metabolomics as a complementary tool in cell culture

Biotechnol. Appl. Biochem. (2007) 47, 71–84 (Printed in Great Britain) doi:10.1042/BA20060221 71

REVIEWMetabolomics as a complementary tool in cell culture

Soo Hean Gary Khoo* and Mohamed Al-Rubeai†1

*Department of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K., and †School ofChemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, Republic of Ireland

Metabolomics, the ‘global’ study of metabolite changesin a biological system, has drawn a significant amountof interest over the last few years. It can be said tobe an amalgam of traditional areas such as metaboliteanalysis, bioanalytical development and chemometrics.Thus, piecing these areas together into the cohesive sci-ence of metabolome analysis has proved to be difficult.Most work to date has been focused on plant, micro-bial, as well as tissue and biofluid samples. However,the diverse potential of metabolomics in many fields,including cell engineering, has made it a universal toolfor industrial, medical and research purposes. It is alsoa vital component of a ‘systems biology’ approach, as itis believed to be a good reflection of the phenotype ofany cell or tissue. At the heart of metabolomics’ growthis the issue of method development, including samplepreparation, instrument analysis, data processing andbioinformatics. Here, we look at the cell-culture appli-cations of metabolomics and the issues that cantransform metabolomics into a mature ‘omics’science.

Introduction

With the systematic genomic sequencing of various org-anisms, an unprecedented amount of information hasnow been revealed. Deciphering such a blueprint viathe understanding of functions and interactions within acomplex biological system has been the focus of the post-genomic era. That has fuelled the growth of other ‘omic’sciences, such as transciptomics and proteomics, which,together with the rapid development of bioinformatics andstatistical tools, can now be used in many research and medi-cal applications. One of those relatively new ‘omic’ sciencesis the field of metabolomics. The metabolome was firstdescribed by Oliver et al. [1] as being the set of allthe low-molecular-mass compounds synthesized by anorganism. Metabolomics is therefore the analysis of smallmolecules that constitute metabolism, and it offers theclosest direct measurement of a cell’s physiological activity

[2]. Hence it follows on that metabolome analysis canbe considered as “the measurement of the change in therelative concentrations of metabolites as the result ofthe deletion or overexpression of a gene . . . [and thus]should allow the target of a novel gene product to belocated on the metabolic map”. Another definition of themetabolome states that it consists of “only of those nativesmall molecules (definable non-polymeric compounds) thatare participants in general metabolic reactions and that arerequired for the maintenance, growth and normal functionof a cell” [3]. This would exclude peptides, and even manylarger lipids, as metabolites. Realistically, metabolites canbe considered a class of naturally occurring compounds,diverse in their chemical structure, that are less than 1 kDain molecular mass. These compounds function as carriers,substrates or products in biochemical pathways. Despitethese attempts to define the metabolome, there remainssome vagueness which will slowly be resolved as the fielddevelops. Some view metabolomics as the vital piece of the‘Rossetta stone’ [4] needed for deciphering the puzzle ofcomplex systems as seen in Figure 1. The field of meta-bolomics could be said to fuse metabolite analysis,bioanalytical science and chemometrics. In its present state,the ‘global’ analysis of all metabolites seems to be a longway off and thus, in the pure sense, metabolomics now onlyconsists of fragments of biochemical and metabolite analysis.Despite the ambiguity and lack of comprehensiveness, thefield of metabolomics grows towards the critical objectiveof extracting useful knowledge from metabolite pools.Therefore, for the purpose of its utility, metabolomics is

Key words: cell culture, metabolism, metabolite analysis, metabolomics,systems biology.

Abbreviations used: CE, capillary electrophoresis; ESI, electrospray ionization;EST, expressed sequence tag; FT-ICR, Fourier-transform ion cyclotronresonance; FTIR, Fourier-transform infrared; HCA, hierarchical clusteranalysis; ICA, independent component analysis; LC, liquid chromatography;MST, mass-spectral tag; NIR, near infrared; NLM, non-linear mapping; PAD,photodiode array detection; PCA, principal component analysis; PLS-DA,partial least squares-discriminant analysis; SBML, systems biology markuplanguage; SOM, self-organizing map; TOF, time-of-flight; UPLC,ultra-performance liquid chromatography.

1 To whom correspondence should be addressed ([email protected]).

C© 2007 Portland Press Ltd

72 S. H. G. Khoo and M. Al-Rubeai

Figure 1 Overview of the interactions between different ‘omic’s within a cell

best described as an area of science, rather than an analyticalapproach, that characterizes a metabolic phenotype undera specific set of conditions which links these phenotypes totheir correspondent genotypes [5].

A number of other diverse applications of metabolomicanalysis exist. These include the commercial applications inagriculture, industrial biotechnology and xenobiochemistry[6], medical applications like biomarker discovery andnutritional health, as well as environmental applications,such as environmental toxicology, developmental growth oforganisms and pathogen–host interactions. The applicationof metabolomics in the area of mammalian cell culture isrelatively undeveloped and thus the aim of the presentreview is to provide an insight into the issues pertainingto metabolome analysis as well as to explore its possibleapplications in cell culture.

