and a nonpolar mobile phase such as hexane. In this separation mode retention increases with more hydrophilic or polar stationary phase. The relative strength of stationary phase decreases as the group around the stationary phase ligand gets bulkier. In reverse-phase liquid chromatography (RPLC), as the name suggests, the stationary phase is nonpolar usually an alkyl chain chemically bonded to silica and a polar mobile phase. In RPLC retention of an analyte is related to its hydrophobicity. Less polar analytes interact with the stationary phase more strongly than polar analytes. Usually weak or moderately polar compounds can be easily separated using either



Choosing correct tools for shotgun urinary metabolomics of these techniques. The RPLC is compatible with MS but the normal-phase chromatography is not compatible with MS owing to the nature of solvents used. The retention mechanism is based on competition in both techniques between sample and mobile phase molecules for binding with localized stationary phase (Snyder et al., 2009). RPLC, however, cannot be used for the retention of very polar analytes like carbohydrates even with 100% aqueous mobile phase (Dos Santos et al., 2009). Hydrophilic interaction liquid chromatography (HILIC) without being named as such has been around since 1975 and routinely used for analysis of sugars (Linden and Lawhead, 1975; Palmer, 1975). It is based on hydrogen bonding interaction and not just charge. In 1990 Alpert advanced the idea of HILIC with his work on separation of peptides, nucleic acids and other polar compounds with this mode of chromatography. Since then HILIC has become a popular separation technique for assays based on MS detection because the eluents used for HILIC are various combinations of acetonitrile in water or volatile buffer which are compatible with MS ionization techniques such as electrospray (Spagou et al., 2010; Nguyen and Schug, 2008).

Hydrophilic interaction liquid chromatography – HILIC In contrast to RPLC, HILIC can provide strong retention of polar compounds. The mechanism of separation is similar to normalphase liquid chromatography except that an aqueous organic mobile phase is used instead of a nonpolar solvent. Thus, it can be used in combination with ESI mass spectrometry. HILIC may give a better separation for very strong polar compounds owing to its less viscous organic mobile phase. Gritti et al. (2010) have also suggested that the gain in efficiency by switching from RPLC to HILIC is in the order of 1000-fold increase in the resolution of hydrophilic analytes (Sequant, 2007) The mechanism of separation in HILIC is not yet clear but researchers suggest that hydrogen-bonding (Nguyen et al., 2010), dipole–dipole movement (Soukup and Jandera, 2012) and/or hydrophilic partitioning could be responsible for retention of polar compounds (Karatapanis et al., 2011). The nature of surface of the stationary phase can vary the extent of hydration of polar molecules and hence immobilized water molecules, with varying effect. The hydrophilic partitioning model, a theory based on circumstantial evidence, suggests that a hydrophilic surface holds water when exposed to mixtures of organic solvent and water (Guo and Gaiki, 2011).

Commonly used HILIC stationary phases The diversity of HILIC stationary phase, supporting materials and surface chemistry has improved over time to meet various requirements of separation science. The basic HILIC columns include plain silica, a chemically bonded neutral polar compounds, ion-exchange and zwitterionic residues; the different types of stationary phases eliciting differences in the retention, efficiency and chromatographic selectivity. The retention mechanism of ordinary plain silica columns may be due to combination of mobile phase partitioning, adsorption and ion exchange. This may also depend on the nature of analytes that are separated using diversely manufactured HILIC columns. At high pH the silanol groups ionize to a greater extent


