Chapter 73

Metabolic Profiling of Plant Tissuesby Electrospray Mass Spectrometry

Heather Walker

73.1 INTRODUCTION

Metabolic profiling is a fairly recent but rapidly expand-ing area of interest; and, as the use of mass spectrometryas an analysis tool has become more common, the numberof publications on metabolite profiling has also increased.There appear to be several definitions of metabolic profil-ing [Fiehn, 2002; Dunn et al., 2005], but essentially it canbe defined as an analysis to identify and quantify as manymetabolites as possible. For nontargeted metabolic profil-ing, the objective is to analyze the maximum number ofmetabolites in a single run, from as many different chem-ical classes as possible, in a quantitative and nonbiasedway, with the minimal loss of metabolites.

Metabolic profiling can be applied to both plantsand bacteria because they are both very adaptable toenvironmental conditions, and it has become increasinglyimportant to identify why and how they adapt (seeChapters 71–74, Vol. I).

Because there are estimated to be over 2600metabolites in Arabidopsis thaliana (see Metabolites inArabidopsis thaliana in Internet Resources) and over1800 metabolites in Escherichia coli (see Metabolitesin Escherichia coli in the Internet Resources section),any full metabolomic analysis needs to be an impartialprocess, and many analyses need to be carried out inorder to gain the whole picture.

This particular chapter will focus on a nontargetedapproach to metabolite profiling using direct infusionmass spectrometry (DIMS). This technique utilizeselectrospray mass spectrometry whereby the sample isinfused into the mass spectrometer at μl min−1 flowrates without any prior separation by high-performance

Handbook of Molecular Microbial Ecology, Volume I: Metagenomics and Complementary Approaches, First Edition. Edited by Frans J. de Bruijn.© 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

liquid chromatography. The electrospray ion sourceis a simple system and can produce many ions withfew breakdown products or fragments; however, it canproduce ions with multiple charges, which can makeaccurate measurement of large molecular masses suchas proteins much easier. It is highly effective for theanalysis of polar compounds and therefore very usefulfor the analysis of plant and bacterial material due tothe abundance of polar compounds that make up theirmetabolic compositions. The example outlined in thischapter will focus on the metabolic profiling of tomatoes.

73.2 MATERIALS AND METHODS

73.2.1 Sample PreparationA general extraction method needs to be employed inorder to maximize the number of metabolites detected,and in fact the extraction procedure itself is often moreimportant than the analysis because, if the extraction pro-cedure is not uniform, the analysis will be meaningless.

The first important step, when extracting plant mate-rial, is to inhibit any enzymatic activity. This can becarried out by using methods such as freeze-clamping,rapid freezing in liquid nitrogen, or acidic treatments withperchloric or nitric acid. Acidic treatments tend to beavoided because they can cause problems for many ana-lytical techniques further down the line. Freeze-clampingis not usually practical for large numbers of samples, sofreezing in liquid nitrogen is usually the most convenientway of preserving the sample. Samples can then be storedat −80◦C until such time as they are extracted. Tissue

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698 Chapter 73 Metabolic Profiling of Plant Tissues by Electrospray Mass Spectrometry

should not be used which has been stored at −80◦C forlonger than four weeks because there is evidence thatconsiderable metabolic changes occur after this time (seeProtocol for Plant Leaf Metabolite Profiling in the Inter-net Resources section). Freeze-drying is another methodthat can be utilized to preserve samples because enzymesare unable to work in the absence of water. Samples thathave been freeze-dried do need to be kept in a desiccatorin order to stop any hydrolyzation of the sample before itis extracted. Hydrolyzation of the sample can lead to theadsorption of metabolites onto cell walls and membranes,so particular care would be needed if metabolite levels ina specific plant area rather than in a whole sample wereof interest.

The sample needs to be homogeneous, so the plantmaterial should be ground in a pestle and mortar underliquid nitrogen, or alternatively by using a cooled ball-mill grinder. Once ground, the plant material needs tobe extracted into a suitable polar medium for electro-spray mass spectrometry. Many methods are based on aprocedure used by Valle et al. [1998] utilizing a chlo-roform/methanol/water extraction whereby polar metabo-lites are extracted into the methanol/water layer and non-polar metabolites are left behind in the chloroform layer.There are many variations on this theme such as usingethanol or heating the extraction mixture; however, nodirect comparisons between similar methods have beenmade, and indeed heating the sample may be too harshfor metabolite analyses because it could lead to the break-down of some metabolites.

Once extracted, the sample is centrifuged, whichenables the two phases of the sample to be separated.These two extraction layers are treated differently.The nonpolar, chloroform phase is highly organic andtherefore not particularly suitable for electrospray massspectrometry analysis. This organic phase could be usedfor targeted analyses such as for chlorophyll, carbohy-drate, or carotenoids. The polar, methanol/water, aqueousphase is used for the metabolite profiling analysis byelectrospray ionization.

