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: urinarymetabolomics –establishing clinicallyrelevant baselinesDrupad 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 tochoose 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 ahighly 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 negatethe need for a pre-separation but specific detection and quantification of less abundant analytes in targeted metabolomics mayrequire concentration of the ions by methods such an ion trap MS. In addition, the inherent variability of metabolomic profilesneed to be established in appropriately large sample sets of normal controls. This review aims to explore various techniques thathave been tried and tested over the past decade. Consideration is given to various key drawbacks and positive alternativespublished by active research groups and an optimum combination that should be used for urinary metabolomics is suggestedto generate a reliable dataset for baseline studies. Copyright © 2014 John Wiley & Sons, Ltd.

Keywords: chromatography; metabolomics; shotgun analysis; urine

* Correspondence to: D. K. Trivedi, Manchester Institute of Biotechnologyand 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, Universityof Manchester, M1 7DN, UK

c MAP Diagnostic Ltd, Ely, Cambridgeshire, UK

Abbreviations used: DIMS, direct injection mass spectrometry; HILIC, hy-drophilic interaction liquid chromatography; IRS, infrared spectroscopy;MALDI, matrix assisted laser desorption; RS, Raman spectroscopy.

IntroductionMetabolomics using a variety of analytical technologies hasattracted 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/biomarkerresearch (Koek et al., 2011; Zhang et al., 2008a, 2008b;Ramautar et al., 2011; Vinayavekhin et al., 2010; Quinones andKaddurah-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 instrumentaltechniques in biomedical research has increased the interest inthe global metabolic profiling. Recently untargeted as well astargeted metabolomics have revealed previously unknownanalytes of interest, for example, Andersen et al. (2014) used liquidchromatography–mass spectrometry (LCMS) as a screening toolfor estimating patient compliance and Reinke et al. (2014) demon-strated key relationships between biomarkers and pathogenesis ofmultiple sclerosis using metabolic profiling. Metabolomics is nolonger about just discovery of biomarkers. It has evolved in recentyears to study patterns in disease or a healthy state. Steffen et al.(2014) recently showed the use of metabolomics biomarkers forstudying dietary patterns and Calvani et al. (2014) used nuclearmagnetic resonance (NMR)-based metabolomics to establish asignature of patterns of ageing in mice – demonstrating versatilenature of metabolomics. There is an array of methods used formetabolomics studies including NMR, gas chromatography (GC)2D electrophoresis, capillary electrophoresis (CE), high-performaceliquid chromatography (HPLC)-MS (Issaq and Blonder, 2009) anda variety of mass spectrometry approaches. No single techniquecan be used on its own; however, owing to the limitation of

Biomed. Chromatogr. 2014 Copyright © 2014 John

technologies available within a laboratory, protocols are oftendeveloped using a single detection technology coupled to aseparation technique, for example, GCMS, HPLC MS, CE-MS. Theinformation generated from such combined devices requiresmultivariate analysis and in some cases pattern-recognition soft-ware to analyse the complex data sets that arise from thesetechniques. The final stage in metabolomics studies is all too of-ten an attempt to identify a single or limited set of discriminatingsignals to a defined molecule using NMR and/or MS in conjunc-tion with database searching and/or reference to commercialstandards (Trivedi and Iles, 2012). Biomarker discovery is oftenthe preconceived hunt that a single new biomarker can beidentified that defines the pathological condition or change. Thiswould fit within a clinical diagnostic industry in which immunoas-says to that new biomarker can fit within the current technologyplatforms. However, reality and the power of metabolomics liein a more complex simultaneous detection and relative

Wiley & Sons, Ltd.

D. K. Trivedi and R. K. Iles

quantification of multiple analytes: the diagnostic and exploratorydissection of a phenotypic pathology revealed in the relativechanges of all those analytes and not just an absolutequantification of one.

Sample collection and preparationThere is a huge variety of ‘metabolomes’ that can be studied byusing various biological fluids. Bio-fluids incorporate functionand phenotypes of many different areas of the body; thus, bio-fluids are usually complex. Furthermore, the diversity in pheno-type is affected by genome and environmental factors so pathol-ogies may display considerable variability.Within a biological system the metabolites are in a constant

metabolic flux. Thus, the experimental design must be robustto obtain statistical significant results. The size of populationdepends on the level of biological occurrence and availabilityof both sample and finances. The sample set must be a repre-sentative set of the whole population. Bio-fluid samples arecollected either noninvasively (urine and saliva) or invasively(serum, cerebrospinal fluid or plasma). Certain invasive samplecollection techniques could affect the metabolome, for example,needle prick could stimulate the release of catecholamine and10 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 pro-cesses so that a snapshot of biological composition can beobtained at the time of sample collection. Usually additionof 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 becarried out. The nature of the matrix and the analytes ofinterest should be carefully considered for a metabolomicsapproach. Environmental samples can be prone to microbialdegradation and organic chemicals like proteins in samplesmay degrade with time (Suzuki et al., 2004). In order to avoidsample-to-sample variability owing to degradation and samplevolatility, careful sample handling is necessary. The sample storageconditions for certain analytes may play an important role inminimizing degradation by heat, light and air (Kirchherr andKuhn-Velten, 2006). Thus, storage of such samples at optimumtemperature, in the dark and in the presence of antioxidants isessential to generate accurate metabolomics data.The sample preparation is dictated by the metabolomics

approach that is being studied, for example, in metabolomicsprofiling, the samples can be analysed immediately followingdilution or after protein removal step. The selection of diluentsdepend on the selected analytical technique, that is, NMRsolvents are deuterated, MS solvents are volatile buffers, LCsolvents are chosen based on desired polarity, etc. In contrast,in studies involving targeted metabolomics, extensive samplepreparation is usually carried out to minimize interference andthus improve signal-to-noise ratio. In mass spectrometry manydetergent or other agents used to halt further (quench) dynamicmetabolism of the sample ex-vivo also quench the physio-chem-ical process of ion generation, Thus to avoid rendering a sampleunsuitable and manipulating any available information from theurinary metabolome, we recommend minimal edition of sol-vents and detergents during sample preparation protocols(Trivedi et al., 2012).

Copyright © 2014 Johnwileyonlinelibrary.com/journal/bmc

Analytical technologies used in metabolomicsAnalytical technologies used in metabolomics can be divided intotwo main types: separation techniques and detection techniques.The use of detection techniques like MS and NMR as standalonesystem 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 ontesting drug safety (Powers, 2009; Schnackenberg and Beger,2008), diagnosis of diseases (Tiziani et al., 2009), identificationof natural products (Halabalaki et al., 2014) and therapy moni-toring (Mancini et al., 2009). The Consortium for MetabonomicToxicology project has been able to show the applicability ofNMR for urinary metabolomics (Linden and Lawhead, 1975;Lindon et al., 2005) with high sensitivity and specificity. NMR iscapable of detecting a wide range of urinary analytes includingsugars, ketones, organic acids, nucleosides, steroid and fattyacids. Use of NMR metabolomics is widely accepted in makingpharmaceutical formulations (Sanchez et al., 2008). Shamsipuret al. (2007) used 19F NMR assay for the antipsychotic drug halo-peridol in human serum and pharmaceutical formulation. Heweret al. (2006) used 1H NMR-based metabonomic techniques todistinguish between HIV-1 positive/AIDS patients on antiretrovi-ral treatment and HIV-1 negative individuals. NMR can be a high-throughput technique that proves helpful when analysing a fewhundred samples in a day. NMR can be used to study magneticisotopes, that is, the isotopes that possess an angular moment orangular spin that is associated with the magnetic moment. A fewwell-known magnetic isotopes include 13C, 1H, 19F, 14N, 17O, 31Pand 33S. Routinely used nuclei for the metabolomics analysis ofbiological or pharmaceutical mixtures include 1H (the most sen-sitive), carbon-13 (13C), fluorine-19 (19F) and phosphorus-31 (31P).The energy differences between the quantized orientations ofdifferent nuclei are very small, in the range of microelectron-volts, and hence the NMR spectroscopy suffers from relativelylow sensitivity. The sensitivity may not improve even with themost advanced instruments and at times requires at least micro-mole concentrations of the analytes (Shachar-Hill, 2002). Thiscan be partly compensated for by the abundance of molecularinformation that can be obtained using typical NMR spectra(Eisenreich and Bacher, 2007). Despite the relative lack ofsensitivity, the nondestructive nature of NMR analysis allowsthe sample to be intact for subsequent analysis by any othertechniques. A combination of chemical shift, spin–spin couplingand relaxation or diffusion facilitates rapid identification ofmetabolites. The hydrophobicity of analytes affects the relaxationtimes and eventually peak broadening and peak overlapping canbe seen (Wishart, 2008). The response of every proton beinguniform in samples, the chemical shift in only one standard canbe sufficient for quantification of urinary metabolites, but whenmetabolites are in very low concentrations, their detection couldbecome a challenge using NMR. Thus, analytes present at lowconcentration, such as nucleosides or hydrophobic analytes likesteroids or fatty acids, are not detected efficiently. Carr–Purcell–Meiboom–Gill pulse train can be used to remove resonance frommacromolecules when studying small molecules in order toincrease sensitivity. However, the variability in protein levelswithin a sample can impact metabolomic studies (Van et al.,2003) as such additional steps can increase the analysis time

Biomed. Chromatogr. 2014Wiley & Sons, Ltd.