Understanding the complexity ofmetabolome analysis

Metabolomics requires the unbiased identification andquantification of all of the metabolites present in a specificbiological sample (from an organism or in vitro) [7]. Such arequirement is difficult to meet with the present analyticaltechnologies. In addition, the exact number of metabolites

in a system is not known. It is believed that the numberof metabolites for a particular cell type should be lowerthan the number of genes and proteins in a cell [8], whichwould give a number less than 10000 [9]. However, ifan analysis platform is to be used universally for variousorganisms, this number increases very significantly. It isspeculated that there are an estimated 200000 differentmetabolites in the Plant Kingdom [10], with the numbersin the mammalian systems being lower. To put this intoperspective, present microarray technologies for transcriptshave an upper limit of about 15000–20000 ESTs (expressedsequence tags) per array, whereas 2-DE [two-dimensional(polyacrylamide) electrophoresis] can readily differentiate afew thousand proteins, with 10000 proteins as an upper limit[11]. Present metabolomic analyses can resolve anythingfrom a low of 70 metabolites [12] to over 4000 metabolites[13]. In addition, their diverse chemical properties makecomplete metabolite analysis difficult. Genes are composedof a linear four-letter code, whereas proteins have a 20-lettercode of primary amino acids as a foundation. Metabolitesdo not have any fixed codes, and thus a universal method ofcharacterization is difficult. Present methods use the specificchemical properties of these entities to separate, identifyand decipher their structures. Combinatorial approachesallow for a greater coverage. It is therefore important todefine criteria for such analyses. Hence an ideal metabolomicanalysis should provide the following:


Metabolomics and cell culture 73

� give an instantaneous snapshot of all metabolites in anygiven system

� use analytical methods that have high recovery, experi-mental robustness, reproducibility, high resolving powerand high sensitivity [14] whilst being able to be applieduniversally

� provide the unambiguous quantification and identificationof metabolites

� allow distinguishing factors to be highlighted whileeasily being incorporated into biochemical networkmodels

To overcome present analytical failings, the metabolo-mic community tends to confine their use of specific analyt-ical approaches (categories) to help answer specific types ofquestions. These categories include metabolite or metabolicprofiling, metabolic fingerprinting and metabonomics. Meta-bolite fingerprinting aims to look at the evidence of majormetabolic effects as a result of perturbations. Rapid classi-fication of samples according to their origin or their bio-logical relevance allows the maintenance of high-throughputanalysis. In such cases it might not be necessary to determinethe levels of all metabolites individually, as patterns of spectramay be sufficient for classification.

On the other hand, metabolic profiling is used to elucid-ate the function of a whole pathway or intersecting pathwaysand does not require the characterization of the entiremetabolome. Thus such analysis focuses on a chosen classof compounds (such as amino acids or carbohydrates).A similar definition to metabolite profiling is termed‘metabonomics’. The term ‘metabonomics’ was first coinedby Nicholson and his colleagues [15] in 1996 to describe thestudies of metabolite profiles in biofluids, such as plasma orurine, from whole organisms. As further elaborated, meta-bonomics is “the quantitative measurement of the dynamicmutliparametric metabolic responses of living systems topathophysiological stimuli or genetic modification” and thekey feature in this type of analysis is pattern recognition[16]. The metabolon thus refers to co-ordinated channellingof substrates through tightly connected enzyme complexes[17]. As the field of metabolomics broadens, the lines thatseparate these classes become blurred, owing to the needfor greater comprehensiveness in data extraction.

Methodology

Sample preparation and extractionThe first step to ensuring the simultaneous detection of alarge number of metabolites is to have adequate methodsfor sample preparation and extraction. Erroneous sample

preparation can lead to misleading or inaccurate data, evenwith the most sensitive instruments. Sampling techniques,extraction, storage and pre-analysis preparation are justsome of the necessary steps taken before instrumentanalysis. As metabolic processes may be rapid, varying frommilliseconds to minutes [18,19], the first necessary stepis to rapidly stop any inherent enzymatic activity or anychanges in the metabolite levels. This is sometimes termed‘quenching’. In addition, sampling methods should not bebiased towards any group of molecules, but this challenge ispresently unresolved. The time and method of sampling areimportant issues to be considered to ensure reproducibilityin the analytical sample, especially since a large number ofbiological replicates is commonly used.

Traditional quenching methods include freeze-clamping(with lower-temperature receptacles), immediate freezingin liquid nitrogen or by acidic (e.g. perchloric acid or nitricacid) treatments [20]. Freezing in liquid nitrogen is generallyconsidered to be the easiest way of stopping enzyme activityprovided that cells or tissues are not allowed to partiallythaw before extracting metabolites. In order to prevent thisfrom happening, enzyme activity is inhibited by freeze-dryingor by immediate addition of organic solvents while applyingheat. Freeze-clamping is a faster process of freezing cells thatavoids the potential artefacts caused by wound response[10]. Acidic treatments can severely decrease the numberof metabolites detected as a result of degradation due tothe low pH. Acidic treatments also pose severe problemsfor many analytical methods that follow, so the acids have tobe removed. Cold organic solvents may be directly addedto tissue samples and kept below temperatures of −20 ◦Cduring the entire sample preparation.

Cells are subsequently disrupted, releasing the meta-bolites. Frozen samples may be ground down by sonication,homogenization by mechanical means (for example mortarsor ball mills) in pre-chilled holders [21] or directly inan extraction solvent [22]. Most frequently, polar organicsolvents such as alcohols are added directly to frozensamples to extract polar compounds, whereas non-polarsolvents such as chloroform or dichloromethane allow theextraction of lipids and other hydrophobic compounds.Sometimes, adding a mixture of polar and non-polar solventsallows for extraction of both classes of metabolites. Hotalcoholic extractions are also performed routinely. A pro-cedure for the extraction and separation of metabolites,proteins and mRNA from a single sample has also beenreported [23]. The mixture of cell debris, protein and thedesired metabolites need to be separated. This can be doneby centrifugation or filtration.

