monitoring dynamic changes in lymph metabolome of fasting and fed rats by matrix-assisted laser...

ORIGINAL RESEARCH

Monitoring dynamic changes in lymph metabolome of fastingand fed rats by matrix-assisted laser desorption/ionization-ionmobility mass spectrometry (MALDI-IMMS)

Kimberly Kaplan & Shelley Jackson & Prabha Dwivedi &W. Sean Davidson & Qing Yang & Patrick Tso &

William Siems & Amina Woods & Herbert H. Hill Jr.

Received: 17 February 2012 /Revised: 15 May 2012 /Accepted: 16 May 2012 /Published online: 6 June 2012# Springer-Verlag 2012

Abstract Matrix-assisted laser/desorption ionization (MALDI)time-of-flight mass spectrometry (TOF) has been investigatedfor use in the field of metabolomics; however, difficulties,mainly due to chemical interferences, are typically encoun-tered. By coupling MALDI with ion mobility time-of-flightmass spectrometry (IMMS), isomers and isobars are resolvedin mobility space reducing the chemical interference frommatrix/background ions. MALDI-IMMS offers the advan-tages of high sensitivity, high throughput and low sampleconsumption. For this study, MALDI-IMMS was evaluatedby monitoring metabolic changes in lymphatic fluid collectedfrom fasting and fed rats. The number of metabolite fea-tures detected in the samples ranged between 1200 and3400 depending on the duration between the feeding timeand lymph sample collection. There were 747 metabolitefeatures that were statistically analyzed by principal com-ponent analysis (PCA). From the 3-D score plots of PC1,PC2 and PC3 65 % of the original variation of the systemwas explained and the differences between the sampleswere demonstrated.

Keyword MALDI-IMMS .Metabolomics . Principalcomponent analysis

Introduction

Electrospray ionization (ESI) and matrix-assisted laser/de-sorption ionization (MALDI) coupled to mass spectrome-try (MS) have been the primary ionization sources usedfor the mass spectral analysis of proteomic, lipidomic, andmetabolomic samples [1]. Both are soft ionization techni-ques that enable the ionization of biomolecules into thegas phase with minimal to no fragmentation. MALDI hasbeen confined to high-molecular weight (MW) compo-nents due to the substantial chemical background signalsgenerated by the matrix [2, 3] that interferes with lowMW compounds such as metabolites [4]. Despite severaldifferent approaches such as altering analyte/matrix ratio[5], applying different target plates [6], and varying matrices[1], MALDI-MS based metabolomics still experience inter-ferences from the matrix.

Ion mobility-mass spectrometry (IMMS) variations ofmass analyzers with different mobility cells have beenreviewed [7]. MALDI-IMMS has been applied to biologicalsystems such as profiling E. coli metabolome [8], lipidimaging [9], peptide-peptide interactions[10], as well aspeptide-quaternary amines interactions [11]. One of theprimary advantages for MALDI-IMMS when applied tosmall molecule analysis is the ability to separate the matrixfrom low molecular weight compounds in mobility spacealong with rapid isomeric separation [12–14]. Thus, IMMSprovides the ability to use conventional MALDI matricesand plates for small molecule analysis. Another added ben-efit of MALDI-IMMS is bio-molecules which share simi-larities in structure such as a class of compounds (e.g. aminoacids, peptides, carbohydrates, etc.) form trend lines (TLs) thatare unique to that class [15–20]. In complex metabolite stud-ies, identification of unknowns can be a daunting challenge

K. Kaplan : P. Dwivedi :W. Siems :H. H. Hill Jr. (*)Washington State University,Pullman, WA 99164, USAe-mail: [email protected]

S. Jackson :A. WoodsNIDA IRP, NIH,Baltimore, MD 21224, USA

W. S. Davidson :Q. Yang : P. TsoUniversity of Cincinnati,Cincinnati, OH 45221, USA

Int. J. Ion Mobil. Spec. (2013) 16:177–184DOI 10.1007/s12127-012-0102-4

even when accurate mass methods are employed. TLs canreduce the search of a specific metabolite if it falls along aknown TL.