Table 1. Different mass analysers that are used in variouscombinations to provide better selectivity

Analyser Principle

Magnetic sector Momentum of ionsElectric sector Kinetic energy of ionsQuadrupole Trajectory stability of ion in

oscillating electric fields toseparate ions based ontheir m/z values

Ion trap Resonance frequency of iontrapped in 2D or 3D

Time of flight Flight time of ionFourier transform orbitrap Resonance frequency of ionFourier transform ioncyclotron resonance

Resonance frequency of ion

Choosing correct tools for shotgun urinary metabolomics

by up to 20min per sample, hence limiting the utility of NMRas a high-throughput technique.Thus, NMR is a specific but nonselective technique and,

because of its nonselective nature, all the low molecular weightcompounds in the sample are detected simultaneously in asingle run. The bio-fluid can be studied using magic-angle spin-ning technology with minimal sample preparation (Ratai et al.,2005). During sample analysis solvent suppression methodshave to be implemented in order to reduce background noisefrom protonated solvent residues. However, this leads to lossof spectral information at that frequency and causes waterresonance in the immediate surrounding areas, which leads toa further loss of information. Post-analysis data processing isrequired sometimes when instrument-to-instrument variationsare observed to influence the metabolome (Bailey et al., 2004).NMR spectrometric reference libraries are not as large as thoseavailable for mass spectrometry. This makes it difficult to identifymetabolites.Targeted NMR data can be analysed using a spectral binning

approach; this is a chemometric technique in which the spectralarea of interest is first selected and then metabolites in thatregion are identified and quantified. This targeted profilingapproach will limit the possibility of new unknown biomarkerinvestigation.Enhanced resolution can be achieved by coupling HPLC with

NMR (Cloarec et al., 2007). LC-NMR to an extent overcomes thesensitivity issues of NMR alone (Alexander et al., 2006; Wannet al., 2005). HPLC-NMR has been used successful for urine-relatedmetabolomics studies in rats (Akira et al., 2011). Solid PhaseExtraction (SPE) trapping interface allows online concentrationand purification of very low-level minor components (Godejohannet al., 2004; Yalçın and Yüktaş, 2006; Spraul et al., 2003). Althoughthe comparison of NMR–spectral fingerprints is gaining popularityto understand differences between two sample sets, only a limitednumber of metabolomic studies have used NMR to comparehuman metabolomes (Lenz et al., 2003; Kohl et al., 2012).

Mass spectrometry for metabolomicsMS is a very sensitive and robust technique in spite of thediverse range of small molecules present and therefore repre-sents a very capable method for metabolomics analysis. It hasboth logistical and analytical advantages over NMR but NMR’skey limitations is sensitivity, which makes it usable only whenthe 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 wellas for structural identification. Compounds can be directlyidentified by their m/z values from direct injected into MS, orcan be pre-separated using chromatography or electrophoresistechniques prior to injection. Chromatography coupled to MSprovides better detection, identification and quantification asresolution is enhanced and signal quenching by abundant ionspecies or matrix is greatly reduced.In MS, a charged analyte ion is subjected to an electromag-

netic fields which determine its passage to a detector. Massspectrometers are split into two functional parts, an ionizationchamber and a mass analyser. The way the electromagnetic fieldis designed to interact with charged ion is always a function ofthe mass to charge ratio of that ion (m/z). Thus in mass sectorinstruments the ions are deflected along a circular path in aradius that is directly proportional to the m/z. In quadrupole


mass spectrometer the accelerated ion flight is focused on tothe detector at specific electromagnetic frequencies and themass analysers sweep across m/z range, recording hits on thedetector at specific calibrated electromagnetic frequencies. Intime-of-flight detectors, the time it takes for the ions to travelalong a long flight tube and recorded at a detector is propor-tional to their m/z. In ion trap analysers generated ions arecollected in an electromagnetic trap (increasing their con-centration) and then expelled at specific m/z to collide with adetector (see Table 1).

Ionization methods are equally as variable: in magnetic sectoranalysers the gas-phased ions of the analytes are formed byelectron ionization (hard ionization). A high-energy beam ofelectrons displaces an electron from the molecules to form aradical cation/anion (or molecular ion). Unstable molecular ionsare fragmented further into smaller ions. This is a rather harshand destructive method not suitable for larger organic mole-cules and certainly not proteins as the fragmentation is toodestructive. More gentle (or soft) ionization are used the mostcommon being electrospray ionization (ESI) and matrix assistedlaser desorption (MALDI): in ESI compounds in a solvent areforced through a nano-bore capillary which has a high voltagepotential difference applied. The nano-spray that is ejected ishighly charged and as the solvent rapidly evaporates theremaining compounds are left multiply charged. In MALDIsamples are mixed with concentrated heterocyclic compoundsand allowed to crystalize as a small dry spot. A laser is fired atthe spot and the energy absorbed by the heterocyclic matrix,which forms a reactive charged gas plume. Sample compoundsare also thrown into that reactive matrix gas plume and chargeis transferred to the compounds in a nondestructive reaction(see Table 2).

The advantages of ESI and MALDI is that large molecules canbe ionized without being destructively fragmented. MALDItends to form predominately singly charged large molecules,although double- and triple-charged forms are found, while ESIforms a range of highly charged forms of the molecule andanalyser/detector data has to be processed (or deconjugated)for interpretation as mass spectra. MADI is particular suitablefor ionization of large proteins.

The wide range of available ionization and analysers, as wellas the option of using hybrid instrument incorporating sequen-tial mass analysis, means that the type of MS used depends on

Wiley & Sons, Ltd. wileyonlinelibrary.com/journal/bmc

Table 2. Ionization methods for mass spectrometry

Ionization Principle and features

Electron bombardment Hard Ionization – suitable for smallcompounds and high resolutionof isotopes

Electrospray (ESI) Soft Ionization – giving multiplecharges suitable for couplingto pre-separation by liquidchromatography

Matrix assisted laserdesorption (MALDI)

Soft ionization – giving limitedcharge transfer suitable foranalysis of large proteins andother macro-molecules


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

Direct injection mass spectrometryDirect injection mass spectrometry (DIMS) is a high-throughputtechnique that can be used to infuse samples directly into MS(Biasioli et al., 2011). Various ionization and analyser combina-tions are used for DIMS. ESI and related spray techniques (suchas atmospheric pressure chemical ionization) has meant thatpolar as well as nonpolar metabolites can be detected. Proton-transfer-reaction MS is used for volatile organic compounds(Lindinger and Jordan, 1998; De Gouw et al., 2003), and selectedion-flow-tube MS is also used for all volatile compounds(Olivares et al., 2011; Storer et al., 2011). However, by far thesimplest method of direct mass spectrometry is via MALDI. Herethe biological microliter quantities of a bio-fluid sample aremixed with a suitable matrix, crystalized and soft ionizationresults from laser exposure. However, the matrix signal is over-whelming so only masses far higher than the matrix m/z canbe detected so small molecule metabolomics are not suitablefor this techniques. Nevertheless, the resolution and accuracyof these MS approaches are continually improving to meet thestandards required to allow a valid metabolomics study to becarried out. In DIMS, as already refered to, the matrix plays animportant role in enhancement or suppression of ionization ofa sample owing to the concentration of the sample. If any matrixeffect is suspected it could be overcome by using isotopicanalogues of metabolites of interest as internal standards. Newmethods of sample injections into a mass spectrometer havehelped nullify matrix effects such that desorption electrosprayionization and extractive electrospray ionization are thepreferred methods for small to medium-sized (~4000m/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 posi-

tive and negative modes and, hence, dual ionization mode canbe used while carrying out metabolic profiling (Lei et al., 2011).ESI is a soft ionization technique suitable for DIMS and coupledto a chromatographic separation if required (Schroder, 1996).


Biological samples can be analysed with relatively good specificityand sensitivity by infusing the sample into an MS equipped withan ESI source. Fourier Transform Ion Cyclotron Resonance MassSpectrometry (FTICR-MS) has additional capability to give accuratemass resolution and sensitivity. However, structural isomers ofthe 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 massrange are present in the sample. This may be overcome by useof ToF MS and algorithms in order to classify and comparedifferent mass spectra. The m/z vs intensity matrices generatedin these mass spectra can be easily exported to external multi-variate analysis software for further statistical analysis.