Dynamic sampling methods allow for the determinationof kinetic rates of substrate change. In a Bioreactor equippedwith specialized equipment, this can be done by sprayingsamples rapidly into a moving belt of sample tubes containing



Table 1 Summary of analytical platforms and their general operational ranges

Analytical platform Analysis time/throughput Metabolites measured Sensitivity/limits of detection Comments

NMR Up to 20 min per sample (at Typically 20–50 metabolites Micromolar concentrations; Non-invasive methodleast 500 µl); autosampler identified sensitivity depends on with a proven track-available with spectrometers peak obsevation in spectra record in the medical field;

provides structuralinformation

TOF/FT-ICR MS A 1 min analysis time; thousands About 2000–10000 mass About 5 p.p.m. for standard mass Can be used withof samples a day; sample volume ions measured spectrometers (<1 p.p.m. for different ionization<1 µl, typically in the nanolitre FT-ICR); limits of detection modes; ion suppressionrange; array formats available depend on ionization of molecule an important factor

GC-MS Ranges from 10 min (run About 200–1000 Picomolar concentrations; Cheap to run andtime) to 1 h (including metabolites sensitivity dependent on MS commonly used due toderivatization time); about long history of usage;1000 samples/month; 1– not all samples can10 µl sample volatilized; be analysed by GC-MSautosampler typically available

LC-MS Ranges from 10 min to 2 h; Up to 3000 putative Femtomolar concentrations; Suffers from substantial matrixinjection range can be from metabolites measured. sensitivity dependent on MS effects as well as ion0.2 µl (capillary columns) to suppression50 µl (HPLC columns);autosampler typically available

quenching liquid [24]. This allows for the automated collec-tions of a large number of samples (up to 4.5 samples/s),which is ideal in cell culture or bioreactor situations. Manyof the traditional methods mentioned above are slow andlaborious, sometimes leading to the degradation of metabol-ites. Hence integrated procedures that allow for sampling,quenching and extraction to be simplified into a singlestep have been devised [25]. Samples can be taken rapidly(less than 1 s) into a sample tube which quenches as wellas disrupts the cells. This is ideal for use with bioreactorsand allows for sampling to be completed in seconds. Intra-cellular metabolite concentrations are subsequently determ-ined by subtracting the metabolite content of the cell-freeextracellular medium from the resultant mixture.

Analytical instrument platformsOnce metabolites have been extracted, analytical analysistakes the form of classical separation and identification.For this purpose, there exist a whole variety of establishedinstruments, each with their own pros and cons. To under-stand the reasons for the use of various instrumentplatforms, one must understand the desired characteristicsfor metabolite analysis. First, the instruments need to haveexcellent sensitivity that is, to be able to analyse multiplemetabolite classes without loss of resolution. Peaks shouldtherefore not represent the merger of several components.This also means that instruments should be able to handlea large range of concentrations, ranging from picomolarto millimolar concentrations. It should also allow easy

identification and quantification of metabolites and allow forthe comparison of relative changes in metabolite abundancesin comparative experiments. Lastly, it should have a rathershort analysis time to increase the number of samplesanalysed. High throughput can be understood in two ways:rapid analysis (short analysis time per sample) and/or havinga wide coverage of components. One way of increasingresolution is by reducing the number of metabolites that aresimultaneously analysed by the instrument, thus allowingfor the reduction of time for the separation process.Fractionation can thus be applied to solve this issue [10]. Todetermine unknown structures of metabolites, informationfrom the intact molecule needs to be obtained, such asfunctional groups, size (mass) and elemental composition.Therefore, hyphenated instruments have been shown to beextremely useful as they allow for an extra dimension ofanalysis. Dimensionally they do two things: they increasethe separation of compounds by different characteristics(such as retention time or different physical propertiessuch as mass) and provide additional structural informationfor identification. It is also possible to combine data fromseparate instruments to form a comprehensive study ofmetabolism [26–28]. Table 1 gives a summary of the differentinstruments used.

NMR spectroscopyNMR is a non-invasive, highly discriminatory, high-throughput method that can analyse rather crude samples.A single metabolite typically gives several signals in the



Figure 2 Overlaid spectra of replicate extractions of metabolites extractedfrom mouse myeloma NS0 cells

The horizontal axis represents p.p.m., while the vertical axis represent theintensity in arbitrary units.

NMR spectra, causing a problem in the resolution ofindividual metabolites. This is disadvantageous, as it makesidentification of metabolites difficult in convoluted complexspectra. Typical NMR analysis normally allows for the identi-fication of 20–50 metabolites. Acquisition time for NMR canbe between 10 and 15 min per sample and is favourable withtranscriptomic and proteomic approaches. However, NMRfalls short of resolution and sensitivity compared with MSmethods. The sensitivity depends on the natural abundanceof the atoms studied (1H, 31P, 13C etc.) or the artificialintroduction of the isotopes into the sample. Significantamounts of culture (at least 3 million cells) are requiredfor metabolite extraction. By increasing the strength ofmagnetic fields, increased specificity with greater resolutionand separation of signature chemical shifts is possible. Inaddition, longer analysis times or the use of cryogenic probescan help [29]. Another disadvantage of NMR-based analysisis the fact that the sample tubes are generally large in volume(at least 500 µl), and hence the sample volumes required arecomparatively large (less than 10 µl for MS applications).Details of NMR metabolomic techniques have also beenreviewed [30,31].