and cation exchange aids the retention mechanism. As a result of the hydrosilation process during production, about 95% of Si–O–H groups are removed making the surface of the silica less polar and gives improved reproducibility of retention (Gomez and Sandoval, 2010). Various columns packed with porous superficial particles or fused core porous silica are also used for certain high-throughput HILIC separation for metabolomics. (Hsieh et al., 2009; Gika et al., 2008). Chemically bonded solid-phase silica is prepared by chemical modification of silica gel by reactions with trialkoxysilanes. The trialkoxysilanes contain polar and alkyl groups that increase retention of many compounds when exposed to an increasing acetonitrile content of mobile phase. Examples of silica-based bonded stationary phases for HILIC applications include poly succinimide - or sulfoalkylbetaine-silica. The advantage of using bonded silica stationary phases is that it is capable of forming a polymeric network to form a water-rich layer that can in turn improve partitioning mechanism for certain polar analytes (Orth and Engelhardt, 1982). Various charged analytes like polysaccharides, oligosaccharides, glycols and glycerol are efficiently separated using ionexchange or zwitterionic charged stationary phase surfaces. It has been noted that columns packed with polymer particles for ion-exchange show lower separation efficiency than silicabased zwitterionic or ion-exchange HILIC stationary phases (Jandera, 2008). The zwitterionic phase for separation of inorganic anions and cations was demonstrated by Jiang and Irgum (1999). The active layer on the silica gel or polymer in zwitterionic stationary phases normally contains one strong acid group and one basic group separated by a short alkyl spacer. These groups, owing to their charges, can simultaneously separate anions as well as cations. The polar interactions, like hydrogenbonding and dipole–dipole moments, are associated with primary retention mechanism for such stationary phases (Sequant, 2011). However, there may be weak electrostatic forces affecting separation owing to oppositely charged groups bonded to the stationary phase.

HILIC mobile phases In HILIC, the initial mobile phase contains a high organic content and relatively low aqueous content. The organic concentration is gradually changed until acceptable sample retention of the analyte(s) is achieved. The choice of organic solvents can significantly affect the retention mechanism in HILIC chromatography, for example, the elution strength of HILIC solvents increases with the increase in solvent polarity. Acetonitrile is the only HILIC solvent known to show no proton–donor interactions (Quiming et al., 2007). Owing to this property, it does not exhibit strong hydrogen bonding like methanol, ethanol or 2-propanol. Acetone shows similar polarity to acetonitrile but has inferior selectivity and provides lower signal intensity on MS (Fountain et al., 2010). The pH of buffers used in the HILIC affects the retention and selectivity of ionizable samples (Orth and Engelhardt, 1982; Hao et al., 2008). With increased concentration of buffer the hydrogen-bonding interactions increase between analytes and the stationary phase, leading to better retention of nonionic polar analytes. However, if the analytes of interest are ionic, then the buffer ions may compete with ionic analyte and displace ionized sample molecules from the stationary phase. In Zwitterionic (ZIC)-HILIC chromatography a high percentage of organic solvent in the eluent increases the retention of the solutes. However, with



D. K. Trivedi and R. K. Iles these columns, at least 3% water in the mobile phase is recommended in order to gain reproducible results (Sequant, 2011). As in RPLC, the most common mode of chromatography in HILIC is isocratic elution. Gradient elution in ZIC-HILIC can be accomplished by increasing the polarity of the mobile phase with time, that is, by decreasing the concentration of organic solvent. For HILIC solid phases that contain a charged group there is also a possibility of controlling retention by increasing the buffer concentration during an organic to aqueous gradient to disrupt electrostatic interactions with the solute. For analysis of samples containing molecules with wide range of polarities such a gradient elution is ideal. In HILIC it is essential to equilibrate the column with 5–10 column volumes of solvents thoroughly to ensure that the water in the aqueous layer of stationary phase is replenished from the eluent between analyses. Thus, HILIC stationary phases are not suitable for fast gradients analyses with short equilibration times (Jandera, 2011). This is primarily due to a rapid change in the composition of water layer in the stationary phase which renders HILIC columns less tolerable to quick gradient changes.

