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Journal of Chromatography B, xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Journal of Chromatography B

jou rn al hom ep age: www.elsev ier .com/ locate /chromb

C–MS-based holistic metabolic profiling. Problems, limitations,dvantages, and future perspectives�

elen G. Gikaa,∗, Ian D. Wilsonb, Georgios A. Theodoridisc

Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UKDepartment of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

r t i c l e i n f o

rticle history:eceived 30 November 2013ccepted 25 January 2014vailable online xxx

a b s t r a c t

The present review aims to critically discuss some of the major problems and limitations of LC–MSbased metabolomics as experienced from an analytical chemistry standpoint. Metabolomics offers dis-tinct advantages to a variety of life sciences. Continuous development of the field has been realised dueto intensive efforts from a great many scientists from widely divergent backgrounds and research inter-

eywords:etabolomicsetabonomics

tandardisationuality control

ests as demonstrated by the contents of this special issue. The aim of this commentary is to describecurrent hindrances to field’s progress, (some unique to metabolomics, some common with other omicsfields or with conventional targeted bioanalysis) to propose some potential solutions to overcome theseconstraints and to provide a future perspective for likely developments in the field.

© 2014 Elsevier B.V. All rights reserved.

ass spectrometry

. Introduction

Metabonomics has been defined as the “the quantitative mea-urement of the dynamic multiparametric response of a livingystem to pathophysiological stimuli or genetic modification” [1].etabolomics or metabolic profiling deal with the study of theetabolic content (small molecule complement) of the analysed

ample, cell or organ. These terms are now practically used inter-hangeably. No matter the term used the field has evolved greatlyver the last years [1–3] as seen by a significant increase in sizef the field, the increasing number of active research groups andhe continuing high degree of investment in capital equipment

Please cite this article in press as: H.G. Gika, et al., J. Chromatogr. B (20

nd facilities. As a result the numbers of publications and cita-ions although still considerably smaller than those of the other two

Abbreviations: APCI, atmospheric pressure chemical ionisation; CE, capil-ary electrophoresis; DoE, design of experiment; ESI, electrospray ionisation;T-ICR-MS, Fourier transformation ion cyclotron resonance mass spectrometry;C–MS, gas chromatography–mass spectrometry; HILIC, hydrophilic interac-

ion chromatography; HRMS, high resolution mass spectrometry; LC–MS, liquidhromatography–mass spectrometry; MetID, metabolite identification; PCR, poly-erase chain reaction; QC, quality control; QTOF-MS, quadrupole time of flight mass

pectrometry; SFC, supercritical fluid chromatography.� This paper is part of the special issue “Metabolomics II” by G. Theodoridis and. Tsikas.∗ Corresponding author. Tel.: +30 2310996224.

E-mail address: gkikae@auth.gr (H.G. Gika).

ttp://dx.doi.org/10.1016/j.jchromb.2014.01.054570-0232/© 2014 Elsevier B.V. All rights reserved.

major omics fields (genomics and proteomics) exhibit significantlyfaster growth rates as shown in Fig. 1.

In the early days of holistic metabolic profiling, proton nuclearmagnetic resonance (NMR) spectroscopy was the dominant analyt-ical tool, and enabled major advances in the art of global metaboliteprofiling, but more recently chromatographic separations (mostespecially gas and, in particular, liquid chromatography) coupledto mass spectrometry (MS) (GC–MS and LC–MS), have becomethe major instrumental approaches [4]. Other separation tech-niques such as capillary electrophoresis (CE) and supercritical fluidchromatography (SFC) have also been applied, but to date havenot achieved to become widely adopted [4]. Overall in the broadbioanalytical field of global metabolic profiling, LC–MS currentlyrepresents the major instrumental technology; it also shows strongpenetration in other omics fields such as proteomics where it con-tinues to displace 2D gel electrophoresis. In metabolomics theprominence of LC–MS can be attributed mostly to the followingreasons:

1) The large number of available instruments coupled with a rangeof suitable vendor-specific and open source data processing soft-ware capabilities and a pool of trained operators.