By far the most popular means of metabolomic mea-surement is one-dimensional proton NMR (see Figure 2).The resulting spectra are generally complex, with over-lapping peaks, and thus require significant data processing.However, to remove ambiguity to the assignment of meta-bolites, an additional dimension may be added. Theseinclude the J-resolved, homonuclear-shift COSY (correlatedspectroscopy), TOCSY (total correlation spectroscopy) andNOESY (nuclear overhauser effect spectroscopy) [32].NMR has also been shown to have reasonably good

inter-laboratory reproducibility, as seen in the COMET(Consortium for Metabonomic Toxicology) project [33]. Ina ground-breaking use of NMR, metabolite concentrationsin a yeast mutant were monitored and correlated with thefunction of genes. This was termed FANCY (FunctionalANalysis by Co-responses in Yeast) [34]. Peak identificationsoftware is also commercially available from Chenomx(Edmonton, AB, Canada).

MSMS instruments are by far the most widely used in the fieldof metabolomics (including hyphenated technologies). DIMS(direct injection MS) is the direct injection of samples intolow-resolution ESI (electrospray ionization) MS instruments,resulting in a quick (less than 1 min/sample) and useful wayof getting high throughput (more than 100 samples perday) with sufficient information. By controlling the factorssuch as ion fragmentation and sample matrix, Dunn et al.[13] were able to analyse up to 250 plant samples/dayusing an ESI-TOF (time-of-flight)-MS. High-throughput MScan also be achieved by adapting a hybrid FT-ICR (Fourier-transform ion cyclotron resonance) MS with a Nanomatechip-based nanoelectrospray assembly (Advion BioSciences,Hethersett, Norwich, U.K.). Typical injection volumes arein the nanolitre range, with a separation of between 3000and 10000 molecular species without chromatographicseparation [9]. Ion suppression is, however, a major problemin MS. Recent technological advancements have made TOF-MS acquisition times quicker and mass determination veryaccurate. TOF instruments can provide mass resolutionsof greater than 4000 peaks at mass 200, which allows theresolution and the detection of metabolites of the samenominal mass but at different monoisotopic masses [8].MALDI (matrix-assisted laser-desorption–ionization) MSmethods are advantageous, as it is a TOF instrumentthat gives direct mass-to-charge ratios, but has substantialinterference from the matrix used. However, to circumventthe problem, a matrix-free system was developed called theDIOS (‘desorption ionization on silicon’) method [35],which, when coupled with a TOF instrument, becomesa powerful tool for metabolite quantification [36,37].Unfortunately the major problem with MS methods is thatthey cannot differentiate chemical isomers with identicalmass-to-charge ratios, such as those of common hexoses.Furthermore, owing to the disruption of chemical bondsduring ionization, structural identification from intact massesof the molecule is lost.

The mass accuracy of these instruments is typicallyonly 5 p.p.m., and overlapping peaks could result in massdifferences lower than that of the threshold. FT-ICR MS canovercome this problem, as it has lower limit of detection(<1 p.p.m.). The disadvantages of FT-ICR are that it has



smaller dynamic ranges and still fails when structural isomershaving the same monoisotopic mass are employed. This newinstrument will become increasingly popular in metabolomicanalyses, and, when coupled with software that can exploitthe information in isotope patterns, it can produce theempirical formula for metabolites directly [38].

Chromatographic and other column separationsThe most common forms of chromatography are GC andLC (liquid chromatography). GC runs are relatively long,at about 60 min or more [39]; however, deconvolutionsoftware allows for the decrease in run times [40]. InLC there is a shift from standard HPLC to UPLC (ultra-performance liquid chromatography), which can significantlyincrease resolution sensitivity and peak capacity [41] due tothe reduced particle size, while decreasing sample volumes(2 µl compared with 20–50 µl in HPLC) and mobile phases(around 500 µl/min compared with 1 ml/min) [42]. UPLCsystems operate at high operating pressures and usesub-2-µm porous packing. The move towards smaller-borecapillary size columns is also advantageous, as complexsamples require high sensitivities. Unlike pressured systemssuch as LC, CE (capillary electrophoresis) makes use of anelectric field to move molecules towards the detector, muchlike gel electrophoresis. CE coupled with UV or LIF (laser-induced fluorescence) detectors are highly sensitive, but lackselectivity [43]. With different detection modes available, itis possible to add two detectors to a single chromatographystep, as in the example of LC instruments (LC-UV-PAD, where PAD is an electrochemical method termedphotodiode array detection) [44]. However, LC-UV-PADmethods require compounds to contain chromophores.Electrochemical detectors together with LC instrumentsare used commonly as an alternative detection step.There are several modes of electrochemical detection, suchas amperometric and coulometric [45].

Other vibrational spectroscopies: FTIR(Fourier-transform infrared), NIR (near-infrared)and RamanVibrational spectroscopies are relatively insensitive, but FTIRallows for high-throughput screening of biological samples inan unbiased fashion. Samples require little or no preparation,with as little as 0.5–20 µl of sample required [8]. However,IR has some drawbacks. Similar to NMR, water signalspose a problem and must be subtracted electronically orattenuated total reflectance may be used. Compared withthe other methods it is one of the least sensitive, butits unbiasness to compounds and ability to analyse largenumbers of samples in a day (1000 spectra or more) makesit a plausible method for screening purposes. FTIR has beenused in the preliminary analysis of the yeast metabolome

Figure 3 First-derivative spectra of NIR spectra obtained fromsupernatants of yeast fermentation

Illustration provided courtesy of Dr G. McLeod, Department of ChemicalEngineering, University of Birmingham, Birmingham, U.K.