Mechanism of ZIC-HILIC The ZIC-HILIC stationary phase contains a covalently bonded, zwitterionic sulfobetaine-group as the functional group bonded to the silica (Fig. 1). Exposed to an aqueous–organic mobile phase, a water-rich layer is established within the stationary phase. The partitioning mechanism of solutes leads to separation of solutes from the eluent. This process is exothermic and is dependent on various factors like acidity or basicity of the solutes, the dipole interactions and hydrogen bonding. In ZIC-HILIC, the stationary phase adds an extra dimension of separation mechanism to analyte retention. However, buffers or salts are required in the mobile phase to disrupt the interactions for successful elution. The use of buffers with high salt concentration is not recommended for methods based on massspectrometric detection. However, with ZIC-HILIC lower concentration of buffers can be used as the electrostatic interaction effect is lowered by ionic-groups present on the stationary phase. The mechanism of HILIC partitioning and retention is still not fully understood and work is ongoing in this area (Sequant, 2011).

RPLC-MS in metabolomics Reversed-phase liquid chromatography is arguably the most popular mode of analyte separation used today, as it allows a diverse range of compounds to be separated. The variety of

Figure 1. A typical ZIC-HILIC stationary phase provides a unique environment, particularly capable of solvating polar and charged compounds via weak electrostatic interactions, as opposed to the strong electrostatic interactions obtained with plain silica or amino HILIC phases (Sequant, 2011).


bonded phases that are commercially available has increased. These include monolithic columns, mixed-mode columns, polar embedded phases as well as specialized phase. Over the past decade the number and variety of commercially available RPLC stationary phases that contain a polar embedded functionality has also increased (O’Gara et al., 2001). The terminology to describe the use of LC-MS for studying metabolites was coined as metabolomics in 1998, but LC-MS for analyses metabolite has been used for years. It is no surprise that the most popular mode of LC with MS detection is RPLC. RPLC conjugated to MS can be useful for separation and detection of semi-polar/nonpolar compounds (Bowen and Northern, 2010) like flavonoids (Tache et al., 2012), glycosylated proteins and steroids (Zhang et al., 2008a, 2008b), alkaloids (Colegate and Gardner, 2007), triaclyglycerols (Sommer et al., 2006), fatty-acid esters (Garcia-de Blas et al., 2011) and phenolic acids (Bravo et al., 2007). Owing to the diversity of chemical nature of small molecules in bio-fluids, the elucidation of the metabolome is particularly challenging. Furthermore, coupling HPLC to MS detection systems like ion-trap helps in obtaining a spectrum for identifying the isolated rare abundance analytes of interest by trapping those ions and hence increasing sensitivity further. Chong et al. (2010) using LC-MS successfully separated and identified nucleotides and nucleosides from mammalian cell profiles. LC-MS provided better metabolomic profiles and marker identification than any other analytical technique on its own (Li et al., 2010). RPLC is based on use of a nonpolar stationary phase and a polar mobile phase. However, very polar metabolites generally elute in the void volume in RPLC. Hence, they are very difficult to separate and ionize. Certain nonpolar analytes that are not well retained on a HILIC column may be well be retained on RPLC columns. Although the separation and retention of polar metabolites can be achieved using RPLC, by using a lower proportion of organic solvent in the mobile phase, a lower MS ionization and detection response is a drawback. In contrast, the number of analytes detected by MS separated by HILIC is higher even though the efficiency of HILIC columns may be lower than that of RPLC columns (Chen et al., 2009). The recognition of this separation advantage for MS-based proteomics appears to be reflected in the increasing number of publications using HILIC (Theodoridis et al., 2012; Wilson et al., 2005; Lenz and Wilson, 2007). Nevertheless, reversed-phase gradient elution with varying run times and varying conditions coupled to electrospray ionization mass spectrometry seems to be the primary method of choice for MS metabolomics (Ayrton et al., 1998). Generally the studies that have been reported in the literature involve use of conventional columns that are 2.1–4.6 mm in diameter, 5–25 cm in length and packed with 3–5 μm particles (Theodoridis et al., 2008). In 2002 and 2004 Plumb reported that using by using reverse-phase UPLC column systems more bio-fluids metabolites can be separated and hence, detected than conventional reverse-phase HPLC systems (Plumb et al., 2002; Plumb, 2004; Wikoff, 2008). HPLC-MS protocols used for metabolic profiling may provide optimum chromatographic resolution with minimal matrix effect on ion suppression or enhancement (Wilm and Mann, 1994). However, pre-analytic matrix effects of the bio-fluids themselves are now a major concern in metabolomics; for example several authors have shown that in urine samples the continued activity of endogenous urease interferes with detection of important