The samples, once extracted, should be stored at−80◦C because it is generally assumed that no changesin metabolite levels or composition will occur at thistemperature.

73.2.2 Introducing Samplesto the Mass SpectrometerUsing a global metabolic profiling procedure, samplesare introduced directly into the mass spectrometer for an“everything at once” analysis. Samples can be infusedusing a syringe pump, which is particularly effective atdelivering a low, pulse-free flow or by the use of a liquidchromatograph. The use of a liquid chromatograph system

allows for the analysis to be automated, whereas a syringepump method does not.

There are, however, some matrix issues attached tothis direct infusion method of sample introduction whichmust be taken into consideration. Ion suppression is amatrix effect that can negatively affect the efficiency ofthe ionization and can occur due to the large amountof metabolites that are infused into the mass spectrom-eter at the same time. The direct infusion method canalso expose the instrument to very high concentrations ofmetabolites putting excess burden on the detector. It istherefore important to have metabolites at such a levelthat the larger peaks are monitored accurately without theloss of the smaller peaks, thereby maximizing the amountof information obtained from one run. For the analysis ofsamples of similar composition, matrix effects are thoughtto be minimal because they are assumed to be uniform forall samples within a set. When using direct infusion as asample introduction method, the sample may need to bediluted further than the original extraction to try and min-imize the effects of suppression. With care, the dilutioncan be carried out at a level that succeeds in loweringthe level of ion suppression without the loss of many-lowabundance compounds. The dilution of the sample alsohelps to minimize the buildup of contaminants upon themass spectrometer sample introduction system.

The direct infusion method lends itself toward a qual-itative approach rather than a fully quantitative one in thata comparison of peak area can be used for the metaboliteanalysis provided the sample reproducibility is good, oran internal standard can be used to compensate for anymatrix effects before data comparison takes place.

73.2.3 Optimization of the MassSpectrometer for the Analysisof Plant MaterialRoutine optimization of the mass spectrometer requireshigh sensitivity over a large mass range. For plant mate-rial the optimization needs to be across a wide range butstarting at a lower mass range than is usual for other sam-ple materials. This is because plant material contains alot of low-molecular-weight compounds such as organicand amino acids. Conditions need to be set therefore, toallow analysis of metabolites down to around 50 atomicmass units up to around 800 atomic mass units. Above800 atomic mass units there will be few polar metabolitesin the plant extract which can be detected by electrospraymass spectrometry. To allow the analysis of the lowermasses as well as a large mass range, the cone voltage ofthe instrument needs to be kept low. The cone voltage isthe electric potential applied to attract ions into the massspectrometer. By increasing the cone voltage, ions speedup and can be induced to fragment. If the cone voltage

73.2 Materials and Methods 699

is reduced, the ions slow down and any collisions haveinsufficient energy for fragmentation. The reduced speedalso allows the small ions to be focused into the massspectrometer rather than being dispersed.

73.2.4 Quality Control of LargeBatchesBiological samples always have some amount of natu-ral variation that will lead to variability within the finaldataset; however, other errors can occur due to the sam-ple preparation and the instrumental analysis. For largebatches of samples, in particular, a quality control checkneeds to be in place. Because it is not feasible to run allsamples at the same time, it is imperative that analysescan be compared from one year’s harvest, for instance, tothe next.

Obtaining a reference material, useful for the qualitycontrol of the extraction procedure, is not always feasiblefor biological samples; however, some material can bepurchased from companies such as the Laboratory ofthe Government Chemist (Teddington, UK). An in-housestandard can be prepared, but this would need to bestored at −80◦C for possibly long periods, which forraw, untreated sample is not always recommended. Anextract could be used because this could be kept at−80◦C storage for longer periods but evokes the questionof how long to keep it, how much extract to make up,and what to do when the extract runs out. For long-termstudies, it may be better to freeze-dry a large amount ofmaterial which can then be prepared along with everybatch of samples extracted. This particular route willmake it much easier for storage of samples, although thesample will have to be kept in a desiccator.