[1] (Figure 3 gives an example of a NIR spectra from yeastfermentations) as well as providing a non-invasive methodwith which to study the overproduction of metabolitesfrom Escherichia coli and Staphylococcus aureus in vivo [46].Metabolite fingerprinting techniques for the diagnosis ofdisease by the analysis of tissues and biofluids using FTIRare also possible [47–49].

Hyphenated instrumentsLC-MS and GC-MS methods are the most common hyphen-ated technologies. Furthermore, GC-MS is considered the‘gold standard’ in metabolite detection and quantification[17]. They offer good sensitivity (limits of detection beingpicomolar or nanomolar) and selectivity, but have relativelylonger analysis times, owing to the GC separation times [43].LC-MS methods typically have a somewhat lower chroma-tographic resolution than GC-MS methods, but, as GC-MSmethods require samples to be rendered volatile, LC-MS methods can analyse higher mass ranges and compoundsnot easily rendered volatile. GC-MS is a relatively low-costmethod that provides high separation efficiencies (about 300metabolites/run] [50] that can resolve complex biologicalmixtures. To search for physical properties unique toeach metabolite while distinguishing it from neighbouringpeaks, a deconvoluting software is often used on the massspectra [40,51] (AMDIS: http://chemdata.nist.gov/mass-spc/amdis). Potentially, the number of metabolites can increaseto 1000 [52,53]. MSFACTS [54] is another program that canelucidate a list of metabolites from a database such as KEGG(Kyoto Encyclopedia of Genes and Genomes) based on theMS profiles and specific GC retention times. Recently, a



database for GC-MS data on metabolites was proposed [55].This database would make use of MSRI (mass spectral andretention time index), which would also contain MST (mass-spectral tag) profiles from various sources. A detailed reviewof GC-MS-based metabolite-profiling technologies has beengiven by Kopka [56]. LC-MS make use of IR-UV detectorsthat can detect double-bonded or aromatic substances,while different separation modes can be chosen. Liquid-phase methods suffer from significant matrix effects, a majorone being the presence of non-volatile compounds, whichmay reduce the evaporation of volatile ions during the elec-trospray process [57]. This effect is termed ‘ion suppression’and can only be circumvented by reducing the size of liquiddroplets [58]. Typical run times for LC-MS methods rangefrom short runs of 10 min [41] to 2 h [59]. CE-MS is anothermethod that can be used to separate a variety of cationic andanionic compounds, nucleotides and coenzyme metaboliteswhile identifying and quantifying them by MS [60].

Since LC-MS and NMR analyses are complementary[41], on-line LC-NMR and LC-NMR-MS approaches havebeen developed [61,62]. LC-NMR techniques were firstdeveloped together with other online separation techniques[63,64], as they were able to overcome the shortcomings ofNMR spectroscopy. 13C isotopes have been used togetherwith LC-ESI-MS/MS instruments to follow the metabolism ofyeast cells, thereby eliminating the drawbacks such as non-linear responses or matrix effects. This isotopic experimentwas termed MIRACLE (mass isotopomer ratio analysis ofU-13C-labelled extracts) [65]. In addition, the use of multi-column separations seems logical, as this adds an additionallevel of separation in an automated-online fashion [41].GC × GC-TOF-MS is another innovative multidimensionalsystem that has been developed [66].

Data processing and databases

Making sense of the data collected on multiple metabolites inthe spectra is just as important as the data collection. Therecan be several objectives. First, one can aim to identify asmany metabolites as possible to obtain a quantitative orqualitative measure of changes in the metabolic networkof the cell. These metabolites are assigned to variousfunctions in the metabolism of the cells and used toreconstruct the metabolic networks according to the designof the comparative experiment. Secondly, one can focus onidentifying changes in the metabolic pathways by lookingfor emerging/disappearing metabolites as well as statisticallysignificant metabolite changes without identifying other‘non-discriminating’ metabolites. The identification of thesemetabolites can be associated with certain physiologicalcharacteristics such as high production in cell lines, cellulardifferentiation or robust growth in cells. In addition, once

Table 2 Common methods for data preprocessing, reduction andprocessing

Data preprocessingNormalization of data–data transformsNormalization of data using internal standard(s)Baseline correction, peak shifting and noise removalMissing value correctionDeconvolution of peaks

Data reductionLimiting data analysis to specified representative region of dataExcluding variables or samples that do not have

consistent replicates or lie outside the analytical limitsExclude sample outliers

Data processing methodsUnivariate and multivariate statistics

Coefficient of variationANOVA or MANOVACorrelation or regression

Unsupervised methodsPCA, ICA and subtypesClustering, HCA, k-meansSOMs

Supervised methodsFisher discriminant analysisPartial least squaresNeural networks (artificial and polynomial)Genetic programming and algorithms

a specific profile has been identified, screening or rapididentification can be carried out by pattern recognition.One application of the latter could be in cell line screeningwhere clones with a known ‘metabolic pattern’ for higherproductivities can be screened. Data processing is thereforean important process for making sense of the ‘soup’ [67],and the steps taken depend on what ones objectives are.Goodacre et al. [68] differentiates the interpretation ofdata into hypothetico-deductive-driven or inductive-drivenapproaches. Most work to date has largely taken thehypothetico-deductive approach, as very little is knownabout the metabolome within a cell. While it is agreed thatstatistical analysis must be performed to ensure analyticalrigour [43], how this should done remains a matter ofdebate, since different methods could give varying results.Certainly, for useful knowledge to be derived, chemometric,comparative and visualization tools will help. Table 2 showssome of the common methods used for preprocessing,reduction and processing of data and demonstrate thatpattern recognition and multivariate discrimination analysisare important steps. As with all ‘omic’ sciences, largedatasets will be required to be stored, retrieved and analysedin an open consistent format. In addition, with the use ofmultiple instruments, data standardization is necessary forintegrated data analysis. Likewise, with transcriptomic data,universal data standardization is pertinent if data sharing anddatabases are to be established [69]. The Standard Metabolic



Reporting Structures working group has outlined a format,which includes the experimental and data processing stages,as to how metabolic data should be reported.