Choosing correct tools for shotgun urinary metabolomics urinary metabolites like hypoxanthine, tyrosine, citrate and acotonic acid (Pasikanti et al., 2008; Issaq et al., 2009; Kujara, 2005; Stamler et al., 2003). The sensitivity of mass spectrometric detection can be increased by reducing band broadening in chromatography. Careful selection of column internal diameter and packing particle size provides better resolution and hence, a better mass spectra. If 3.5–5 μm particles do not provide the required resolution required then UPLC may be used. Very polar analytes can be separated using HILIC which is orthogonal to RPLC. Using HPLC-MS with orthogonal chromatographic chemistries like RPLC and HILIC, polar and nonpolar analytes can be resolved and detected. Nontargeted metabolomics can be carried out by coupling HPLC to Time of Flight (ToF) or Ion Trap (IT) for enhanced sensitivity It is now recognized that global metabolic profiling using RPLC-MS is unlikely but may be more suitable for, tissue extract, serum and fluid exudates as these bio-samples are generally balanced to favour nonpolar metabolites. Urine, however, is a polar environmental matrix and many metabolites are modified to be polar for excretion. Thus, separation and detection systems have to recognize this for urine metabolomic studies and HILIC is more appropriate. However, modifications to RPLC have shown success in various challenging areas. For example, Wikoff et al. reported the use of capillary RPLC-MS based metabolomic study for understanding viral infection induced neurodegeneration which has been a relatively unexplored area (Wikoff, 2008). Hyndman et al. (2011) in their review about studies using RPLC and NMR concluded that this metabolomics approach to urine can be used for diagnosis of bladder cancer. In a review by Lakshmanana et al. (2011) the authors highlighted the use of RPLC/MS along with HILIC/MS for the study of polyamines, glycerol and lipid metabolism pathways related to malaria pathology. Thus, a large number of published studies suggest that the use of RPLC/MS is an effective approach to metabolomics as well as an orthogonal approach with HILIC/MS for metabolomic profiling studies.

Gas chromatography–mass spectrometry GC-MS is ideal for metabolomic studies involving volatile and thermally stable polar as well as nonpolar compounds. Electron impact or chemical ionization MS can be used in order to detect the separated compounds in GC-MS. In electron impact is ionization high-energy electrons interact with gas-phased atoms or molecules to produce ions and the instrument-to-instrument variation has been found to be minimal. Hence, reproducible characteristic fragmentation patterns are obtained. Metabolite identification or mass spectral matching is achieved by retention time or retention index comparisons with pure compounds as well as by comparison against reference libraries. The availability of metabolite libraries for identification in GC and GC-MS is the main advantage this method has over LC-MS metabolomics. Also, the high chromatographic resolution achieved using GC permits separation of structurally similar compounds that may be very difficult to separate using LC. Thus, GC-MS metabolomic profiling has been used to identify biologically active metabolites/biomarkers that are either protective or harmful to the structure and function of heart (Alexander et al., 2010). Chen et al. used GC-MS and other modes of chromatography to study gastric tumor (Chen et al., 2010) and metabolomic studies related to oxidative stress in hepatic tissue samples, urine and