Vibrational spectroscopyRaman spectroscopy (RS) and infrared spectroscopy (IRS) havebeen widely used for metabolic fingerprinting without the useof any derivatizing reagents and without destructive ionizationof the sample (Li et al., 2012). The vibrations and rotations ofmolecular functional groups are measured using optical spec-troscopy. The electronic excitation, change in vibration and/orchange in rotations take place when the sample is irradiatedand energy exchange occurs. IRS uses the IR region of thewavelength spectrum whereas RS uses a monochromatic beam(visible light or UV region). The transition event depends onthe type of irradiation. Vibrational spectroscopy is not as sensi-tive as MS and does not facilitate the identification of analytes.Fourier transform-infrared is widely used form of IRS. TheRaman-effect is very weak and hence a highly sensitive andoptimized system is required for detection of unknown metabo-lites. The sample may overheat through the intense laser radia-tion if the system is not optimized. This can cause the sampleto degrade which in turn will give meaningless Raman spectra.The fingerprinting analysis is relatively inexpensive using vibra-tional spectroscopy and the sample preparation and analysis isextremely quick. Thus, IRS and RS act as complimentary to eachother for detection.

Liquid chromatographyLiquid chromatography (LC) is one of the most popular chro-matographic techniques used for metabolite identification andquantification as the complexity of the sample is reduced byseparation before detection (Xiao et al., 2012). LC is a versatileseparation technique that can be used for separation of widerange of molecules. The urinary metabolites with a wide rangeof polarity require more than one type of column chemistry, thatis, the use of orthogonal techniques for separation of polar aswell as nonpolar metabolites.Normal phase chromatography is based on using a polar

stationary phase such as silica and a nonpolar mobile phasesuch as hexane. In this separation mode retention increases withmore hydrophilic or polar stationary phase. The relative strengthof stationary phase decreases as the group around the stationaryphase ligand gets bulkier. In reverse-phase liquid chromatogra-phy (RPLC), as the name suggests, the stationary phase is nonpo-lar usually an alkyl chain chemically bonded to silica and a polarmobile phase. In RPLC retention of an analyte is related to its hy-drophobicity. Less polar analytes interact with the stationaryphase more strongly than polar analytes. Usually weak or mod-erately polar compounds can be easily separated using either



of these techniques. The RPLC is compatible with MS but thenormal-phase chromatography is not compatible with MS owingto the nature of solvents used. The retention mechanism isbased on competition in both techniques between sample andmobile phase molecules for binding with localized stationaryphase (Snyder et al., 2009).RPLC, however, cannot be used for the retention of very polar

analytes like carbohydrates even with 100% aqueous mobilephase (Dos Santos et al., 2009). Hydrophilic interaction liquidchromatography (HILIC) without being named as such has beenaround since 1975 and routinely used for analysis of sugars(Linden and Lawhead, 1975; Palmer, 1975). It is based on hydro-gen bonding interaction and not just charge. In 1990 Alpertadvanced the idea of HILIC with his work on separation ofpeptides, nucleic acids and other polar compounds with thismode of chromatography. Since then HILIC has become a popu-lar separation technique for assays based on MS detectionbecause the eluents used for HILIC are various combinations ofacetonitrile in water or volatile buffer which are compatible withMS ionization techniques such as electrospray (Spagou et al.,2010; Nguyen and Schug, 2008).

Hydrophilic interaction liquidchromatography – HILICIn contrast to RPLC, HILIC can provide strong retention of polarcompounds. The mechanism of separation is similar to normal-phase liquid chromatography except that an aqueous organicmobile phase is used instead of a nonpolar solvent. Thus, itcan be used in combination with ESI mass spectrometry. HILICmay give a better separation for very strong polar compoundsowing to its less viscous organic mobile phase. Gritti et al.(2010) have also suggested that the gain in efficiency byswitching from RPLC to HILIC is in the order of 1000-fold increasein the resolution of hydrophilic analytes (Sequant, 2007)The mechanism of separation in HILIC is not yet clear but re-

searchers suggest that hydrogen-bonding (Nguyen et al., 2010),dipole–dipole movement (Soukup and Jandera, 2012) and/orhydrophilic partitioning could be responsible for retention of polarcompounds (Karatapanis et al., 2011). The nature of surface of thestationary phase can vary the extent of hydration of polar mole-cules and hence immobilized water molecules, with varying effect.The hydrophilic partitioningmodel, a theory based on circumstan-tial evidence, suggests that a hydrophilic surface holds waterwhen exposed to mixtures of organic solvent and water (Guoand Gaiki, 2011).

Commonly used HILIC stationary phasesThe diversity of HILIC stationary phase, supporting materials andsurface chemistry has improved over time to meet variousrequirements of separation science. The basic HILIC columnsinclude plain silica, a chemically bonded neutral polar com-pounds, ion-exchange and zwitterionic residues; the differenttypes 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, adsorptionand ion exchange. This may also depend on the nature ofanalytes that are separated using diversely manufactured HILICcolumns. At high pH the silanol groups ionize to a greater extent


and cation exchange aids the retention mechanism. As a resultof the hydrosilation process during production, about 95% ofSi–O–H groups are removed making the surface of the silica lesspolar and gives improved reproducibility of retention (Gomezand Sandoval, 2010). Various columns packed with poroussuperficial particles or fused core porous silica are also used forcertain high-throughput HILIC separation for metabolomics.(Hsieh et al., 2009; Gika et al., 2008).

Chemically bonded solid-phase silica is prepared by chemicalmodification of silica gel by reactions with trialkoxysilanes. Thetrialkoxysilanes contain polar and alkyl groups that increaseretention of many compounds when exposed to an increasingacetonitrile content of mobile phase. Examples of silica-basedbonded stationary phases for HILIC applications include polysuccinimide - or sulfoalkylbetaine-silica. The advantage of usingbonded silica stationary phases is that it is capable of forming apolymeric network to form a water-rich layer that can in turnimprove partitioning mechanism for certain polar analytes (Orthand Engelhardt, 1982).

Various charged analytes like polysaccharides, oligosaccha-rides, glycols and glycerol are efficiently separated using ion-exchange or zwitterionic charged stationary phase surfaces. Ithas been noted that columns packed with polymer particlesfor ion-exchange show lower separation efficiency than silica-based zwitterionic or ion-exchange HILIC stationary phases(Jandera, 2008). The zwitterionic phase for separation of inor-ganic anions and cations was demonstrated by Jiang and Irgum(1999). The active layer on the silica gel or polymer in zwitter-ionic stationary phases normally contains one strong acid groupand one basic group separated by a short alkyl spacer. Thesegroups, owing to their charges, can simultaneously separateanions as well as cations. The polar interactions, like hydrogen-bonding and dipole–dipole moments, are associated withprimary retentionmechanism for such stationary phases (Sequant,2011). However, there may be weak electrostatic forces affectingseparation owing to oppositely charged groups bonded to thestationary phase.

HILIC mobile phasesIn HILIC, the initial mobile phase contains a high organic contentand relatively low aqueous content. The organic concentration isgradually changed until acceptable sample retention of theanalyte(s) is achieved. The choice of organic solvents can signif-icantly affect the retention mechanism in HILIC chromatography,for example, the elution strength of HILIC solvents increases withthe increase in solvent polarity. Acetonitrile is the only HILICsolvent known to show no proton–donor interactions (Quiminget al., 2007). Owing to this property, it does not exhibit stronghydrogen bonding like methanol, ethanol or 2-propanol. Ace-tone shows similar polarity to acetonitrile but has inferior selec-tivity and provides lower signal intensity on MS (Fountain et al.,2010). The pH of buffers used in the HILIC affects the retentionand selectivity of ionizable samples (Orth and Engelhardt,1982; Hao et al., 2008). With increased concentration of bufferthe hydrogen-bonding interactions increase between analytesand the stationary phase, leading to better retention of nonionicpolar analytes. However, if the analytes of interest are ionic, thenthe buffer ions may compete with ionic analyte and displace ion-ized sample molecules from the stationary phase. In Zwitterionic(ZIC)-HILIC chromatography a high percentage of organic solventin the eluent increases the retention of the solutes. However, with



these columns, at least 3% water in the mobile phase is recom-mended 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 beaccomplished by increasing the polarity of the mobile phasewith time, that is, by decreasing the concentration of organicsolvent. For HILIC solid phases that contain a charged groupthere is also a possibility of controlling retention by increasingthe buffer concentration during an organic to aqueous gradientto disrupt electrostatic interactions with the solute. For analysisof samples containing molecules with wide range of polaritiessuch 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 waterin the aqueous layer of stationary phase is replenished from theeluent between analyses. Thus, HILIC stationary phases are notsuitable for fast gradients analyses with short equilibration times(Jandera, 2011). This is primarily due to a rapid change in thecomposition of water layer in the stationary phase which rendersHILIC columns less tolerable to quick gradient changes.

Mechanism of ZIC-HILICThe ZIC-HILIC stationary phase contains a covalently bonded,zwitterionic sulfobetaine-group as the functional group bondedto the silica (Fig. 1). Exposed to an aqueous–organic mobilephase, a water-rich layer is established within the stationaryphase. The partitioning mechanism of solutes leads to separa-tion of solutes from the eluent. This process is exothermic andis dependent on various factors like acidity or basicity of the sol-utes, the dipole interactions and hydrogen bonding. In ZIC-HILIC,the stationary phase adds an extra dimension of separationmechanism to analyte retention. However, buffers or salts arerequired in the mobile phase to disrupt the interactions forsuccessful elution. The use of buffers with high salt concentra-tion is not recommended for methods based on mass-spectrometric detection. However, with ZIC-HILIC lower concen-tration of buffers can be used as the electrostatic interactioneffect is lowered by ionic-groups present on the stationaryphase. The mechanism of HILIC partitioning and retention isstill not fully understood and work is ongoing in this area(Sequant, 2011).