To start, the data have to be converted into a formatthat can be processed by statistical packages. For NMRdata, this involves separating spectra or plots into discreteintensity sections. Some of the methods commonly usedinclude ‘binning’ and area calculation. This can now betransformed into a matrix for processing. MS data can alsobe converted into similar matrices [70]. Complete deter-mination of the metabolite concentrations can be done atthis stage by cross-referencing with known standards (peakpositions or MSTs). However, this is not always accomp-lished, mostly because of the lack of a comprehensive data-base of metabolite concentrations run under the similarmatrix conditions. Instead, it is often the aim to study thesignificant changes in the metabolism of the cell and toidentify these specific features. The data matrix is thereforeused in combination with machine-learning methods toachieve this. Pattern recognition and multivariate analysisare not distinct, as there is a great deal of similaritiesbetween the two. Both aim to reduce the dimensionalityof the measurement vector. Pattern recognition methods(inductive machine learning) include non-supervisedmethods like NLM (non-linear mapping), HCA (hierarchicalcluster analysis), PCA (principal component analysis) [16],k-means clustering and SOMs (self-organizing maps) [71].Unsupervised methods allow the data to make classificationwithout a training dataset (not a priori output) and can beused to follow the physiology of a cell in the course ofa culture. For example, the expansion of undifferentiatedstem cells in culture can be studied, as the outcomeof metabolic changes is not known, or when developingchemically defined media for industrial applications. In bothcases the changes in the physiological response of thecells in culture can be studied and understood in greaterdetail. PCA has become the norm in visualization of data(Figure 4). It makes use of a statistical method for reducingmultidimensional data (such as multiple spectra) down to afew dimensions that can be readily comprehended. Anothermore recently introduced method is ICA (independentcomponent analysis; [72]), a linear representation of com-ponents that are statistically independent [73] that is idealfor non-Gaussian datasets from a large number of samplesand a small number of variables. With ICA we are ableto separate the original source of the components byobserving the signal mixtures of the components, henceit is useful in the separation of different sample classes.Furthermore, it can also extract features from the recordedspectra, which is useful for the identification of the specificmetabolites (biomarkers) from the samples. MCA (multilevelcomponent analysis) has been recently developed [74]to deal with datasets that have variations in different

Figure 4 PCA plot of mouse myeloma NS0 samples

‘C’ represents control cultures, while ‘I’ represents the induced cultures whichhave been activated to overexpress the p21 cytostatic gene.

levels, like variations between organisms and variations intime. Supervised methods used in pattern recognition alsoexist, but require a training dataset in addition to a prioriknowledge for it to function well. This can be used if theorigins of the samples (classes) are known, for example,when studying the different between high producer clonesand their parental cell lines, hence allowing screeningof clones based on ‘rules’ obtained from the metabolome.Examples of these include SIMCA (soft independent modell-ing of class analogy) and neural networks, partial leastsquares [or PLS-DA (partial least squares-discriminantanalysis)], support vector machines, genetic programmingand genetic algorithms. McGovern et al. [75] used geneticprogramming and genetic algorithms to decipher spectraldata from different spectroscopic sources.

Once discriminate analysis has been done, it has beensuggested that statistical significance can be determined byapplying classical statistics such as Student’s t test, ANOVAor MANOVA (multiple ANOVA) [10]. Given that largestatistical variation exists in biological samples, this maynot be fruitful. Another way of dealing with data after thepreprocessing step is to try to detect significant correlationsof components within a data matrix [73]. For a largernumber of samples with sets of identified and quantifiedmetabolites, the correlations between them can be foundusing Person’s correlation coefficient [7], where the distanceof biological connectivity of all the measured metabolites tobe found and enables such ‘distance maps’ to be visualized.One recent introduction is the use of correlation matricesas a step taken after supervised or unsupervised data mining[73]. Such correlations between metabolites allow for theunderstanding of regulatory characteristics in the metabolic



Table 3 Online databases of biochemical references and metabolite profilereferences

Abbreviations: BRENDA, BRaunschweig ENzyme Database; EMP, Enzymes andMetabolic Pathways database; IUBMB, International Union of Biochemistryand Molecular Biology; DOME, Database of OMEs; AMDIS, Automated MassSpectral Deconvolution and Identification System.

Name URL

Biochemical referencesKEGG http://www.genome.ad.jp/keggBRENDA http://www.brenda.uni-koeln.deThe EMP project http://www.empproject.comIUBMB Enzyme Nomenclature http://www.chem.qmul.ac.uk/iubmb/enzymeEcoCyc http://ecocyc.orgMetaCyc http://metacyc.org

Metabolite profile referencesArMet http://www.Armet.orgDOME http://medicago.vbi.vt.edu/dome.htmlAMDIS http://chemdata.nist.gov/mass-spc/amdisMetAlign http://www.metalign.nlMetaGeneAlyse http://metagenealyse.mpimp-golm.mpg.deMeT-RO http://www.metabolomics.bbsrc.ac.uk/

MeT-RO.htm

Metabolic toolsAraCyc http://www.Arabidopsis.org/biocyc/index.jspMapMan http://gabi.rzpd.de/projects/MapManMetNet http://metnet.vrac.iastate.edu/Biosilico http://biosilico.kaist.ac.kr

network and the system has been applied to plant samples[76].