2) The wide metabolite coverage provided by LC–MS, often with

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high sensitivity and specificity, which brings the field closer tothe attainment of true “holistic” metabolic profiling.

3) The versatility of the technology: thus a single LC–MS instru-ment combination can be used for a variety of different

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number of publica�ons per year

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Genomics Proteomics Transcriptomics Metabolomics

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earch terms in abstract, title, keywords: genomics, proteomics, metabonomics oretabolomics, transcriptomics. Search was conducted in the end of August 2013,

nd results for the year 2013 were extrapolated to the full length of the year.

functions (e.g., drug bioanalysis or environmental analysis)and then, with minimal or without modifications, can readilybe switched to endogenous metabolite profiling work. Inessence any contemporary MS instrument can be used formetabolomic/metabonomic profiling (though obviously withinstrument-dependant outcomes).

. Advantages offered by undertaking metabolomicrofiling

The major advantages and potential benefits offered by adopting holistic analytical approach to metabolic profiling include:

) the prospect of identifying novel markers: metabolites wherechanges in concentration/flux in response to the biological chal-lenge were unforeseen and/or unexpected with regard to theirbiochemical function,

) as a result of the above a gain in biochemical insight and newdescriptor(s) of the biochemical phenomena,

) integration of a “systems” approach as metabolite concen-tration information complements genomic/transcriptomic andproteomic data,

) the description of the real-time metabolic status of the stud-ied system. Metabolomics describes the metabolic status ofthe sample at the time of sample collection, providing asummary of the cells/organisms response to (among others)gene/environmental/drug interactions,

) to obtain equivalent metabolome coverage using conventionalclassical methods that cover all major groups of metabolites onehas to employ large number of different methods. Combinationof all results obtained in different instruments and by differentpractitioners would be impractical, compromising prospectivefor comprehensive analysis.

. Analytical technologies: current state of the art

Continuous developments in both LC and MS technology haveed to noteworthy advances in separations (e.g. UHPLC) and iney operating MS characteristics such as ionisation methods (e.g.

Please cite this article in press as: H.G. Gika, et al., J. Chromatogr. B (20

ith exciting recent developments in in situ desorption ionisationnd MS imaging [5,6]), sensitivity, mass accuracy, mass resolution,can speed and data acquisition rates, thus improving applicabilitynd utility for metabolic profiling. What is more, developments in

PRESSr. B xxx (2014) xxx–xxx

software have facilitated more efficient use of the wealth of databeing collected by the newer mass spectrometers. Such softwareallow for the automated and programmed implementation of dif-ferent real-time experiments within full scan analyses such asMS–MS/MSn experiments in data dependent acquisition mode,MS/MS with different collision energies (e.g. such as MSE) and soforth. Further to this, the data collected can be effectively scru-tinised by advanced software utilities (often offered as add-onson the basic operating platform) which can effectively combinedifferent levels of data (e.g. accurate mass with isotope ratiomeasurement from both precursor and product ions) to predictfragmentation patterns and reconstruct molecular formulae toachieve the putative identification of unknowns. In GC–MS thepotential for the generation of spectral libraries from electron ion-isation (EI) data has been exploited and a specific metabolomicsdatabase (Agilent Fiehn mass spectral library) has been developedwhich when used in addition to retention indices and existingspectral libraries that contain chemicals and pharmaceuticals, canprovide effective identification of known-unknown peaks (peakannotation).

4. Problems and limitations

Despite such important achievements the current state of the artof LC–MS-based metabolic profiling still meets hindrances not theleast of which is that no single mode of chromatography is yet capa-ble of comprehensively profiling the whole metabolome in a singleanalytical run (e.g., the limitations of RPLC for highly polar com-pounds requires other chromatographic modes to be used such asHILIC as well). Establishing unbiased analytical methods is not triv-ial because the metabolites (expected to be) present in biologicalsamples:

1) can show high diversity of chemical properties,2) can occur over wide concentration ranges (up to eight orders of

magnitude),3) derive from many different sources as they can be endogenous,

exogenous (xenobiotics such as nutrients or pharmaceuticals)or of symbiotic origin, and

4) as a result of the above an “unbiased” quantitative extraction ofall metabolites from their matrix can be considered unrealistic[4 and references included therein].