RPLC-MS in metabolomicsReversed-phase liquid chromatography is arguably the mostpopular mode of analyte separation used today, as it allows adiverse range of compounds to be separated. The variety of

Figure 1. A typical ZIC-HILIC stationary phase provides a uniqueenvironment, particularly capable of solvating polar and chargedcompounds via weak electrostatic interactions, as opposed to the strongelectrostatic 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, polarembedded phases as well as specialized phase. Over the pastdecade the number and variety of commercially available RPLCstationary phases that contain a polar embedded functionalityhas 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-MSfor analyses metabolite has been used for years. It is no surprisethat 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 andNorthern, 2010) like flavonoids (Tache et al., 2012), glycosylatedproteins 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 phenolicacids (Bravo et al., 2007). Owing to the diversity of chemicalnature of small molecules in bio-fluids, the elucidation of themetabolome is particularly challenging. Furthermore, couplingHPLC to MS detection systems like ion-trap helps in obtaininga spectrum for identifying the isolated rare abundance analytesof interest by trapping those ions and hence increasing sen-sitivity further. Chong et al. (2010) using LC-MS successfullyseparated and identified nucleotides and nucleosides frommammalian cell profiles. LC-MS provided better metabolomicprofiles and marker identification than any other analyticaltechnique 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 generallyelute in the void volume in RPLC. Hence, they are very difficultto separate and ionize. Certain nonpolar analytes that are notwell retained on a HILIC column may be well be retained onRPLC columns. Although the separation and retention of polarmetabolites can be achieved using RPLC, by using a lowerproportion of organic solvent in the mobile phase, a lower MSionization and detection response is a drawback. In contrast,the number of analytes detected by MS separated by HILIC ishigher even though the efficiency of HILIC columns may belower than that of RPLC columns (Chen et al., 2009). The recog-nition of this separation advantage for MS-based proteomicsappears to be reflected in the increasing number of publicationsusing HILIC (Theodoridis et al., 2012; Wilson et al., 2005; Lenz andWilson, 2007).Nevertheless, reversed-phase gradient elution with varying

run times and varying conditions coupled to electrospray ioniza-tion mass spectrometry seems to be the primary method ofchoice for MS metabolomics (Ayrton et al., 1998). Generallythe studies that have been reported in the literature involveuse of conventional columns that are 2.1–4.6mm in diameter,5–25 cm in length and packed with 3–5μm particles(Theodoridis et al., 2008). In 2002 and 2004 Plumb reportedthat using by using reverse-phase UPLC column systems morebio-fluids metabolites can be separated and hence, detectedthan 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 effecton ion suppression or enhancement (Wilm and Mann, 1994).However, pre-analytic matrix effects of the bio-fluids themselvesare now a major concern in metabolomics; for example severalauthors have shown that in urine samples the continued activityof endogenous urease interferes with detection of important



urinary metabolites like hypoxanthine, tyrosine, citrate andacotonic 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 parti-cle size provides better resolution and hence, a better massspectra. If 3.5–5μm particles do not provide the required resolu-tion required then UPLC may be used.Very polar analytes can be separated using HILIC which is

orthogonal to RPLC. Using HPLC-MS with orthogonal chromato-graphic chemistries like RPLC and HILIC, polar and nonpolaranalytes can be resolved and detected. Nontargeted meta-bolomics can be carried out by coupling HPLC to Time of Flight(ToF) or Ion Trap (IT) for enhanced sensitivityIt 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 generallybalanced to favour nonpolar metabolites. Urine, however, is apolar environmental matrix and many metabolites are modifiedto be polar for excretion. Thus, separation and detection systemshave to recognize this for urine metabolomic studies and HILIC ismore appropriate. However, modifications to RPLC have shownsuccess in various challenging areas. For example, Wikoff et al.reported the use of capillary RPLC-MS based metabolomic studyfor understanding viral infection induced neurodegenerationwhich has been a relatively unexplored area (Wikoff, 2008).Hyndman et al. (2011) in their review about studies using RPLCand NMR concluded that this metabolomics approach to urinecan be used for diagnosis of bladder cancer. In a review byLakshmanana et al. (2011) the authors highlighted the use ofRPLC/MS along with HILIC/MS for the study of polyamines,glycerol and lipid metabolism pathways related to malariapathology. Thus, a large number of published studies suggestthat the use of RPLC/MS is an effective approach to meta-bolomics as well as an orthogonal approach with HILIC/MS formetabolomic profiling studies.

Gas chromatography–mass spectrometryGC-MS is ideal for metabolomic studies involving volatile andthermally stable polar as well as nonpolar compounds. Electronimpact or chemical ionization MS can be used in order to detectthe separated compounds in GC-MS. In electron impact is ioniza-tion high-energy electrons interact with gas-phased atoms ormolecules to produce ions and the instrument-to-instrumentvariation has been found to be minimal. Hence, reproduciblecharacteristic fragmentation patterns are obtained. Metaboliteidentification or mass spectral matching is achieved by retentiontime or retention index comparisons with pure compounds aswell as by comparison against reference libraries. The availabilityof metabolite libraries for identification in GC and GC-MS is themain advantage this method has over LC-MS metabolomics.Also, the high chromatographic resolution achieved using GCpermits separation of structurally similar compounds that maybe very difficult to separate using LC. Thus, GC-MS metabolomicprofiling has been used to identify biologically active metabo-lites/biomarkers that are either protective or harmful to thestructure and function of heart (Alexander et al., 2010). Chenet al. used GC-MS and other modes of chromatography to studygastric tumor (Chen et al., 2010) and metabolomic studiesrelated to oxidative stress in hepatic tissue samples, urine and


plasma have proven GC-MS to be a very useful analytical toolfor screening and biomarker discovery (Bando et al., 2010).

The application of GC-MS in metabolome analysis can beclassed into two groups:

(1) naturally volatile metabolites – such as ketones, aldehydes,alcohols, esters, furan and pyrrole derivatives, heterocycliccompounds, sulfides, some lipids, isocyanates, isothio-cyanates 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 bemade volatile by derivatization.

Nonvolatile compounds have to be derivatized using agentssuch as N,O-bis(trimethylsilyl) trifluoroacetamide, N-methyl-N-(trimethylsilyl) trifluoroacetamide (Yi-qi et al., 2007) and N-methyl-bis(trifluoracetamide) (Hidvegi et al., 2008). Derivatizationof samples adds a potentially selective and therefore limitingstep to the sample analysis even when online-derivatization isused. This is also has disadvantages with respect to sensitivity foranalyte detection in bio-fluids (Pasikanti et al., 2008). Furthermore,preparation of aqueous samples for GC-MS analysis involves adrying step which may result in loss of volatile metabolites.

Thus, the major limitations of both direct and indirect GC-MSfor metabolomics include limited molecular range, resolution ofonly volatile and thermally stable compounds, formation ofmultiple derivatives and sensitivity (which may be dependenton the injection method used (Villas-Boas et al., 2005). The typeof MS employed may also affect the sensitivity of detection: fortargeted metabolomics the use of quadrupole MS in single ionmonitoring mode provides enhanced sensitivity whereas formetabolomic profiling ToF and IT MS can be used for MS/MSanalysis to characterize the analytes. Thus, GC-MS is very usefulwhen sample preparation steps are not a concern and idealwhen targeted analytes are volatile.

Capillary electrophoresis–mass spectrometryCapillary electrophoresis is capable of high-resolution separationof a wide range of chemical compounds especially the polar andcharged compounds (Soga and Imaizumi, 2001). Compared withLC, capillary electrophoresis has relatively higher separationefficiency owing to the plug-flow profile which is generated asa result of the electro-osmotic flow, smaller injection volume,reduced proportion of buffers and organic solvents used. How-ever, a very limited amount of sample volume can be introducedinto the capillaries, which leads to poor concentration sensitivityin many detector systems. Guillo et al. demonstrated the use ofsulfated β-cyclodextrine-modified micellar electro kinetic chro-matography as a tool for urine fingerprinting (Guillo et al.,2004). Barbas et al. studied polyacrylamide-coated capillariesfor CE separation and compared micellar electrokinetic chroma-tography with reversed-polarity CE for separation of urine sam-ples (Barbas et al., 2008). The use of highly sensitive detectorslike laser-induced fluorescence or MS can provide higher sensi-tivity. The coupling of CE-ESI-MS and online sample pre-concen-tration has aided analysis of biological samples and thesestudies demonstrate the use of CE for metabolomic analysis ofmolecules with a wide range of polarities (Cai and Henion,1995; Schmitt-Kopplin and Frommberger, 2003). Chromato-graphic separation in CE-MS is similar to that of HPLC-MS in that



the sample is carried in a liquid phase. Owing to its high resolu-tion and quick separation times, CE-MS has gained importancein metabolomics. However, CE-MS like CE-MALDI-ToF is morepopular 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 asa hyphenated technology for polar compounds. Sheath-flowCZE-ESI-MS/MS has been used for targeted profiling of aminoacids in urine (Desiderio et al., 2010). Barbas et al. (2011) havedemonstrated successful uses of CE-MS for nontargeted meta-bolic fingerprinting.