One area in which metabolomics has been particularlyuseful is that of the investigation of individual diets onmetabolomes. Novel metabolomic technologies have en-abled biomarker discovery of dietary nutrients and non-nutrients to give nutritional recommendations for personalhealth [22]. Nutrimetabolomics, a rapidly developing field,is defined as the interaction between dietary changes andmetabolites [21]. In a recent review article, the challengesand advances of nutrimetabolomics were presented [22].Current studies have given insight into the metabolicresponses of humans or animals with changes in diet includ-ing the effects of catechin, a flavonoid, in rats fed a high fatdiet [23]; the effect of diet on metabolites in purine metab-olism and the urea cycle that center on metabolism of aminogroups in different diets given to rats [24]; and variations inbiomarkers were quantitatively measured in human plasmaand cerebrospinal fluid (CSF) using liquid chromatography/mass spectrometry (LC/MS) [25].

In a recent investigation, electrospray ionization followedby atmospheric pressure ion mobility spectrometry demon-strated that ESI-IMMS could be used to follow the meta-bolic process from fasting-to-fed-to-fasting conditions ofmale Sprague–Dawley rats [17]. ESI sources however arequasi-real time sources. That is, samples can be electro-sprayed into an IMMS directly from the extract at the timeof extraction utilizing hundreds of microliters of sample.Whereas with MALDI sources, sample consumption is typ-ically less than tens of microliters and spotted onto a targetbefore ionization and analysis. The objective of this studywas to evaluate MALDI-IMMS as a potential metabolomicsand nutrimetabolomics technique coupled with multi-variant technique (PCA [26]) for determining changes inmesenteric lymph metabolites of rats that were fasting or fedwith a high fat, protein, and carbohydrate meal. The primaryaim of this paper was to determine if MALDI- IMMScoupled with statistical analysis (PCA) can be used to viewdifferences between fasting-to-fed-to-fasting conditions andprovide similar information to that obtained from the previousESI-IMMS study [17].

Experimental

Animal preparation

Three male Sprague–Dawley rats were used. After an over-night fast, the main mesenteric lymph duct was cannulatedwith vinyl tubing (0.8 mm OD) while rats were underhalothane anesthesia, according to the procedure originallydescribed by Bollman et al. [27] and modified by Tso et al.

[28]. In addition, a silicone tube (2.2 mm OD) was placed2.0 cm into the duodenum through the proximal stomach.The incision in the stomach wall’s fundus was closed with apurse-string suture. The rats were placed into restrainingcages, and the surrounding temperature was kept at 30 °Cduring the overnight recovery and the next day’s experi-ments. Postoperatively, animals were given a glucose-salinesolution (145 mM NaCl, 4.0 mM KCl, and 0.28 M glucose)via the intraduodenal cannula at a rate of 3.0 mL/hour for16 h before the experiment then infusate was changed tosaline. The experiments were always performed on the dayafter surgery.

Reagents

HPLC grade methylene chloride (J.T. Baker (Phillipsburg,NJ)); ACS grade methanol (EMDChemicals Inc. (Gibbstown,NJ)).

Lymph nutrient treatment

On the day of the experiment, lymph was collected for1 h as the fasting sample. The saline infusion into theduodenal cannula was then replaced by 3.0 mL ofEnsure, a commercially available dietary supplementfrom Ross Laboratories, Columbus, OH. Thirty minutesafter the bolus dose of Ensure, 3.0 mL/hour of salinewas infused again until the end of the experiment.Lymph fluid was collected at hourly intervals for 6 hin graduated glass tubes surrounded by ice. The advan-tage to this experimental design is that each animal acted asits own control. A total of three rats were treated, with sevensamples collected per rat (n021). The samples were placed ondry ice and shipped toWashington State University for metab-olomic analysis.