Further to these general limitations, issues specific to theapplication of hyphenated techniques include:

5) problems in combining data from different MS analyseswhich hinder correlation of data obtained in different instru-ments/laboratories [7]; as a result

6) large scale studies (of epidemiology level) need to overcomethe difficulty of standardisation of methods [8] and the riskof instrumental drifts such as retention time drifts (Fig. 2B)due to deterioration of the analytical column [9] or MS detec-tion instability due the MS ion source contamination. Analytequantification is a problem compared to conventional targetedmethods.

These issues result in7) the fragmentation of research effort, as it becomes clear that

with the current state of the art, profiling work cannot confi-dently be split between different laboratories and that differentresearchers may spend a lot of time identifying the same com-pound in different samples. A major problem is that resultsobtained in untargeted profiling mode in different laboratories

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or even in the same laboratory but in different instruments(e.g. Orbitrap-MS and QTOF-MS) cannot be directly comparedor easily correlated even when the same separation system isemployed [7].

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Fig. 2. Utilisation of quality control measures is necessary to assess the system’s stability and suitability. (A) PCA scores plot depicting the stabilisation of the system following5 initial QC injections (system conditioning); QC replications form a tight cloud; analysis by LC–ESI-TOF-MS (unpublished data). (B) PCA scores plot showing a significant timetrend in data due to problematic analysis in LC–TOF-MS. In this example, a problem in the LC system occurred in the analysis of samples 30–31 and affected the subsequenta eptablP lyticad this fi

8

nalyses. (C) Plot of variables in time (X axis, RT in s) and m/z (Y axis). In green accCA scores plot depicting the day-to-day variability in a test of five repeats of an anaiffer to a significant extent in PC2. (For interpretation of the references to colour in

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) a major bottleneck is the identification of candidate markers(metabolite identification or MetID) [10]. Unlike for GC–MSspectra where EI give a characteristic and reproducible “fin-gerprint” of the analysed molecule, LC–MS spectra are highly

e variables (in this case a CV filter of 30% is set), in red unacceptable variables. (D)l sample-set. The three first days cluster together, whereas the fourth and fifth daysgure legend, the reader is referred to the web version of the article.)

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variable: adduct and cluster formation is not controllable or pre-dictable. Fragmentation is not reproducible even in instrumentsof the same type. As a result commercial or freely availableLC–MS spectral libraries still represent an aspirational rather

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than a robust solution to the identification of unknowns. Con-sequently the annotation of metabolites or de novo structureelucidation in complex biological matrices typically requires sig-nificant effort that can add up to months or years [10].

) Finally, biochemical pathway analysis and the development ofrelated visualisation tools are still in their infancy.

The lack of standardised protocols from the phase of studyesign, sample collection and handling, up to the phase of chemi-al and statistical analysis remains an issue to be resolved. Protocolarmonisation is needed to facilitate successful large-scale appli-ation of metabolic profiling in life, food/plant or environmentalciences to deliver trustworthy results. However to successfullymbark on epidemiology-scale studies a number of problemshould be resolved: (1) Researchers need to make sure that the ana-ytical system remains stable, and this needs to be expressed in auantitative and reportable manner. (2) If large analytical runs needo be split into batches (a strategy which is in practice unavoidable),atch to batch variation (for an example see Fig. 2D) needs to beinimised and data from these batches needs to be combined [11],

n this aspect implementation of reference or QC samples withinach batch can assist in assessing the quality of the data (Fig. 2And C). (3) Practical interventions such as the clean-up of an ion-ource or the chromatographic system or even the replacement of

chromatographic column need to be studied so that results beforend after this modification can be safely combined.