Samples, data and metabolomic analysisMetabolomics, like genomics and transcriptomics, is capable ofgenerating huge amounts of data but the approach to makingsense of that data has to be clear from the start of experi-ments in metabolomics. Typically we are assailed with multi-variate analysis statistic programs and the inevitable cry forthe need for large sample size. The magnitude of data gener-ated can become overwhelming, but it is essential not to losesight of a simpler big picture. The data generated is alwaysgoing to be reduced to the simplest possible to answer thequestion so you should not lose sight of the question: the firstquestion in a global metabolomics study will be how manyanalytes can I separate? In a targeted metabolomics studycan I detect or resolve the desired metabolites so that othermolecules and matrix do not interfere with its measurement?This is essentially an analytical method development taskand it will be optimally achieved by making a pool of sacrifi-cial aliquots from control and/or separately pathologicalsamples. From this pool separation and detection will be opti-mized and if targeted metabolomics the desired moleculespositively identified.The data analysis systems will give a matrix of data: ions

with m/z values, retention times and relative intensities. It isessential that the relative intensities are not interpretedbeyond a threshold for positive detection above baseline.The reason is that the analyte amount in metabolomics is adynamic biological phenomena; this is unlike most measure-ments made in pathology in which biomarkers are generallyheld at homeostatic levels. This is not to say that the levelsare not of importance, but that they are unlikely to be nor-mally distributed and extremes will have influenced the appar-ent average levels found in the pools.The second question will be, do any metabolite molecules

vary between the pathological condition(s) and controls? Thiscan only be determined by running all controls and all patholog-ical samples individually using the developed method. In theinterpretation of the data a focus favoured is the identificationof a few metabolites that are unique or nearly unique to thepathological condition. This is a favoured approach by thediagnostic technology sector as such identified biomarkerscan be adapted for measurement on established clinical anal-ysis platforms and in particular those based on immunoassay.However, aside from absolute quantification of the numerousresolved metabolites, like genomics and transcriptomics, therelative changes in multiple metabolites can be analyseddirectly from spectra and may prove more diagnostic thanmeasurement of individual markers (Trivedi et al., 2012; Trivediand Iles, 2012)


ConclusionThere is vast number of techniques available for studying a urinarymetabolome. However for generating a metabolic fingerprint thatis as close a representation as possible to original biological state, itis important to understand the urinary biochemistry. Lan et al.(2010) carried out metabolomics study using HPLC with UVdetection and demonstrated that UV based methods have severaldisadvantages that include lack of structural information andlower sensitivity. The authors reported that, despite these draw-backs, HPLC with UV detection can be a cost-effective way ofcarrying out preliminary metabolomic studies. According to Orešič(2009), for the study of lipids the drawbacks mentioned by Lanet al. (2010) can be easily overcome by use of UPLC either on itsown or coupled to MS to enhance spectral quality, obtain fasterseparation and thus allow detection of more metabolites.Hassan-Smith et al. (2012) in their recent study preferred couplingHPLC to MS or NMR to study the metabolomics of multiple sclero-sis. By generating metabolite profiles the authors have suggestedthat monitoring disease progression, prognosticating and guidingtherapeutic decisions may be possible in future. Other reviewshave suggested that, although the majority of the publishedLC-MS studies for global metabolite profiling are based on usingRPLC, it is not well suited for the polar and/or ionic analytes(Theodoris et al., 2011). An orthogonal analysis with RPLC/MSshould provide the holistic view of the urinary metabolome in-cluding the nonpolar analytes. However, in order to understandomics and closely relate it to real clinical physiology, metabolomicsshould be connected further with other omics like genomics,transcriptomics and proteomics – an ‘ultraomics’ approach thattells more than a chapter in a book – the whole story.

ReferencesAkira K, Mitome H, Imachi M, Shida Y, Miyaoka H and Hashimoto T.

LC-NMR identification of a novel taurine-related metabolite ob-served in (1)H NMR-based metabonomics of genetically hyperten-sive rats. Journal of Pharmaceutical and Biomedical Analysis 2011;51(5): 1091–1096.

Alexander AJ, Xu F and Bernard C. The design of a multi-dimensionalLC-SPE-NMR system (LC(2)-SPE-NMR) for complex mixture analysis.Magnetic Resonance in Chemistry 2006; 44(1): 1–6.

Alexander D, Lombardi R, Rodriguez G, Mitchell MM and Marian AJ.Metabolomic distinction and insights into the pathogenesis ofhuman primary dilated cardiomyopathy. European Journal of ClinicalInvestigation 2010; doi: 10.1111/j.1365-2362.2010.02441.x

Andersen MS, Rinnan A, Manach C, Poulsen SK, Pujos-Guillot E, Larsen TM,Astrup A andDragsted LO. UntargetedMetabolomics as a Screening Toolfor Estimating Compliance to a Dietary Pattern. Journal of ProteomeResearch 2014. Available from: http://pubs.acs.org/doi/abs/10.1021/pr400964s (accessed 5 February, 2014, doi: 10.1021/pr400964s).

Armitage EG and Barbas C. Metabolomics in cancer biomarker discovery:current trends and future perspectives. Journal of Pharmaceutical andBiomedical Analysis 2014; 87: 1–11.

Ayrton J, Plumb R, Leavens WJ, Mallett D, Dickins M and Dear GJ. Appli-cation of a generic fast gradient liquid chromatography tandemmassspectrometry method for the analysis of cytochrome P450 probesubstrates. Rapid Communications in Mass Spectrometry 1998; 12:217–224.

Bailey NJC, Oven M, Holmes E, Zenk MH and Nocholson JK. An NMR-based metabolomic approach to the analysis of the effects ofxenobiotics on endogenous metabolite levels in plants. In 2ndInternational Conference on Biomedical Spectroscopy, Vol. 18,2004; 279–287.

Bando K, Kunimatsu T, Sakai J, Kimura J, Funabashi H, Seki T, Bamba Tand Fukusaki E. GC-MS-based metabolomics reveals mechanism ofaction for hydrazine induced hepatotoxicity in rats. Journal of AppliedToxicology 2010; doi: 10.1002/jat.1591


http://pubs.acs.org/doi/abs/10.1021/pr400964s

http://pubs.acs.org/doi/abs/10.1021/pr400964s


Barbas C, Vallejo M, Garcia A, Barlow D and Hanna-Brown M. Capillaryelectrophoresis as a metabolomics tool in antioxidant therapystudies. Journal of Pharmaceutical and Biomedical Analysis 2008; 47:388–398.

Barbas C, Edgar P, Villasenor MA. Capillary electrophoresis as ametabolomics tool for non-targeted fingerprinting of biological sam-ples. Journal of Pharmaceutical and Biomedical Analysis 2011; 55(4):823–831.

Biasioli F, Yeretzian C, Mark TD, Dewulf J and Langenhove HV. Direct-in-jection mass spectrometry adds the time dimension to (B)VOC anal-ysis. Trends in Analytical Chemistry 2011; 30(7): 1003–1017.

Bowen BP and Northern TR. Dealing with the unknown: metabolomicsand metabolite atlases. Journal of the American Society for Mass Spec-trometry 2010; 21(9): 1471–1476.

Bravo L, Goya L and Lecumberri E. LC/MS characterization of phenolicconstituents of mate (Ilex paraguariensis, St. Hil.) and its antioxidantactivity compared to commonly consumed beverages. Food ResearchInternational 2007; 40(3): 393–405.

Cai J and Henion J. Capillary electrophoresis–mass spectrometry. Journalof Chromatography A 1995; 703: 667–692.

Calvani R, Brasili E, Pratico G, Capuani G, Tomassini A, Marini F, Sciubba F,Finamore A, Roselli M, Marzetti E and Miccheli A. Fecal and urinaryNMR-based metabolomics unveil an aging signature in mice. Experi-mental Gerontology 2014; 49: 5–11.

Chen H, Pan Z, Talaty N, Raftery D and Cooks RG. Combining desorp-tion electrospray ionisation mass spectrometry and nuclear mag-netic resonance for differential metabolomics without samplepreparation. Rapid Communication Mass Spectrometry 2006; 20(10):1577–1584.

Chen J, Wang W, Lv S, Yin P, Zhao X, Lu X, Zhang F and Xu G.Metabonomics study of liver cancer based on ultra performance liq-uid chromatography coupled to mass spectrometry with HILIC andRPLC separations. Analytica Chimica Acta 2009; 650(1): 3–9.