Instrumental quality control checks need to be inplace for mass as well as intensity. An internal standardapproach could be used whereby spikes of individualcompounds can be added to the extracts or indeed addedto the sample before extraction as a quality controlmeasure. However, the compound needs to be selectedcarefully in that it must have a mass that occurs ina region of the spectra where there is an absence ofsample peaks. It also needs to be easily and quantitativelyextractable if it is undergoing the extraction procedure.This particular type of quality control, particularly fordirect infusion methods, may not be the most suitablebecause it may be difficult to find an area free of peaks inorder to choose a suitable mass. There is also the possibleproblem of the introduction of contaminants to a sampleand indeed, the interaction of the compound introducedand any contaminants, with other peaks present in thespectrum. This could lead to the formation of othermolecular species within the spectrum or lead to matrixeffects. Some instruments on the market have the option

of monitoring an external standard so, rather than addingan internal standard to a particular sample, the instrumentmonitors a standard that is introduced separately to thesample stream flow, but under the same conditions. Analternative approach for direct infusion mass spectrometryis to use a peak already present in the sample for thecorrection of mass error only. With plant material thereare usually many peaks that are common even betweendifferent species—for instance, simple organic acids andamino acids which could be used for mass correction.For analyses over large mass ranges, more than one peakmay have to be chosen. This is a quick and easy way tocorrect for mass without any adulteration of the sample.

Direct infusion samples are usually normalized tothe largest peak in the spectrum or to the total ion countfor the whole sample, in order to standardize large sets ofdata. The area of quality control and standardization ofdata, particularly for reporting purposes, where analysesare carried out by different research groups, is beinglooked at by several groups. Indeed an entire volume ofthe Metabolomics Journal was given over to this subjectin 2007.

A standard procedure would involve the reportingof many parameters and the Metabolomics StandardsInitiative (see Internet Resources section) is workingtoward developing guidelines for reporting metabolomicswork utilizing working groups for Biological ContextMetadata, Chemical Analysis, Data Processing, Ontology,and Exchange Format [Fiehn et al., 2007]. The proposedstandard reporting guidelines need to encompass goodanalytical chemistry practices as well as allow for largedatasets of metabolomic data to be compared electron-ically and across different instrumental techniques. Theneed is to obtain a minimum set of criteria for reportingwhich describe the experiments carried out so that anyinformation published is useful across the wider com-munity by standardizing any data submitted to academicjournals. Frameworks and procedures for the standard-ization of reporting of metabolic experiments have beenproposed by Jenkins et al. [2004] and Lindon et al. [2005].The Metabolomics Society also, has begun a discussionforum on Standardization in Metabolomics Experimentsencompassing drafting a document on best practice,reporting and data exchange (see Internet Resourcessection). Bino et al. [2004] suggested a checklist ofinformation needed in order to publish metabolomic databut does not give a complete set of guidelines. Jenkinset al. [2004, 2005] also discussed the use of ArMet (anarchitecture for metabolomics) (see Internet Resourcessection) which is a complete and formal data descriptionfor plant metabolomics, supporting datasets, and exper-imental context information. The ArMet data model isfreely available on the web, and it is hoped that it willlead to the long-term adoption of a standard model that

700 Chapter 73 Metabolic Profiling of Plant Tissues by Electrospray Mass Spectrometry

will serve as a guideline for researchers, journals, funding,scientific and regulatory bodies, as well as vendors.

Data files are usually exported as text, thereby keep-ing the size of the files small and more manageable. Textfile format is a universal one that allows the data to beintroduced easily into many different applications—forinstance, Excel spreadsheets as well as web- and grid-based systems.

The original spectra need not be kept because spectradata files can be very large, particularly if each individualspectrum is stored separately. Alternatively, the spectracan be added together so all scans within a run are storedas one “summed” spectrum, making the data file muchsmaller in size. Having the individual spectra, however,allows any discrepancies in the text format to be inves-tigated easily so it can be invaluable, but only if storagefacilities exist for these large data files.

The use of database software is becoming increas-ingly useful for the storage of spectra and other largefiles, and these can often be found published on the web.Metabolite and transcript data for Arabidopsis thalianaand tomato are already published on the web (see InternetResources section).

73.2.5 Data Processingand InterpretationThe direct infusion of samples into a mass spectrometerfor a global metabolic analysis produces very large quan-tities of data. These data need to be handled routinely, soany reduction in the volume of data is highly desired.

Several ways of reducing the data have been utilized,specifically a noise reduction and a binning procedure.These two procedures can be used separately or together.

Noise reduction is a way of removing small, noisepeaks and ideally, needs to be carried out without placinga particular threshold value on the data. The use of athreshold value can mean that small peaks are lost wherethey fall below the cutoff value, and indeed there is thequestion of where to set this threshold value. A simpleway to bypass this problem is to run all samples threetimes and then combine these data with the rule that onlypeaks seen within all three runs will be accepted as a realpeak. If a peak appears in only one or two of the repeats,it will be excluded from the final dataset because it isassumed to be noise [Overy et al., 2005]. By using thismethod, the data can be significantly reduced without theloss of any of the small peaks.