In targeted analyses these issues are addressed using e.g. inter-al standards. After years of development and debate, clear-cutriteria have been established: FDA guidelines and EC directivesssist the practitioner in the validation of bioanalytical methodolo-ies (e.g. EC Directive 86/469 and 657/2002/EC, periodical issuesf Guidance for Industry Bioanalytical Method Validation from theDA FDA). However similar guidelines do not exist despite efforts

uch as the metabolomics standards initiative [12,13] for holisticnalytical approaches and it is basically unrealistic to correlate,ompare or repeat results reported in the literature. Practically eachesearch group follows their own (if any) route for the validationf methods and results, marker identification and reporting.

. Perspective for the way forward

To solve multi-faceted problems such as those mentionedbove, multidisciplinary and inter-sectoral collaboration is neededhrough the co-operation of analytical chemists, biochemists,nstrument and software developers, informaticians, statisticiansnd life scientists. The wealth of the generated data is the key fea-ure of holistic metabolic profiling, but this same characteristic islso the source for a number of problems. Meticulous experimentalesign and research work are necessary to enable the researcher to

dentify the occurrence of any of the failures mentioned above andvoid the risk of comprehending analytical or chemical bias as anding of biological significance.

In the following paragraphs key issues which in our opinioneed further ellaboration are highligthed and ideas for addressinghem are described:

) Design of experiment (DoE): Although the most recent of themajor omic fields, metabolomics can not be based in the “legacy”of the pioneers of genomic or proteomic research. Metabolomicscan indeed profit from the knowledge and experience obtainedduring the development of the genomic and proteomic fields.

Please cite this article in press as: H.G. Gika, et al., J. Chromatogr. B (20

Perhaps the most important lesson is the high rate of fail-ure of proposed biomarkers [14]. However in their core theseomics disciplines differ for variety of reasons; a major differen-tiating parameter is the nature of the target molecules: genes

PRESSr. B xxx (2014) xxx–xxx

and proteins are practically polymers, generated by repetitionof a small number of units (four bases or 20 amino acids),thus the process of biomarker identification is very different.Another factor is that genomics benefits from the existenceof protocols for signal amplification (based on PCR) whereassimilar amplification is not applicable in metabolics profiling.The fact is that the metabolomics research community needsto address and set-up new rules specific for the design ofappropriate metabolomic experimentation. Designing succesfulexperiments for metabolic profiling should be the job of expertsof the field (i.e. statisticians with experience in metabolomics) inclose collaboration with the analytical scientists. If existing sam-ple sets (not originally collected for metabolomics studies) are tobe analysed, caution is needed to avoid interferences from con-founding factors such as (in the case of samples from humans)lifestyle effects, sub-clinical illness, mistakes in sample collec-tion, enzymatic degradation and so forth.

The selection of controls is another important step that needsto be debated beforehand. Certain logical rules already knownin the field of proteomics should be applied: Controls and testgroup should ideally have the same genetic background, shouldbe matched for age, gender and other factors. If the aim is to pro-file the metabolome of plants, the conditions of growth shouldbe the same; in the case of animal models these should be housedin the same facility. Finally the number of samples to be analysedremains a topic of some debate. Statistical power demands anappropriate number of samples being analysed which if large,may be in contradiction with the real-life practicalities of thestudy, and indeed of the capabilities of the analytical laboratory.

This last problem is common to all omics studies; inevitablya compromise is often reached. On the whole, the time spenton properly designing a metabolomics experiment can neverbe excessive. DoE should aim to cover all possible problems,but also to justify the experiment and prioritise the differentanalytical expeditions available (paragraph 3, Improvement ofmetabolome coverage) essentially in a value to money/timeratio. This can help in the selection of the analytical tools to beused for a specific task.

2) Analytical developments for quality control and validation: Inmetabolic profiling, stability issues may emanate from bothinstrument ends: chromatography (retention time drift, generaldegradation of column performance, etc.) and mass spectrome-try (sensitivity loss due to e.g. contamination in the ion source).In GC–MS the stability of the derivatised samples is limited. Suchissues may result in considerable run-order trends, which maymask biological differentiation and delay biomarker discovery.Efforts are needed to find ways to minimise and avoid suchproblems. Such developments are even more necessary for thesuccessful application of metabolic profiling in large-scale epi-demiological studies where hundreds or thousands of analysesneed to take place. One factor that needs to be considered is thatthese phenomena are also related to the type of specimen ana-lysed. With the current state of the art, some hundreds of urinesample injections can be performed in LC–MS without problems[15]. For blood derived samples (serum/plasma) or tissue thenumber is lower due to the modification of the analytical col-umn from the binding of lipophilic species, indicating the needfor further developments [16]. Similar considerations need to betaken in GC–MS analysis.