For the proper assignment of metabolites, whetherin terms of identity or models of networks, bioinfomatictools and databases are required. Databases allow for thereferencing of metabolites and the elucidation of chemicalstructures. However, for databases to be widely usedby different communities, the curation system should beuser-friendly and biology-orientated, thus avoiding differentcomputational standards which will limit access to data. Oneway of introducing standards is the use of SBML (SystemsBiology Markup Language) [77], which will allow inter-operability between different models. Two main types ofdatabases are used in metabolomics: the referencebiochemical databases and the metabolite profile databases.Although there are many online reference databases ofboth types currently being developed (shown in Table 3),comprehensive databases for intracellular metabolites aregreatly lacking. To date, few public databases exist. Thosethat do exist are collections of metabolites from literatureor standards and may not bear any resemblance to theactual sample composition of cellular mixtures. Examplesof NMR-based collections of metabolites include the BRMB(Biological Magnetic Resonance Data Bank, Department ofBiochemistry, University of Wisconsin-Madison, Madison,WI, U.S.A.; www.bmrb.wisc.edu/metabolomics/), the MDL

(Magnetic Resonance Metabolomics Database of Linkoping,University of Linkoping, Linkoping, Sweden), ESMRMB(European Society for Magnetic Resonance in Medicine andBiology, Basel, Switzerland; mdl.imv.liu.se) and NMRShiftDB(Cologne University Bioinformatics Centre, Cologne,Germany; nmrshiftdb.cubic.uni-koeln.de). Although still inits infancy, metabolomic communities around the worldagree that certain reporting standards are required. ArMET(architecture for metabolomics), is one such frameworkfor reporting metabolomic data and experiments for datastorage [78].

Applications in cell culture

The rapidly changing face of metabolome analysis, instru-mentation development and data processing are funda-mentally driven by specific application needs. Metabolomics’diverse importance in the medical, nutrition, health andenvironmental fields is its critical factor for growth.As already mentioned, the potential of metabolomics intraditional and emerging areas in cell culture has yet tobe realized. One such area that has created a great interestis in the area of pharmacokinetics and drug testing. Withthe initiative to implement the ‘3R’ (refinement, reductionand replacement) principles [79] in animal testing, tissue cellculture is set to develop into in vitro models as an alternativemeans of drug testing. Metabolomics, together with theother ‘omics’, as well as the bioreactor culture of humantissues, can therefore contribute to the ‘3R’ principles[80]. Metabolomics can be used to characterize tissuegrowth in these bioreactors, thereby validating their use asreplacements for drug testing. Furthermore, metabolomicsis the single best window into the cellular state [81] andtherefore is ideal for drug development and testing. It hasbeen noted that present testing of drugs on animals (namelydogs, mice and rats) is insufficient in clinical testing andthat these human cell cultures may be an alternative forunderstanding the specific pharmacokinetic metabolism ofdrug candidates [81–83]. A metabolomic approach to drugdiscovery using in vitro cultures of cell lines and tissuesis particularly considered to be an appropriate way bywhich SPARs (structure–pathway–activity relationships) canbe determined [84]. These tissues and pathogen-specific celllines allow dosage and metabolic effects to be characterized.Such validation and characterization of drug candidates iscritical in obtaining full clinical approval. This has lead to theUSA FDA (Food and Drug Administration) to acknowledgethe need for such methods to be used together withmanufacturing scale-up development and clinical trials ofbiologics [85]. NMR metabolic profiling has been used tocharacterize human hepatoma cells under specific cultureconditions [86], thus paving the way for understanding the



Figure 5 PCA plot of metabolic profile of a NS0 myeloma batch culture

The circles represent samples taken from cultures across different stages of growth; early (red)-, mid (blue)- and late (green)-exponential phases of growth. Thearrow shows the trajectory of the metabolic profile changes as the culture grows.

metabolism in these cancer cells. Some examples of drug andtoxin metabolism studies include work in endometrial cells[87] and the response of plant [Silene cucubalus (bladdercampion)] cell cultures to environmental toxins such ascadmium [88].

Other applications include plant and mammalian cellmetabolic engineering. While plant metabolomics has beenaround for the last decade or so, mammalian cell metabolo-mics for industrial cell lines is greatly lacking. The use ofmetabolomics in plant cell cultures allows for the deter-mination of important secondary metabolites [89] such asisoflavone and taxol, which have been proved to be effectivepharmaceutical ingredients [90,91]. In the case of flavonalmetabolic engineering, the transformation of ubiquitousnaringenin to the isoflavone genistein as a means of introduc-tion of isoflaviods into legumes [92,93] has been carried out.Taxol, a form of taxoid compound, is an anticancer drugcommercially available on the market. Metabolomic studieson Taxus (yew) cell cultures have allowed for the identi-fication new taxoids [94], which could lead to the furtherunderstanding of the induction of taxoid production in Taxuscells [95] and the production of synthetic versions in otherplant cells [96]. Furthermore, the need to understand theelicitor induction of secondary metabolite production inplant cells also opens up new doors for metabolomic analysis[97] and also emphasizes how metabolomics can be a func-tional genomic tool in studying transgenic plants [43]. One