The binning procedure involves binning data into set-sized atomic mass unit bins, thereby limiting the numberof entries to deal with. For instance, a scan from 50 to 800atomic mass units can easily produce over 4000 peaks,even after noise reduction has been carried out. By sortingthese data into single atomic mass unit bins, the data are

reduced to 750 entries for this particular scan range. Eachbin contains metabolites from ±0.5 atomic mass unitsaround a whole atomic mass unit; for example, mass bin191 contains all metabolites found between 190.5 and191.5 atomic mass unit. This approach allows the datasetto be compacted, making it much easier to handle, andalso provides a simple way to compare the profiles ofsamples where many masses are observed and wherethe masses obtained may not be exactly identical. Wheredifferences in a particular bin are discovered, the originaldataset and spectra can be looked at in order to investigatefurther and obtain an accurate mass for a specific peakwithin a bin. Different bin sizes other than one atomicmass unit can be used, although a minimum size is likelyto be found whereby the binning procedure becomes lessuseful mainly due to the error in measuring the mass. Themain disadvantage of using this binning data techniqueis that a bin may include more than one peak that maylead to a large peak within a bin masking a change ina smaller peak; however, the advantages of handling amuch smaller dataset tend to outweigh any disadvantages.

Once a metabolite profile has been obtained foreach sample within a set, the data are transferred to asoftware package to carry out a statistical analysis ofthe data. Some packages utilized for this purpose includeSIMCA-P (Umetrics, Windsor, UK) and SPSS (SPSS UKLtd., Woking, UK), which can run a principal componentanalysis. This analysis is a dimension reduction techniquethat is used to examine the relationships between a set ofcorrelated variables. Its aim is to reduce the number ofvariables by creating a small number of principal compo-nents that explain most of the variation in the data. Thisis a standard way of looking at metabolite data, althoughother groups have investigated the use of other statisticaltechniques (see Chapter 74, Vol. I). Goodacre [2005]has investigated the use of evolutionary computation,Tokimatsu et al. [2005] have investigated a web-basedanalysis tool based on the use of vectors, and Fiehn[2001] and more recently Hageman et al. [2008] havelooked at clustering techniques for analysis of the data.

These types of statistical analysis are particularlysuited to the handling of data for a global metabolomicanalysis where no identification of each peak is carriedout and the nontargeted approach allows for an unbiasedanalysis. Any differences found using this approach canbe investigated further using more targeted approaches.For instance, if a peak in mass bin 191 (in negative mode)was found to be changing, it could be identified usingtriple quadrupole mass spectrometry. If the peak wasfound to be citric acid and quantitation was needed, thiscould be achieved by using a standard high-performanceliquid chromatography method. However, if a targetedapproach had been used, this changing peak may not

73.2 Materials and Methods 701

have been found, unless the analysis had specificallytargeted organic acids.

73.2.6 Metabolic Profilingof TomatoThe example discussed will illustrate the metabolic profil-ing of tomatoes by electrospray time-of-flight mass spec-trometry. Tomatoes are a commercially important crop,and tomato fruit yield and quality are regulated by a widerange of genetic and environmental factors. Therefore, theability to enhance yield and quality is highly prioritizedin current breeding programs because centuries of plantbreeding and domestication have resulted in modern cropswith a very narrow genetic base. Increased genetic diver-sity and agronomic value of cultivated crops can be gainednaturally by cross breeding with wild relatives; however,in order to maximize and predict yield and quality, theremust be an understanding of (a) the function of genesand (b) the relationship between a plant’s genome and itsmetabolites. The tomato is currently in the process of hav-ing its entire genome sequenced, and the current status ofthis sequencing can be observed on the web (see InternetResources section).

The common tomato, Solanum lycopersicon (for-merly known as Lycopersicon esculentum), belongsto an extremely diverse and large family called thesolanaceae. Often referred to as the Nightshade family,this family contains many commonly cultivated plants(potato, peppers, aubergine, tobacco, petunias) as wellas various weeds (nightshades, jimson weed). The genusLycopersicon consists of a vast reservoir of geneticvariability, much of which remains underexploited,although some disease resistance in tomatoes has beenderived from related species within the Lycopersicongenus. Other species within this genus may provide otherpotential benefits such as heat or cold tolerance, salttolerance, or increase in nutrient amounts. Currently,tomatoes are of interest in the medical field due to thehigh levels of carotenoids they contain. Lycopene, acarotenoid present in tomatoes, is one of nature’s mostpowerful antioxidants and has been found to be beneficial

in preventing cancer, particularly prostate cancer [Gannand Khachik, 2003].