Implementation of QC measures (injection of QC (see Fig. 2A)and reference samples) in the analytical sequence can help in therealisation of such endeavours. Explicit criteria for QC accep-

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tance should also be established in data treatment and datamining. It is now well understood that MS-generated data forholistic profiling are very complex and contains a high per-centage of noise. It is well understood that the majority of

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the detected features do not correspond to unique metabolites.Hence application of judicious filters should precede multivari-ate statistical analysis. These filters could enforce cut-off criteriain order to eliminate false signals, noise, ghost peaks or peakswith a systematic trend e.g. like those corresponding to contam-inant built-up on column. Rules such as i.e. filtering with the CV%of peak areas in QC injections [17–19] (Fig. 2C) or the 80% rule[20] effectively reduce the contribution of unstable peaks andeliminate noise from the dataset.

Further to this, differences in the performance of various soft-ware (either commercial or open source algorithms) used fordata extraction should be systematically evaluated. Bespokesoftware, typically developed by instrument vendors as add-on of their MS operating platform offer limited freedom foroptimisation of the data extraction process whilst the user isnot fully aware of the meaning of certain functions or param-eters selected. Alteration of the data treatment method is notreported within the data set or the generated files meant tobe exported as peak tables. Re-analysis of the same sample-setwith different software settings could provide notable changesand this could pass un-noticed. Utilisation of open sourcesoftware allows bigger flexibility but still needs advanced com-puting abilities such as experience in R computing language orMATLAB.

) Improvement of metabolome coverage: After immense researchfrom various groups, several methods and protocols have beenestablished for metabolic profiling using NMR spectroscopy,GC–MS and LC–MS analysis. However these methods do notcover the whole of metabolic space. NMR spectroscopic anal-ysis is limited by sensitivity and can typically detect onlythe most abundant metabolites contained in the analysedsample. GC–MS is limited by the volatility and size of theanalysed molecules. LC–MS is mostly realised in reversed-phase (RP) [4] or hydrophilic interaction chromatography(HILIC) modes [21,22] (whilst detectability and sensitivity arestructure-dependent). Although these LC modes are largelycomplementary, one to the other, there are still numerousimportant metabolites or even metabolic groups which are notpractically analysed by either of them. RPLC on C18 media willcover metabolites of medium to low polarity, but will not effec-tively separate very polar, ionic, and very apolar molecules.Although C18 phases can find use in the analysis of lipids, utilisa-tion of a C30 column may be beneficial for the analysis of certainlipid species e.g. carotenoids and steroids [23]. HILIC can ana-lyse some very polar molecules, but can hardly accommodate allionic species in one injection. It may be necessary to develop twomethods for polar metabolites: one for acidic and one for basicmolecules. In any case the analysis of phosphorylated metabo-lites is very difficult, thus necessitating another analysis modeand ion pair liquid chromatography has been proposed for thistask by several researchers [24–26]. It is thus becoming clear thatcurrently an array of techniques needs to be used in the questfor high metabolome coverage. In such a case a fair number ofmetabolites will appear in more than one mode although mostlikely with different analytical sensitivity. This strategy althoughbeing more comprehensive in terms of metabolome coverageresults in another major issue in current metabolomics which is

) the slow progress of the fusion of data collected on different ana-lytical platforms. For example it is extremely difficult or evenunrealistic to try to combine data obtained with different exper-imental configurations (e.g. HILIC–MS and RPLC–MS; APCI-MSand ESI-MS). The development of advanced data mining tools is

Please cite this article in press as: H.G. Gika, et al., J. Chromatogr. B (20

necessary to embark on such endeavours; however it is advisedthat such efforts start off with high quality data from coherentsample sets and well-planned experiments.