Chen JL, Tang HQ, Hu JD, Fan J, Hong J and Gu JZ. Metabolomics of gastriccancer metastatis detected by gas chromatography and mass spec-trometry. World Journal of Gastroenterology 2010 16(46): 5874–5880.

Chong WP, Yusufi FN, Lee DY, Reddy SG, Wong NS, Heng CK, Yap MG andHo YS. Metabolomics-based identification of apoptosis-inducingmetabolites in recombinant fed-batch CHO culture media. Journalof Biotechnology 2010; doi: 10.1016/j.jbiotec.2010.12.010

Cloarec O, Campbell A, Tseng LH, Braumann U, Spraul M, Scarfe G,Weaver R and Nicholson JK. Virtual chromatographic resolutionenhancement in cryoflow LC-NMR experiments via statistical totalcorrelation spectroscopy. Analytical Chemistry 2007; 79(9): 3304–3311.

Colegate SM and Gardner DR. LC-MS of Alkaloids: Qualitative Profiling,Quantitative Analysis, and Structural Identification. In Modern Alka-loids: Structure, Isolation, Synthesis and Biology, Fattorusso E andTaglialatela-Scafati O (eds). Wiley-VCH: Weinheim, 2007.

De Gouw JA, Goldan PD, Warneke C, Kuster WC, Roberts JM, MarchewkaM, Bertman SB, Pszenny AAP and Keene WC. Validation of protontransfer reaction–mass spectrometry (PTR-MS) measurements ofgas-phase organic compounds in the atmosphere during the NewEngland Air Quality Study (NEAQS) in 2002. Journal of Geophysical Re-search 2003; 108(D21): 4682.

Denery JR, Nunes AAK, Hixon MS, Dickerson TJ and Janda KD.Metabolomics-based discovery of diagnostic biomarkers for oncho-cerciasis. PLoS Neglected Tropical Diseases 2010; 4(10): e834.

Desiderio C, Iavarone F, Rossetti DV, Messana I and Castagnola M.Capillary electrophoresis–mass spectrometry for the analysis of aminoacids. Journal of Separation Science 2010; 33(16): 2385–2393.

Dos Santos PA, David F, Vanhoenacker G and Sandra P. The acetonitrileshortage: Is reversed HILIC with water an alternative for the analysisof highly polar ionisable solutes? Journal of Separation Science 2009;32(12): 2001–2007.

Eisenreich W and Bacher A. Advances of high-resolution NMR techniquesin the structural and metabolic analysis of plant biochemistry. Phyto-chemistry 2007; 68(22–24): 2799–2815.

Fountain KJ, Xu J, Dieh DM and Morrison D. Influence of stationary phasechemistry and mobile-phase composition on retention, selectivityand MS response in hydrophilic interaction chromatography. Journalof Separation Science 2010; 33(6–7): 740–751.

Garcia-de Blas E, Mateo R, Vinuela J and Alonso-Alvarez C. Identificationof carotenoid pigments and their fatty acid esters in an avian integu-ment combining HPLC-DAD and LC-MS analysis. Journal of Chroma-tography B 2011; 879(5–6): 341–348.


Gika HG, Theodoridis GA and Wilson ID. Hydrophilic interaction andreversed-phase ultra-performance liquid chromatography TOF-MSfor metabonomic analysis of Zucker rat urine. Journal of SeparationScience 2008; 31(9): 1598–1608.

Godejohann J, Tseng LH, Braumann U, Fuchser J and Spraul M. Charac-terization of a paracetamol metabolite using online LC-SPE-NMR-MSand cryogenic NMR probe. Journal of Chromatography A 2004;1058: 191–196.

Gomez JE and Sandoval JE. New approaches to prepare hydride silica.Analytical Chemistry 2010; 82(17): 7444–7451.

Gritti F, Pereira A, Sandra P and Guiochon G. Efficiency of the same neatsilica column in hydrophilic interaction chromatography and peraqueous liquid chromatography. Journal of Chromatography A 2010;1217: 683–688.

Gu HW, Chen HW, Pan ZZ, Jackson AU, Talaty N, Xi BW, Kissinger C, DudaC, Mann D, Raftery D and Cooks RG. Monitoring diet effects viabiofluids and their implications for metabolomics studies. AnalyticalChemistry 2007; 79: 89–97.

Guillo C, Barlow D, Perrett D and Hanna-Brown M. Micellar electrokinecticcapillary chromatography and data alignment analysis: a new tool inurine profiling. Journal of Chromatography A 2004; 1027: 203–212.

Guo Y and Gaiki S. Retention and selectivity of stationary phases forhydrophilic interaction chromatography. Journal of ChromatographyA 2011; 1218(35): 5920–5938.

Guy C, Kopka J and Moritz T. Plant metabolomics coming of age.Physiologia Plantarum 2008; 132(2): 113–116.

Halabalaki M, Vougogiannopoulou K, Mikros E and Skaltsounis AL. Recentadvances and new strategies in the NMR-based identification ofnatural products. Current Opinion in Biotechnology 2014; 25: 1–7.

Hamilton JG. Needle phobia: a neglected diagnosis. Journal of FamilyPractice 1995; 41(2): 169–170.

Hao Z, Xiao B and Weng N. Impact of column temperature and mobilephase components on selectivity of hydrophilic interaction chro-matography (HILIC). Journal of Separation Science 2008; 31(9):1449–1464.

Haselberg R, de Jong GJ and Somsen GW. Capillary electrophoresis–massspectrometry for the analysis of intact proteins. Journal of Chroma-tography A 2007; 1159: 81–109.

Hassan-Smith G, Wallace GR, Douglas MR and Sinclair AJ. The role ofmetabolomics in neurological disease. Journal of Neuroimmunology2012. Available from: www.sciencedirect.com (accessed 16 February2012).

Hewer R, Vorster J, Steffens FE and Meyer D. Applying biofluid 1H NMR-based metabonomic techniques to distinguish between HIV-1positive/AIDS patients on antiretroviral treatment and HIV-1 negativeindividuals. Journal of Pharmaceutical and Biomedical Analysis 2006;41(4): 1442–1446.

Hidvegi E, Hideg Z and Somogyi GP. Different reactivities of amphet-amines with N-methyl-bis (trifluoroacetamide) in heated gas chro-matographic injectors. Pharmazie 2008; 63(3): 233–234.

Hsieh Y, Galviz G and Long BJ. Ultra-performance hydrophilic interactionliquid chromatography/tandem mass spectrometry for the determi-nation of everolimus in mouse plasma. Rapid Communications inMass Spectrometry 2009; 23(10): 1461–1466.

Huck CH, Bakry R, Huber LA and Bonn G. Progress in capillary electropho-resis couple to matrix-assisted laser desorption/ionisation–time offlight mass spectrometry. Electrophoresis 2006; 27: 2063–2074.

Hyndman ME, Mullins JK and Bivalacqua TJ. Metabolomics and bladdercancer. Urologic Oncology: Seminars and Original Investigations 2011;29(5): 558–561.

Issaq HJ and Blonder J. Electrophoresis and liquid chromatography/tan-dem mass spectrometry in disease biomarker discovery. Journal ofChromatography B 2009; 877(13): 1222–1228.

Issaq HJ, Van QN, Waybright TJ, Muschik GM and Veenstra TD. Analyticaland statistical approaches to metabolomics research, Metabolomicsin Separation Science 2009; 32(13): 2183–2199.

Jandera P. Stationary phases for hydrophilic interaction chromatography,their characterization and implementation into multidimensionalchromatography concepts. Journal of Separation Science 2008; 31(9): 1421–1437.

Jandera P. Stationary and mobile phases in hydrophilic interaction chro-matography: a review. Analytica Chimica Acta 2011; 692(1–2): 1–25.

Jiang W and Irgum K. Covalently bonded polymeric zwitterionic station-ary phase for simultaneous separation of inorganic cations andanions. Analytical Chemistry 1999; 71(2): 333–344.


http://www.sciencedirect.com


Kamlage B, Maldonado SG, Bethan B, Peter E, Schmitz O, Liebenberg Vand Schatz P. Quality markers addressing preanalytical variations ofblood and plasma processing identified by broad and targetedmetabolite profiling. Clinical Chemistry 2004. Available from: http://www.clinchem.org/content/60/2/399.short (accessed 2 February2014, doi: 10.1373/clinchem.2013.211979)

Karatapanis AE, Fiamegos YC and Stalikas CD. A revisit to the retentionmechanism of hydrophilic interaction liquid chromatography usingmodel organic compounds. Journal of Chromatography A 2011;1218(20): 2871–2879.

Kirchherr H and Kuhn-Velten WN. Quantitative determination of forty-eight antidepressants and antipsychotics in human serum by HPLCtandem mass spectrometry: a multi-level, single-sample approach.Journal of Chromatography B 2006; 843(1):100–113.

Klampfl CW. CE with MS detection: a rapidly developing hyphenatedtechnique. Electrophoresis 2009; 30: S83–S91.

Koek MM, van de Kloet FM, Kleemann R, Kooistra T, Verheij ER andHankemeier T. Semi-automated non-target processing in GC X GC-MS metabolomics analysis: applicability for biomedical studies.Metabolomics 2011; 7(1): 1–14.