Eshed and Zamir and colleagues [Eshed et al.,1992; Eshed and Zamir, 1994] generated a popula-tion of introgressed lines (ILs) of the tomato speciesSolanum lycopersicon containing defined chromosomalintrogressions from the wild species Solanum pennellii(formerly Lycopersicon pennellii ). These introgressionlines were produced by crossing a cultivated Solanumlycopersicon with a wild relative Solanum pennellii tocreate an F1 hybrid. This F1 hybrid was then backcrossedto the Solanum lycopersicon for six generations in orderto successively reduce the proportion of the Solanumpennellii chromosome. Self-fertilization produced lineshomozygous for the Solanum pennellii introgression,and each introgression line produced possesses a singlechromosome fragment of the wild species in the culti-vated Solanum lycopersicon . See Figure 73.1 for thephenotypes of the two parents and the F1 hybrid tomatospecies.

The entire tomato genome in this set is representedby 50 introgression lines, with each line homozygous fora single introgressed region. See Figure 73.2 for someexamples of the phenotypes seen from the set of 50 intro-gression lines. Phenotypes for the other introgression linesin the set of samples did not differ significantly from thephenotype for the Solanum lycopersicon parent and thusare not reproduced here.

Subsequent analysis of these introgression lines canincrease the knowledge of the genetics underlying certaingiven traits. Several quantitative trait loci (QTL) for ben-eficial agronomic traits such as fruit soluble solids, fruitweight, yield, and color have previously been identified[Eshed and Zamir, 1995; Eshed et al., 1996; Liu et al.,2003] as well as some unbeneficial traits such as unde-sirable volatiles [Tadmor et al., 2002] and leaf dissection[Holtan and Hake, 2003]. These traits have been identi-fied using targeted approaches utilizing specific methodsof sample preparation and analysis. The use of a metabolicprofiling approach enables a global picture to be obtainedfor a sample after a standardized extraction procedure anda single instrumental analysis. The benefit of this approach

Figure 73.1 Phenotypes observed for parents inorder from left to right Solanum lycopersiconparent, F1 hybrid, and Solanum pennellii parent.

702 Chapter 73 Metabolic Profiling of Plant Tissues by Electrospray Mass Spectrometry

Figure 73.2 Phenotypes observed forintrogression lines 3-2 (LA4044), 4-4 (LA 3494),2-5, (LA 4040) and Solanum lycopersicon parent(LA 3475).

is that it can then be used to target any specific traits thatare identified.

73.2.7 Sample PreparationThe seeds for the tomatoes used in this study wereobtained from the Tomato Genetic Resource Center,University of California, Davis. Eight plants of eachline were grown in pots in a glasshouse under ambientconditions, using Levington M3 compost with supple-mented nutrients (Osmocote, Scotts Ltd, UK). Plantswere randomized throughout the glasshouse in order tominimize the effects of local environmental conditions.Plants were watered daily and fed weekly with a com-mercially available tomato food (Tomato liquid, LBSHorticulture, Lancashire, UK) after flowering. Sampleswere taken from the pericarp (the fruit wall) of ripetomatoes and were immediately frozen in liquid nitrogenafter harvesting. Ripeness was judged as being 7–10 dayspost breaker, with the breaker being classified as the firstappearance of carotenoid coloration (i.e., turning red).Samples were extracted following a procedure developedby Overy et al. [2005] by taking 0.1g of the tomatopericarp and grinding it with 750 μl of an extractionmedium mixture of 80 μl of water, 200 μl of chloroform,and 470 μl of methanol. This mixture was then left on icefor 30 minutes. The aqueous, polar phase was removedand the organic phase was then re-extracted twice morewith 400 μl of water, and these polar phases werecombined, centrifuged at 12,000g for two minutes, andretained for analysis by electrospray mass spectrometry.The nonpolar phase was retained and could be used for

the analysis of nonpolar compounds such as chlorophyllor carotenoids, but was not used for this particular study.For metabolite profiling, samples were extracted fromthree fruits per plant and three plants per introgressionline, thus giving nine replicates per line. Because thereis always some degree of biological variation within asample set, nine replicates gave enough data for a goodstatistical analysis. After extraction the extracts werestored at −80◦C until analysis was carried out.

73.2.8 Analysis by MassSpectrometryAll analysis was carried out using a Waters MicromassLiquid Chromatograph Time-of-Flight mass spectrometer(LCT) (Waters, Manchester, UK). Time-of-flight instru-ments work on the principal that ions of different m/z(mass to charge ratio) are accelerated by an electric field.These ions achieve velocities that are dependent upontheir m/z values and the accelerating field to which theyhave been subjected. Among the advantages of time-of-flight measurement of mass spectra is the very shorttime needed to measure a mass spectrum, because only afew microseconds is needed for a mass range scan from50 to 800 atomic mass units. Time-of-flight instrumentsalso provide a better mass resolution than traditionalquadrupole instruments [Dunn et al., 2005], and thishigher resolution allows the detection of metabolitesof different monoisotopic masses which have the samenominal mass. An accurate mass measurement can helpwith metabolite identification; however, for confirmationof metabolite identities, more specific analyses may

73.2 Materials and Methods 703

need to be carried out because an accurate mass cannotdistinguish between isomers. Accurate mass also becomesless useful at higher masses where the number of possiblemetabolites at a particular mass becomes much larger.The mass spectrometer was operated in both positive andnegative modes in order to analyze as many metabolites aspossible. See Tables 73.1 and 73.2 for the conditions used.