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At the same time an important development would be theestablishment of readily available tools that would bind togetherdata from different analytical batches or data obtained in dif-ferent time periods (i.e. several weeks apart). Inter-laboratorystudies, using initially the same and at a later stage differenttypes of instrumentation/software, should be organised in orderto pave the way for such developments which could aim in over-coming research effort fragmentation.

5) Metabolite identification (MetID) remains a major bottleneckin contemporary LC–MS metabolomics. The combination ofhigh resolution MS (HRMS), MS/MS analyses, and advancedinformatics tools are deemed as the most promising tools forMetID. For metabolite annotation (that is the assignment of adetected peak to a known metabolite) candidate lists are gen-erated based on accurate mass detected. However, the processfrom this point is not straightforward as the number of puta-tive identities can be very large and diverse (i.e. 10 or morecandidate molecules per feature) even when using sub 2 ppmmass accuracy. Incorporation of independent (orthogonal) data,such as retention time data, can promote metabolite identifi-cation. In the current practice of metabolic profiling by LC–MSresearchers may rely almost solely on MS data in their efforts toidentify unknown metabolites and this can be regarded inad-equate as the chromatographic properties (such as retentiondata) do not contribute significantly (if they contribute at all)to the MetID process. As retention data can act as a surrogatefor e.g., Log P the lists of putative identities can be reducedby ruling-out candidates by suggesting that a structure(s) isunlikely based on its chromatographic properties. Our grouphas applied retention data prediction algorithms developed inboth RPLC and HILIC profiling of polar metabolites [27,28]. Inthe same line other researchers have exploited retention “pro-jection” as a supplementary means for compound identification[29]. Quadrupole MS was able to identify more compounds thanan FT-ICR-MS when the quadrupole spectra were assisted withretention information. A recent study analysed 120 authen-tic standard metabolites to develop a quantitative structureretention relationship model, incorporating six physicochemi-cal variables in a multiple-linear regression, and exhibited goodpredictive ability for retention times of a range of metabo-lites in HILIC mode [30]. In the end however, identificationis only proven when backed up by demonstration of identi-cal behaviour (chromatographic and spectrometric) with anauthentic standard. Once this is done, those potential biomark-ers have to be validated both by repeating a study and samplesshould be reanalysed using compound-specific validatedmethods.

6) Finally data visualisation remains a considerable hurdle. Evenin the case where metabolites are identified, comprehension ofsuch data it is not trivial within complex metabolic pathwaysand fluxes. A recent trend is seen towards the developmentof biochemical pathway analysis tools to effectively search inthe generated multidimensional data [31], exploiting existingknowledge (e.g. public databases such as KEGG, HMDB [32], lipidmaps) to build associations between the collected signals. Suchinitiatives include Biocyc, Metabolights [33], Reactome, MGIGenome, and MassTrix [31]. Incorporation of data from knownbiochemical pathways may assist in MetID, identifying signals(peaks) that represent missing metabolites in half-covered path-ways or known-unknowns (i.e. peaks that are frequently foundbut are not identified or their MetID is not confirmed). Efficientpathway visualisation can be seen as a drive from signals to

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molecules; from data to information. This in the end may rep-resent a step forward, closer to the perspective of translationalmedicine.

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. Conclusions

Overall the field of metabolomics has evolved greatly duringhe last decade. The number of active researchers has rapidlyncreased. Large scale initiatives promoted the topic, increasedwareness and provided proof of concept; multi-million Eurosrojects such as the Human Metabolome Database [32], Huser-et [16], COMET [34], the Netherlands Metabolomics Center

http://www.metabolomicscentre.nl/), the UK National Phenomeenter [35] and other projects have put intensive efforts ineveloping new methods and promoting biomarker discovery inertain application fields. Research needs now to address moreorizontal aspects to achieve harmonisation of practices andethods.

cknowledgment

Authors Gika and Theodoridis acknowledge co-financing by theuropean Union (European Social Fund- ESF) and Greek nationalunds through the Operational Program “Education and Lifelongearning” of the National Strategic Reference Framework (NSRF)esearch Funding Program: Thales II.

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