Kohl SM, Matthias SK, Hochrein J, Oefner PJ, Spang R and Gronwald W.State-of-the-art data normalization methods improve NMR-basedmetabolomics analysis. Metabolomics 2012; 8(1); 146–160.

Kujara T. Metabolomics: The Frontier of Systems Biology. Springer: Tokyo;2005.

Lakshmanana V, Rhee KY and Daily JP. Metabolomics and malaria biology.Molecular and Biochemical Parasitology 2011; 175(2): 104–111.

Lan K, Zhang Y, Yang J and Xu L. Simple quality assessment approach forherbal extracts using high performance liquid chromatography–UVbased metabolomics platform. Journal of Chromatography A 2010;1217(8): 1414–1418.

Lei Z, Huhman DV and Sumner LW. Mass spectrometry strategies inmetabolomics. Journal of Biological Chemistry Papers in Press 2011.Available from: http://www.jbc.org/cgi/doi/10.1074/jbc.R111.238691(accessed 8 March 2013).

Lenz EM, Bright J, Wilson ID, Morgan SR and Nash AFP. A 1H NMR-basedmetabonomic study of urine and plasma samples obtained fromhealthy human subjects. Journal of Pharmaceutical and BiomedicalAnalysis 2003; 33(5): 1103–1115.

Lenz EV and Wilson ID. Analytical strategies in metabonomics. Journal ofProteome Research 2007; 6(2): 443–458.

Li F, Wang J, Nie L and Zhang W. Computational Methods to Interpret andIntegrate Metabolomic Data, Metabolomics, Dr Ute Roessner (ed.).2012; 99–130. ISBN: 978-953-51-0046-1

Li Q, Zhao C, Li Y, Chang Y, Wu Z, Pang T, Lu X, Wu Y and Xu G. Liquidchromatography/mass spectrometry-based metabolic profiling toelucidate chemical differences of tobacco leaves between Zimbabweand China. Journal of Separation Science 2010; doi: 10.1002/jssc.201000652

Linden JC and Lawhead CL. Liquid chromatography of saccharides.Journal of Chromatography A 1975; 105(1): 125–133.

Lindinger W and Jordan A. Proton-transfer-reaction mass spectrometry(PTR-MS): on-line monitoring of volatile organic compounds at pptvlevels. Chemical Society Reviews 1998; 27: 347–375.

Lindon JC, Keun HC, Ebbels TMD, Pearce JMT, Holmes E and NicholsonJK. The Consortium for Metabonomic Toxicology (COMET): aims,activities and achievements. Pharmacogenomics 2005; 6: 691–699.

Liu X, Zhang L, You L, Yu J, Zhao J, Li L, Wang Q, Li F, Li C and Liu D, et al.Differential toxicological effects induced by mercury in gills fromthree pedigrees of Manila clam Ruditapes philippinarum by NMR-based metabolomics. Ecotoxicology 2011; 20(1): 177–186.

Maharjan RP and Ferenci T. Global metabolite analysis: the influence ofextraction methodology on metabolome profies of Escherichia coli.Analytical Biochemistry 2003; 313(1): 145–154.

Mancini L, Davies L, Friedlos F, Falck-Miniotis M, Dzik-Jurasz AS, SpringerCJ, Leach MO and Payne GS. A novel technique to monitor carboxy-peptidase G2 expression in suicide gene therapy using 19F magneticresonance spectroscopy. NMR in Biomedicine 2009; 22(5): 561–566.

Nguyen HP and Schug KA. The advantages of ESI-MS detection in con-junction with HILIC mode separations: fundamentals and applica-tions. Journal of Separation Science 2008; 31(9): 1465–1480.

Nguyen HP, Yang SH, Wigginton JG, Simpkins JW and Schug KA. Reten-tion behaviour of estrogen metabolites on hydrophilic interactionchromatography stationary phases. Journal of Separation Science2010; 33(6–7): 793–802.


O’Gara JE, Walsh DP, Phoebe CH, Alden BA, Bouvier ESP, Iraneta PC,Capparella M and Walters TH. Embedded-polar-group bondedphases for high performance liquid chromatography. LC-GC NorthAmerica 2001; 19: 632.

Olivares A, Dryahina K, Navaroo JL, Smith D, Pane PS and Flores M.SPME-GC-MS versus selected ion flow tube mass spectrometry(SIFT-MS) analyses for the study of volatile compound generation andoxidation status during dry fermented sausage processing. Journal ofAgricultural and Food Chemistry 2011; 59(5): 1931–1938.

Orešič M. Metabolomics, a novel tool for studies of nutrition, metabolismand lipid dysfunction. Nutrition, Metabolism, and CardiovascularDiseases 2009; 19(11): 816–824.

Orth P and Engelhardt H. Separation of sugars on chemically modifiedsilica gel. Chromatographia 1982; 15(2): 91–96.

Palmer JK. A versatile system for sugar analysis via liquid chromatogra-phy. Analytical Letters 1975; 8(3): 215–224.

Pan ZZ, Gu HW, Talaty N, Chen HW, Sanajah N, Hainline BE, Cooks RGand Raftery D. Principal component analysis of urine metabolitesdetected by NMR and DESI-MS in patients with inborn errors ofmetabolism. Analytical and Bioanalytical Chemistry 2007; 387:539–549.

Pasikanti KK, Ho PC and Chan ECY. Development and validation of a gaschromatography/mass spectrometry metabonomic platform for theglobal profiling of urinary metabolites. Rapid Communications in MassSpectrometry 2008; 22(19): 2984–2992.

Perez-Cornago A, Brennan L, Ibero-Baraibar I, Hermsdorff HHM,O’Gorman A, Zulet MA and Martinez JA. Metabolomics identifieschanges in fatty acid and amino acid profiles in serum of overweightolder adults following a weight loss intervention. Journal of Physiol-ogy and Biochemistry 2014. Available from: http://link.springer.com/article/10.1007/s13105-013-0311-2/fulltext.html (accessed 15 January2014, doi: 10.1007/s13105-013-0311-2).

Plumb RS. UPLC detects more metabolites than HPLC. Trends in AnalyticalChemistry 2004; 23(10–11): 5–10.

Plumb RS, Stumpf CL, Gorenstein MV, Castro-Perez JM, Dear GJ, AnthonyM, Sweatman BC, Connor SC and Haselen JN. Metabonomics: the useof electrospray mass spectrometry coupled to reversed-phase liquidchromatography shows potential for the screening of rat urine indrug development. Rapid Communication in Mass Spectrometry2002; 16(20): 1991–1996.

Powers R. NMR metabolomics and drug discovery.Magnetic Resonance inChemistry (Supplement: NMR-based mixture analysis – metabolomicsand beyond) 2009; 47(S1): S2–S11.

Quiming NS, Denola NL, Soliev AB, Saito Y and Jinno K. Retention behav-iour of ginsenosides on a poly(vinyl alcohol)-bonded stationaryphase in hydrophilic interaction chromatography. Analytical andBioanalytical Chemistry 2007; 389(5): 1477–1488.

Quinones MP and Kaddurah-Daouk R. Metabolomics tools for identifyingbiomarkers for neuropsychiatric diseases. Neurobiology of Disease2009; 35(2): 165–176.

Ramautar R, Mayboroda OA, Somsen GW and de Jong GJ. CE-MS formetabolomics: Developments and applications in the period 2008–2010.Electrophoresis (Special Issue: CE and CEC Reviews) 2011; 32(1): 52–65.

Ratai EM, Pilkenton S, Lentz MR, Greco JB, Fuller RA, Kim JP, He J, Cheng LLand Gonzalez RG. Comparisons of brain metabolites observed byHRMAS 1H NMR of intact tissues and solution 1H NMR of tissue extractsin SIV-injected macaques. NMR in Biomedicine 2005; 18: 242–251.

Reinke SN, Broadhurst DI, Sykes BD, Baker GF, Catz I, Warren KG and PowerC. Metabolomic profiling in multiple sclerosis: insights into biomarkersand pathogenesis. Multiple Sclerosis Journal. Available from: http://msj.sagepub.com/content/early/2014/01/22/1352458513516528.abstract(accessed 3 February 2014; doi: 10.1177/1352458513516528).

Robertson DG, Watkins PB and Reily MD. Metabolomics in Toxicology: Pre-clinical and clinical applications. Toxicological Sciences 2010; 120(1):S146–S170.

Sanchez S, Ziarelli F, Viel S, Delaurent C and Caldarelli S. Improved solid-state NMR quantifications of active principles in pharmaceutical for-mulations. Journal of Pharmaceutical and Biomedical Analysis 2008;47(4–5): 683–687.

Schmitt-Kopplin P and Frommberger M. Capillary electrophoresis – massspectrometry: 15 years of developments and applications. Electropho-resis 2003; 24: 3837–3867 .