plasma have proven GC-MS to be a very useful analytical tool for screening and biomarker discovery (Bando et al., 2010). The application of GC-MS in metabolome analysis can be classed into two groups: (1) naturally volatile metabolites – such as ketones, aldehydes, alcohols, esters, furan and pyrrole derivatives, heterocyclic compounds, sulfides, some lipids, isocyanates, isothiocyanates and hydrocarbons with 1–12 carbons. (2) nonvolatile metabolites – such sugars, sugar-phosphates, amino acids, lipids, peptides, long-chain alcohols, amines, amides, alkaloids, sugar-alcohols and organic acids can be made volatile by derivatization. Nonvolatile compounds have to be derivatized using agents such as N,O-bis(trimethylsilyl) trifluoroacetamide, N-methyl-N(trimethylsilyl) trifluoroacetamide (Yi-qi et al., 2007) and Nmethyl-bis(trifluoracetamide) (Hidvegi et al., 2008). Derivatization of samples adds a potentially selective and therefore limiting step to the sample analysis even when online-derivatization is used. This is also has disadvantages with respect to sensitivity for analyte detection in bio-fluids (Pasikanti et al., 2008). Furthermore, preparation of aqueous samples for GC-MS analysis involves a drying step which may result in loss of volatile metabolites. Thus, the major limitations of both direct and indirect GC-MS for metabolomics include limited molecular range, resolution of only volatile and thermally stable compounds, formation of multiple derivatives and sensitivity (which may be dependent on the injection method used (Villas-Boas et al., 2005). The type of MS employed may also affect the sensitivity of detection: for targeted metabolomics the use of quadrupole MS in single ion monitoring mode provides enhanced sensitivity whereas for metabolomic profiling ToF and IT MS can be used for MS/MS analysis to characterize the analytes. Thus, GC-MS is very useful when sample preparation steps are not a concern and ideal when targeted analytes are volatile.

Capillary electrophoresis–mass spectrometry Capillary electrophoresis is capable of high-resolution separation of a wide range of chemical compounds especially the polar and charged compounds (Soga and Imaizumi, 2001). Compared with LC, capillary electrophoresis has relatively higher separation efficiency owing to the plug-flow profile which is generated as a result of the electro-osmotic flow, smaller injection volume, reduced proportion of buffers and organic solvents used. However, a very limited amount of sample volume can be introduced into the capillaries, which leads to poor concentration sensitivity in many detector systems. Guillo et al. demonstrated the use of sulfated β-cyclodextrine-modified micellar electro kinetic chromatography as a tool for urine fingerprinting (Guillo et al., 2004). Barbas et al. studied polyacrylamide-coated capillaries for CE separation and compared micellar electrokinetic chromatography with reversed-polarity CE for separation of urine samples (Barbas et al., 2008). The use of highly sensitive detectors like laser-induced fluorescence or MS can provide higher sensitivity. The coupling of CE-ESI-MS and online sample pre-concentration has aided analysis of biological samples and these studies demonstrate the use of CE for metabolomic analysis of molecules with a wide range of polarities (Cai and Henion, 1995; Schmitt-Kopplin and Frommberger, 2003). Chromatographic separation in CE-MS is similar to that of HPLC-MS in that



D. K. Trivedi and R. K. Iles the sample is carried in a liquid phase. Owing to its high resolution and quick separation times, CE-MS has gained importance in metabolomics. However, CE-MS like CE-MALDI-ToF is more popular for targeted metabolomics than profiling (Huck et al., 2006). Intact protein analysis carried out by Haselberg et al. (2007) and Klampfl (2009) highlights the potential of CE-MS as a hyphenated technology for polar compounds. Sheath-flow CZE-ESI-MS/MS has been used for targeted profiling of amino acids in urine (Desiderio et al., 2010). Barbas et al. (2011) have demonstrated successful uses of CE-MS for nontargeted metabolic fingerprinting.