Spectra were collected over the mass range 50–800atomic mass units at a rate of one scan per 0.5 s withan interscan delay of 0.1 s. The RF lens value was set at75 V to help optimize the detection of low mass ions asdiscussed earlier in the chapter.

A problem that occurs with the analysis of tomatofruit extracts is the buildup of material on the mass spec-trometer inlet system, particularly the extraction cones.When analyzing plant material, the extracts produced areusually very clean and cause minimal problems to thesample inlet system apart from routine maintenance clean-ing. With tomato fruit, however, a buildup of a dark,caramel-type deposit occurs after only a few days of run-ning samples. This is probably caused by the large amountof sugars in the fruit samples but does mean that rou-tine cleaning of the instrument must be carried out everyweek. It is possible that a different extraction proceduremay help; however, as the purpose of this analysis is to tryand monitor as many metabolites as possible within onestandardized run, the extraction procedure needs to be asuniversal as possible without discrimination toward anyparticular class of compounds. For ease of analysis, oneextraction procedure is used for both positive and negativeanalyses.

A Lockspray™ interface (Waters, Manchester, UK)was used which allowed a reference standard to run exter-nally along with the samples to check for mass drift. Allspectra were automatically corrected for mass using thisexternal standard before data processing occurred.

The samples were run automatically using a liquidchromatograph system for the sample infusion, and a qual-ity control standard was run after every 10 samples to

Table 73.1 Negative Ion Mode Electrospray MassSpectrometer Conditions

Parameter Setting

Resolution 4000 (full width at halfheight) at 300 m/z

Capillary Voltage 2800 VExtraction Cone 1 VSample Cone 20 VRF Lens 75 VSource Temperature 110◦CDesolvation Temperature 120◦CDesolvation Gas Flow Rate 400 Lh−1

Table 73.2 Positive Ion Mode Electrospray MassSpectrometer Conditions

Parameter Setting

Resolution 6000 (full width at halfheight) at 300 m/z

Capillary Voltage 3000 VExtraction Cone 5 VSample Cone 20 VRF Lens 75 VSource Temperature 120◦CDesolvation Temperature 150◦CDesolvation Gas Flow Rate 400 Lh−1

check instrument performance and abundance. The liquidchromatograph system was used only as a flow injectionsystem, to automate the analysis.

In order to try and minimize any matrix effectsand contamination of the instrument, due to buildup ofdeposits on the sample introduction system, the sampleswere diluted 1:1 with 50:50 high-purity water:methanol,before analysis. All introgression lines and parents wererun in both positive and negative modes.

Figure 73.3 shows two examples of mass spectrain negative ionization mode from the two parent fruitextracts of Solanum lycopersicon and Solanum pennellii .The differences between the two spectra can be observedimmediately, and it can also be observed that most of thehigh abundance metabolites are observed at the lower-mass end of the spectra. This pattern is common for mostaqueous plant extracts using electrospray mass spectrome-try and is further enhanced by the fact that the instrumentis optimized for these low masses.

The spectra obtained in positive mode are reproducedin Figure 73.4 for the two parent species, and they areagain observed to be very different from each other. Thereare fewer high-abundance peaks appearing in the low-mass region and more high-abundance peaks seen in thehigh-mass region for the Solanum pennellii parent as com-pared against the Solanum lycopersicon parent.

Higher-mass peaks tend to be seen more in the pos-itive mode spectra rather than the negative mode spectradue to the increased stability for positive ions with increas-ing mass. Less identification has been possible for themetabolites seen in the positive ion mode as opposedto the negative ion mode. This is partly because of themore complex nature of the spectra, which can includemore molecular species formation, but is also due to theincreased number of high-mass peaks that appear. Identifi-cation of high-mass peaks, using the electrospray time-of-flight instrument, which relies on accurate mass withoutany structural identification, is very difficult due to theincreasing number of isomers that are possible.

704 Chapter 73 Metabolic Profiling of Plant Tissues by Electrospray Mass Spectrometry

Figure 73.3 Raw metabolic profile mass spectra in negative ionisation mode for two parent ripe tomato fruit species extracts. The peaks inthe spectra are annotated with m/z (top number) and intensity (bottom number). (Top) Solanum lycopersicon . (Bottom) Solanum pennellii .