Schnackenberg LK and Beger RD. The role of metabolic biomarkers indrug toxicity studies. Toxicology Mechanisms and Methods 2008;18(4): 301–311.


http://msj.sagepub.com/content/early/2014/01/22/1352458513516528.abstract

http://msj.sagepub.com/content/early/2014/01/22/1352458513516528.abstract


Schroder HF. Polar organic pollutants from textile industries in the waste-water treatment process – biochemical and physiochemical elimina-tion and degradation monitoring by LC-MS, FIA-MS and MS-MS.Trends in Analytical Chemistry 1996; 15(8): 349–362.

Sequant. A Practical Guide to HILIC: A Tutorial and Application Book.Sequant AB: Sweden, 2007; 3.

Sequant. ZIC HILIC for HPLC separation of Polar Compounds, 2011. Avail-able from: http://www.sequant.com/default.asp?ml=11528 (accessed20 May 2012).

Shachar-Hill Y. Nuclear magnetic resonance and plant metabolic engi-neering. Metabolic Engineering 2002; 4: 90–97.

Shamsipur M, Shafiee-Dastgerdi L, Talebpour Z and Haghgoo S. 19F NMRas a powerful technique for the assay of anti-psychotic drug haloper-idol in human serum and pharmaceutical formulation. Journal ofPharmaceutical and Biomedical Analysis 2007; 43(3): 1116–1121.

Snyder LR, Kirkland JJ and Dolan JW. Introduction to Modern LiquidChromatography, 3rd edn. Wiley: New Jersey, 2009.

Soga T and Imaizumi M. Capillary electrophoresis method for the analysisof inorganic anions, organic acids, amino acids, nucleotides, carbohy-drates and other anionic compounds. Electrophoresis 2001; 22(16):3418–3425.

Sommer U, Herscovitz H, Welty FK and Costello CE. LC-MS-based methodfor the qualitative and quantitative analysis of complex lipid mix-tures. The Journal of Lipid Research 2006; 47: 804–814.

Soukup J and Jandera P. Hydrosilated silica-based columns: the effects ofmobile phase and temperature on dual hydrophilic-reversed-phaseseparation mechanism of phenolic acids. Journal of ChromatographyA 2012; 1228: 125–134.

Spagou K, Tsoukali H, Raikos N, Gika H, Wilson ID and Theodoridis G.Hydrophilic interaction chromatography coupled to MS formetabonomic/metabolomic studies. Journal of Separation Science2010; 33(6–7): 716–727.

Spraul M, Freund AS, Nast RE, Withers RS, Maas WE and Corcoran O.Advancing NMR sensitivity for LC-NMR-MS using a cryoflow probe:application to the analysis of acetaminophen metabolites in urine.Analytical Chemistry 2003; 75(6): 1536–1541.

Stamler J , Elliott P, Dennis B, Dyer AR, Kesteloot H, Liu K, Ueshima H andZhou BF. Intermap: background, aims, design, methods, and descrip-tive statistics (nondietary). Journal of Human Hypertension 2003; 17:591–608.

Steffen LM, Zheng Y and Steffen BT. Metabolomic biomarkers reflectusual dietary pattern: a review. Current Nutrition Reports 2014. Avail-able from: http://link.springer.com/article/10.1007/s13668-014-0073-7/fulltext.html (accessed 4 February 2014; doi: 10.1007/s13668-014-0073-7).

Storer MK, Hibbard-Melles K, Davis B and Scotter J. Detection of volatilecompounds produced by microbial growth in urine by selected ionflow tube mass spectrometry (SIFT-MS). Journal of MicrobiologicalMethods 2011; 87(1): 111–113.

Suzuki T, Nakagawa Y, Takano I, Yaguchi K and Yasuda K. Environmentalfate of bisphenol A and its biological metabolites in river water andtheir xeno-estrogenic activity. Environmental Science and Technology2004; 38(8): 2389–2396.

Tache F, Nascu-Briciu RD, Sarbu C, Micale F and Medvedovici A.Estimationof the lipophilic character of flavonoids from the retentionbehavriou in reversed phase liquid chromatography on differentstationary phases: a comparative study. Journal of Pharmaceuticaland Biomedical Analysis 2012; 57: 82–93.

Theodoridis G, Gika HG and Wilson ID. LC-MS-based methodology forglobal metabolite profiling in metabonomics/metabolomics. Trendsin Analytical Chemistry 2008; 27(3): 251–260.

Theodoris G, Gika HG and Wilson ID. Mass spectrometry-based holisticanalytical approaches for metabolite profiling in systems biologystudies. Mass Spectrometry Reviews 2011; doi: 10.1002/mas.20306


Theodoridis GA, Gika HG, Want EJ and Wilson ID. Liquid chromatogra-phy–mass spectrometry based global metabolite profiling: a review.Analytica Chimica Acta 2012; 711: 7–16.

Tiziani S, Lopes V and Gunther UL. Early stage diagnosis of oral cancerusing 1H NMR-based metabolomics. Neoplasia 2009; 11(3): 269–276.

Trivedi DK and Iles RK. The application of SIMCA P+ in shotgunmetabolomics analysis of ZIC®HILIC-MS spectra of human urine –experience with the Shimadzu IT-TOF and profiling solutions dataextraction software. Journal of Chromatography and Separation Tech-nology 2012; 145: 3.

Trivedi DK, Jones H, Shah A and Iles RK. Development of zwitterionichydrophilic liquid chromatography (ZIC®HILIC-MS) metabolomicsmethod for shotgun analysis of human urine. Journal of ChromatSeparation Techniques 2012; 3: 144.

Van QN, Chmurny GN and Veenstra TD. The depletion of protein signals inmetabonomics analysis with the WET-CPMG pulse sequence. Biochem-ical and Biophysical Research Communications 2003; 301(4): 952–959.

Villas-Boas SG, Mas S, Akesson M, Smedsgaard J and Nielsen J. Massspectrometry in metabolome analysis. Mass Spectrometry Reviews2005; 24: 613–646.

Vinayavekhin N, Homan EA and Saghatelian A. Exploring disease throughmetabolomics, ACS Chemical Biology 2010; 5(1): 91–103.

Wann MH, Ahuja S and Dong MW. Handbook of Pharmaceutical Analysis,Separation Science Technology. Elsevier: San Diego, CA, 2005; 569–579.

Weckwerth W. Integration of metabolomics and proteomics inmolecular plant physiology – coping with the complexity bydata-dimensionality reduction. Physiologia Plantarum 2008; 132(2):176–189.

Wikoff WR. Metabolomic analysis of the cerebrospinal fluid revealschanges in phospholipase expression in the CNS of SIV-infectedmacaques. Journal of Clinical Investigation 2008; 118: 2661–2669.

Wilm MS and Mann M. Electrospray and Taylor–Cone theory, Dole’sbeam of macromolecules at last? International Journal of Mass Spec-trometry and Ion Processes 1994; 136(2–3): 167–180.

Wilson ID, Plumb R, Granger J, Major H, Williams R and Lenz EA. HPLC-MS- based methods for the study of metabonomics. Journal ofChromatography B 2005; 817(1): 67–76.

Wishart DS. Quantitative metabolomics using NRM. Trends in AnalyticalChemistry 2008; 27: 228–237.

Xiao JF, Zhou B and Ressom HW. Metabolite identification and quantita-tion in LC-MS/MS based metabolomics. Trends in Analytical Chemistry2012; 32: 1–14.

Xu X, Zheng P, Ren G, Liu M, Mu J, Guo J, Cao D, Liu Z, Meng H and Xie P.2,4-Dihydroxypyrimidine is a potential urinary metabolite biomarkerfor diagnosing bipolar disorder. Molecular Systems Biology 2014.Available from: http://pubs.rsc.org/en/content/articlepdf/2014/MB/C3MB70614A (accessed 6 February 2014, doi: 10.1039/c3mb70614a).

Yalçın G and Yüktaş FN. An efficient separation and method develop-ment for the quantifying of two basic impurities of Nicergoline byreversed-phase high performance liquid chromatography using ion-pairing counter ions. Journal of Pharmaceutical and Biomedical Analy-sis 2006; 42(4): 434–440.

Yi-qi Z, Zi-Jian W and Ning J. Formation of multiple trimethylsilyl deriva-tives in the derivatization of 17α-ethinylestradiol with BSTFA orMSTFA followed by gas chromatography–mass spectrometry deter-mination. Journal of Environmental Sciences 2007; 19: 879–884.

Zhang Q, Tang N, Schepmoes AA, Phillips LS, Smith RD and Metz TO. Pro-teomic profiling of non-enzymatically glycated proteins in humanplasma and erythrocyte membranes. Journal of Proteome Research2008a; 7(5): 2025–2032.

Zhang Y, Wang Z, Ma Z and Cheng Y. Characeterization of progesteronederivatives by LC-DAD-ESI/MSn and its application to the identifica-tion of impurities in flurogestone acetate. Chromatographia 2008b;68(11–12): 903–909.


http://pubs.rsc.org/en/content/articlepdf/2014/MB/C3MB70614A

http://pubs.rsc.org/en/content/articlepdf/2014/MB/C3MB70614A

Documents

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