Samples, data and metabolomic analysis Metabolomics, like genomics and transcriptomics, is capable of generating huge amounts of data but the approach to making sense of that data has to be clear from the start of experiments in metabolomics. Typically we are assailed with multivariate analysis statistic programs and the inevitable cry for the need for large sample size. The magnitude of data generated can become overwhelming, but it is essential not to lose sight of a simpler big picture. The data generated is always going to be reduced to the simplest possible to answer the question so you should not lose sight of the question: the first question in a global metabolomics study will be how many analytes can I separate? In a targeted metabolomics study can I detect or resolve the desired metabolites so that other molecules and matrix do not interfere with its measurement? This is essentially an analytical method development task and it will be optimally achieved by making a pool of sacrificial aliquots from control and/or separately pathological samples. From this pool separation and detection will be optimized and if targeted metabolomics the desired molecules positively identified. The data analysis systems will give a matrix of data: ions with m/z values, retention times and relative intensities. It is essential that the relative intensities are not interpreted beyond a threshold for positive detection above baseline. The reason is that the analyte amount in metabolomics is a dynamic biological phenomena; this is unlike most measurements made in pathology in which biomarkers are generally held at homeostatic levels. This is not to say that the levels are not of importance, but that they are unlikely to be normally distributed and extremes will have influenced the apparent average levels found in the pools. The second question will be, do any metabolite molecules vary between the pathological condition(s) and controls? This can only be determined by running all controls and all pathological samples individually using the developed method. In the interpretation of the data a focus favoured is the identification of a few metabolites that are unique or nearly unique to the pathological condition. This is a favoured approach by the diagnostic technology sector as such identified biomarkers can be adapted for measurement on established clinical analysis platforms and in particular those based on immunoassay. However, aside from absolute quantification of the numerous resolved metabolites, like genomics and transcriptomics, the relative changes in multiple metabolites can be analysed directly from spectra and may prove more diagnostic than measurement of individual markers (Trivedi et al., 2012; Trivedi and Iles, 2012)


Conclusion There is vast number of techniques available for studying a urinary metabolome. However for generating a metabolic fingerprint that is as close a representation as possible to original biological state, it is important to understand the urinary biochemistry. Lan et al. (2010) carried out metabolomics study using HPLC with UV detection and demonstrated that UV based methods have several disadvantages that include lack of structural information and lower sensitivity. The authors reported that, despite these drawbacks, HPLC with UV detection can be a cost-effective way of carrying out preliminary metabolomic studies. According to Orešič (2009), for the study of lipids the drawbacks mentioned by Lan et al. (2010) can be easily overcome by use of UPLC either on its own or coupled to MS to enhance spectral quality, obtain faster separation and thus allow detection of more metabolites. Hassan-Smith et al. (2012) in their recent study preferred coupling HPLC to MS or NMR to study the metabolomics of multiple sclerosis. By generating metabolite profiles the authors have suggested that monitoring disease progression, prognosticating and guiding therapeutic decisions may be possible in future. Other reviews have suggested that, although the majority of the published LC-MS studies for global metabolite profiling are based on using RPLC, it is not well suited for the polar and/or ionic analytes (Theodoris et al., 2011). An orthogonal analysis with RPLC/MS should provide the holistic view of the urinary metabolome including the nonpolar analytes. However, in order to understand omics and closely relate it to real clinical physiology, metabolomics should be connected further with other omics like genomics, transcriptomics and proteomics – an ‘ultraomics’ approach that tells more than a chapter in a book – the whole story.

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On writing: just do it.

Target it all right, but do not forget the torchbearer.

Physical activity in primary stroke prevention: just do it!

How I Do It.

Do it for love.

Cystourethroscopy in the cat: What do you need? When do you need it? How do you do it?

Teams Do It Better!

Schizotypy--do not worry, it is not all worrisome.

Do it right the first time: advice for cornea authors.

[How I do it: myelomeningocele].

"Do-it-yourself" mat table.

Would you do it again?

Chimpanzees in trees do it.

Let us do it ourselves.

Pericardiocentesis – How to do it.

Sex Chromosomes Do It Differently.

Lymphadenectomy: how to do it?

3D transoesophageal echocardiography in the TAVI sizing arena: should we do it and how do we do it?

Individualizing therapy: do we have the tools to do it?

Plant reproduction and environmental noise: how do plants do it?

Laparoscopic middle pancreatectomy: how do I do it?

Should we treat pyrexia? And how do we do it?

Depression: you see it--but what do you do about it?

Ask not what your journal can do for you - ask what you can do for it.