side of the spectrum, the production of biopharmaceuticalsfrom animal cells, has not embraced metabolomics as atool. This is mostly because metabolites are now not theprimary focus, and the relationship between metabolismand protein production is not fully understood. Yet it isprecisely for this reason that metabolomics can bridge thegap of understanding as to the dynamics of metabolism,cell growth and protein production. One demonstration ofthis relationship is shown in Figure 5, where the metaboliteprofile of cell cultures varies across the age of a batchculture is depicted. In addition, it may be used to optimizeconditions of bioreactors or the development of chemicallydefined media. It can been seen that the rapid growth of cellsgives rise to a metabolic profile that can be distinguishedfrom that of slow-growing cells (S.H.G. Khoo and M.Al-Rubeai, unpublished work). Therefore the effect ofspecific chemicals on metabolism can be screened rapidlyusing metabolomics methods, thus linking chemical entityto high or robust growth and even productivity. Miniaturebioreactors that allow for a variety of conditions to bemonitored can be used together with metabolomic studieswith rapid sampling [98]. Metabolomics has been used tomonitor cell transfections using similar techniques [99]as well as being used as a determinant of apoptosis incell culture [100]. In the latter example, cells sensitive toapoptosis induction were distinguished from resistant cellsand hence showed the potential of cell line selection in



industrial applications. Characterizing cell lines and selectionof cell lines are a vital step in the process development ofbiologics. Once the clone is selected for production, it isunlikely to be changed for the entire lifespan of production.Hence stable and efficient protein production lies on the vitalstage of clonal selection [101]. In addition, cell line geneticdrifts and clonal variations, which are important validatingsteps in cGMP (current Good Manufacturing Practice)production, can be monitored by using high-throughputmetabolomics methods.

Monitoring mutagenesis and gene deletions in cellcultures can also be carried out using metabolomics.Gene knockouts and genetic strategies using RNAi (RNAinterference) can be used with phenotypic screens such asmetabolomics to determine the physiological functions ofenzymes. It has been used to map silent mutations [34,102]as well as to observe pleiotropic effects of single geneticalterations [102], but it has mostly been applied to microbialsystems. Hence it is also a vital research tool and an essentialpart of any cellular modelling process. As the metabolicnetwork in a living cell is viewed as being a complexnetwork of reactions that are tightly connected [103], anyperturbations in the transcriptome and proteome can filterdown to the concentration of metabolites. This makes themetabolome data a form of integrated data, which givesit strength as an ‘omic’ science. Metabolite concentrationand fluxes have been shown to not be regulated by geneexpression alone (e.g. glycolysis in trypanosomes; [104]).This may again disprove the idea of a simple ‘hierarchical’regulation of function by gene expression, as is the casewith proteins and mRNA. Metabolic flux analysis canalso integrate metabolite concentration data, making fluxdata more accurate than hitherto. Principally, using stableisotopes, flux can be determined by feeding strategies as wellas rapid sampling methods [24,105]. Flux analysis with intra-cellular metabolite concentrations can be combined withtraditional isotopomer flux analysis and constraints-basedmodelling to give a better picture of cell phenotypes [106].

Towards systems biology

Data integration is not limited to flux data. The conceptof systems biology is similar to functional genomics in itsapproach, but is slightly different in its objectives. Systemsbiology encompasses a holistic approach to the studyof biology and the objective is to simultaneously monitorall biological processes operating as an integrated system.It involves the iterative interplay between linked activitieswhich allow for the study of signal-processing elementsthat lie ‘downstream’ of the signal initiator. These helpone understand how cross-talk may be occurring betweenpathways [107]. However, as mentioned above, linking

‘omics’ is not always simple, owing to mRNA splicing andtranslation control issues [108]. In addition, a single genemay code for isoenzymes reacting with multiple metabolitesubstrates [109]. The difficulty in determining the timing ofdifferent events, that is, transcription and protein activity[110], also contribute to the difficulty in integrating data.Hence, in order for metabolomics to be used in systems bio-logy, novel strategies will need to be created. One step for-ward in such an integration process is the functional assign-ments between protein/gene and metabolite within asystem of interest [111,112]. This can be done by creat-ing decomposable models [113] where basic biochemicalpathways are first modelled using static data. Subsequently,time-dependent concentrations of other types of compo-nents (transcriptomic and/or proteomic) will then beincorporated. Databases and standardization also play keyroles in this process, as data sharing will be the cornerstoneof any major cellular reconstruction model.

Of particular interest is the coining of the terms‘synthetic biology’ and ‘systems biotechnology’, that is, theengineering of cells with novel abilities [114,115]. In theseproposed processes, systems biology is considered to bethe complete characterization of a cell of interest. Thischaracterization involves the transmission of data by usingmatrices to an in silico process. Modelling and simulation ofthe cellular components follows, and what results will leadto the ‘redesigning’ of the cellular system. Since this is aniterative process, the feedback loops allow for the improvedlearning of cellular dynamics. As with most developments,this is still many years away and the likelihood of itsimplementation in cell culture systems rest upon the initialdevelopments in microbial organisms such as E. coli. Clearly,the exciting prospect of this happening is limited by thepresent state of technological development and the lackof data-handling approaches in dealing with the volume ofinformation. Yet, in due time, the widespread use of meta-bolomics in understanding biological processes will seeboundless benefits.

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Received 3 November 2006/8 February 2007; accepted 20 March 2007Published on the Internet 18 May 2007, doi:10.1042/BA20060221