73.2.9 Data Processingand InterpretationThree replicate mass spectra of each individual samplewere obtained in order to check the instrument repro-ducibility and enable noise reduction to take place. Noisereduction was carried out according to the proceduredefined by Overy et al. [2005], which deems a true peakto be one that is present in all three instrumental replicateruns, as discussed earlier. This allows for all peaks regard-less of intensity to be selected as true peaks and allowseven the smallest peaks to be monitored. The methodemploys a linear equation to define the mass variance

between the three replicate runs, because this was foundto generate the maximum number of true peaks with theminimum false positives or non-true peaks. A differentequation was used for both positive and negative modes.Once a peak was selected as a true peak, the mean of thethree masses over the three replicate scans was used as theaccurate mass, and this value along with the correspond-ing average intensity made up the metabolite profile. Tominimize the variation between different samples anddifferent analysis batches, the data for each sample werenormalized to the total ion count for each replicate. Oncethese data transformations had taken place, the data wererounded into one atomic mass unit bins, as previously

73.3 Results and Discussion 705

Figure 73.4 Raw metabolic profile mass spectra in positive ionization mode for two parent ripe tomato fruit species extracts. The peaks in thespectra are annotated with m/z (top number) and intensity (bottom number). (Top) Solanum lycopersicon . (Bottom) Solanum pennellii .

discussed, and the abundances within a bin were summedas a percentage of the total ion count. This binned datasetwas saved as a metabolite profile in text file format.

Once a metabolite profile had been obtained for eachsample, the data were transferred to a software package,SIMCA-P (Umetrics, Windsor, UK), to carry out a prin-cipal components analysis.

73.3 RESULTS AND DISCUSSION

In this particular example, using 50 introgression linesand two parents along with nine replicate samplesper line, an enormous amount of data was generated.Hence, comparisons between the datasets rapidly become

very complex and time-consuming to process. Also,the Solanum pennellii parent was observed to be verydifferent from the Solanum lycopersicon parent, notonly by the principal component analysis separationbut also due to their very different phenotypes, seen inFigure 73.1. For these reasons, the 50 introgression lineswere individually compared to the two parents, as wellas to the other introgression lines within their chromo-somes. Every introgression line showed clear principalcomponent analysis discrimination between itself andthe two parents. Not all principal component analysisgraphical representations are reproduced here; a few arereproduced as an example of the type of plots expected.Two examples of principal component analysis plots are

706 Chapter 73 Metabolic Profiling of Plant Tissues by Electrospray Mass Spectrometry

detailed below in Figures 73.5 and 73.6. Figure 73.5reproduces the plot for introgression line 4-4 in negativemode, and Figure 73.6 reproduces the plot for introgres-sion line 4-4 in positive mode. All introgression lines usedin this study could be distinguished from the two parentlines within a principal component analysis, and all plotsare published on the web (see Internet Resources section).

In many cases, the metabolites that were identifiedas changing were similar for all lines, and this is to beexpected because genetically the difference between someof the introgression lines is small. Differences that arethe most interesting, from a commercial point of view,are those that are associated with taste, smell, or color.For color comparison, the metabolites of interest are mostlikely found in the nonpolar, organic phase of the extract,so they would not be picked up by this particular anal-ysis that has concentrated on the polar extracts. Also,compounds that contribute to smell may be too volatileto be analyzed by electrospray mass spectrometry and

may be better suited to analysis by gas chromatographymass spectrometry. However, compounds that contributeto taste should be picked up by an analysis of the polarextract, because these include organic acids and sugars.These compounds can all be easily seen in negative mode,using electrospray mass spectrometry. Due to the lack ofmolecular species formation in negative mode, other thanthe (M-H)− molecular ion, it is possible to identify someof the small metabolites easily.

It is significant that different principal componentanalysis separations can be observed with data fromthe different modes, and often the separation seen canbe better in one mode rather than the other. From theprincipal component analyses that were carried out, alist of the most changing metabolites, whether they beregulating up or down, could be obtained, and this leadsus onto the next challenge. From this list, the search isfor common metabolites as well as for major differencesbetween the introgression lines. The spectra obtained in

Figure 73.5 PCA plot for introgression line 4-4(3494) in negative mode plotted against parentsSolanum lycopersicon (lyco) and Solanum pennellii(pen).

Figure 73.6 PCA plot for introgression line 4-4(3494) in positive mode plotted against parentsSolanum lycopersicon (lyco) and Solanum pennellii(pen).

Internet Resources 707

negative mode were dominated by peaks for malic acid(peak at 133) and citric acid (peak at 191), as seen inFigure 73.3. These organic acids are very sensitive toelectrospray mass spectrometry, particularly because theinstrument is optimized for low mass, so it must be notedthat peak intensity is not necessarily a reflection of therelative concentrations of these metabolites.

In the negative mode case, which has two very dom-inant peaks, differences are sometimes identified from theprincipal component analysis, which are due to the size ofthese large peaks rather than the changes between them.This can sometimes skew the principal component anal-ysis slightly because small changes in the large peaksare not always significant. This is where scaled and more“educated” methods become increasingly useful, such as adiscrimination function analysis as pioneered by Goodacre[2005]. This type of method is a little more intelligent inthat it can look beyond the original analysis and mine thedata for more specific changes by using a supervised andtraining approach to the analysis.

For the comparison of introgression lines withina chromosome, many different patterns were obtainedbecause some introgression lines were very differentfrom each other whereas others showed similarities. Inthis practical example the principal component analysisdemonstrated that the Solanum pennellii parent was verydifferent not only from the Solanum lycopersicon parentbut also from all the other introgression lines. This is notunexpected, due to the inclusion of just one introgressionfrom the Solanum pennellii parent into each introgressionline.

Due to the small differences seen between some ofthe introgression lines, further investigation using a super-vised data analysis technique, such as a discriminationfunction analysis, would be very useful in obtaining fur-ther information. The discriminant function analysis takesthe data from the principal component analysis, discountsthe data from the samples that have already separatedout, and reanalyzes the data. This allows the analysis toconcentrate on the differences between the remaining sam-ples. In this case, using all the data, only the Solanumpennellii parent separates out from all the other intro-gression lines in the principal component analysis. Thediscriminant function analysis takes the data, excludes thatobtained for the Solanum pennellii , and reprocesses. Thisis an iterative process because the discriminant functionanalysis can then be repeated several times.

The initial principal component analyses wereinvestigated for differences between samples. Work haspreviously been carried out on the measurement ofdissolved sugars in this set of tomato introgression lines[Eshed and Zamir, 1995], and this enables the comparisonof results found using a metabolite profiling method

with those observations obtained using more traditionalmethods.

The main purpose of this study was to identifyany interesting differences using a global metabolicapproach with the possibility to study them further usingmore targeted analyses. Therefore only minimal attemptswere made to identify any metabolites found at thisstage.

73.4 SUMMARY

Metabolic studies that utilize a direct infusion methodcan be used to detect concentrations down to micromolelevels. Because electrospray ionization leads to minimalfragmentation of molecular ions, it produces a lesscomplex mass spectrum than that of chemical or elec-tron impact ionization methods. The spectra produced,however, can still be very complex, but the presence ofmolecular ions increases the ability to identify metabo-lites, without the use of chromatographic separation.Electrospray mass spectrometry is the technique of choicebecause it is also highly sensitive to many metabolitesfound routinely in plant and bacterial extracts.

For this particular case, metabolite profiling isinvaluable in order to reduce the number of introgressionlines of interest, down to a more manageable set. It alsoenables the identification of specific samples showingthe largest or the most interesting differences, which canthen be used for further metabolite profiling or for moretargeted analysis.

Metabolite profiling can also be used with discrimina-tive statistical methods in order to mine the data for furtherinformation. In this way the metabolic profile route givesa very quick and efficient way of targeting areas of interestwithin a large dataset.

The practical example outlined fulfills the criteria ofa metabolic profile analysis by increasing knowledge ofplant systems in a very quick and routine way, and thissimple procedure can be used successfully with bacterialas well as plant material.

INTERNET RESOURCES

Metabolites in Arabidopsis thaliana: http://pmn.plantcyc.org/PLANT/class-instances?object=Com-pounds

Metabolites in Escherichia coli : http://biocyc.org/ECOLI/class-instances?object=Compounds

Protocol for Plant Leaf Metabolite Profiling:http://fiehnlab.ucdavis.edu/publications/Arabidopsis%20Protocols%202nd%20ed%20-%20Fiehn.pdf

708 Chapter 73 Metabolic Profiling of Plant Tissues by Electrospray Mass Spectrometry

Metabolomics Standards Initiative: http://msi-work-groups.sourceforge.net

Standardization in Metabolomics Experiments: http://129.128.185.121/metabolomics_society/stand-ardization.html

ArMet: http://www.armet.org

Arabidopsis thaliana Transcript and Metabolite Data:http://kpv.kazusa.or.jp/kpv4/

Tomato Metabolite Data: http://ted.bti.cornell.edu/

Tomato Genomics: http://solgenomics.net/

PCA of all Tomato Introgression Lines: http://www.shef.ac.uk/aps/tomato/index.html

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