Metabolite extraction

Chen et al. [29] metabolite extraction protocol was fol-lowed at Washington State University. Metabolites from100 μL of rat lymph were extracted in methylene chlo-ride using a 2:1 v/v ratio (solvent: lymph) evaported todryness with a nitrogen stream and reconstituted in500 μL methanol. The samples were centrifuged for30 min at 13, 000 RPM and at ambient temperature.The first set of data has been published using electro-spray ionization IMMS and was conducted at WashingtonState University [17]. The supernatant was spotted 1:1with dihydroxybenzoic acid (DHB) (Fluka (Allentow,PA)) onto a MALDI plate and flown at ambient temperatureto National Institute on Drug Abuse Intramural ResearchProgram (NIDA-IRP) (Baltimore, MD) where the MALDI-IMMS was performed.

178 Int. J. Ion Mobil. Spec. (2013) 16:177–184

Matrix-assisted laser/desorption ionization ion mobilitymass spectrometer

TheMALDI-IMMSwasmanufactured by Ionwerks (Houston,Texas) instrumental details are described elsewhere [9]. Thedata was collected in positive ion mode with an averagemobility resolution (drift time/half-width of peak (td /t1/2)) of20 and a mass resolution of 1500 for m/z <1000. The mobilitycell length was 15 cm and operated with 3.5 Torr He pressure.An X-Y sample stage (National Aperture Inc.) provided 1 μmaccuracy in beam positioning and sample scanning. A ND:YLF UV laser (Crystalaser, λ0349 nm at 200 Hz) was used togenerate ions in the source at the operating pressure of themobility cell. The plume of ions enters the mobility cell wherethe electric field was applied to the electrode rings within theion mobility spectrometer. The ions separate based on collision

cross section to charge (ΩD/z) ratio and pass through theinterface where they are pulsed orthogonally into a time-of-flight (TOF) mass spectrometer. In the TOF the ions weremass analyzed and with the mobility two dimensional datawere acquired. The mobility drift time was collected inmilliseconds and the TOF MS was collected in μs; there-fore, for every one mobility spectra, hundreds to thousandsof MS spectra are measured depending on the mass rangeand drift time scan time. Mass spectra acquired were storedindividually along with the mobility time at which it wascollected. The individual mass spectra were summed overseveral hundred laser shots so that the ion mass as afunction of mobility can be reconstructed for every iondetected with sufficient intensities. The data for the 21samples was acquired in 1 day with sample collection timesranging from 1 to 5 min.

Fig. 1 Principal component analysis process for fasting and fed rats.IMMS spectrum showing three highlighted metabolites (M1, M2 andM3) a; highlighted metabolites are converted into a table with m/z, drifttime and intensity where the metabolite intensities are normalized priorto PCA analysis b; the three normalized intensities are drawn in a linegraph to view 1D differneces c; to visualize PCA a 3-D plot is created

from the line graph where M1, M2 and M3 are the x, y and z axis d;principal component 1 accounts for as much of the variability in thedata as possible and the succeeding component account for as much ofthe remaining variability as possible; and the data is projected on PC1and PC2 to form a score plot that accounts for 89 % of the originalvariation for this example of the three metabolites e

Int. J. Ion Mobil. Spec. (2013) 16:177–184 179

Data processing

An example of the workflow of the data processing is shownin Fig. 1. In order to statistically analyze the 2-D IMMS datausing PCA a peak list was generated using a programcreated by Ionwerks that runs on IDL Virtual MachineVersion 6.3 (ITT Visual Information Solutions, Boulder,CO) to obtain m/z, drift time and intensity for each metab-olite feature (threshold was set so only ions with >5 countswere included). For example, in Fig. 1A is an ion mobility-mass spectrum of the sample that was taken 3 h afterfeeding. The mobility drift times, measured in μs, wereplotted as a function of m/z ratio. The inset on the figure

at the upper left hand quadrant demonstrates one of the majoradvantages of IMS when coupled with mass spectrometry;isomer separation. Even though the resolving power for thislow pressure ion mobility spectrometer was low (Rp~20) twomobility resolved peaks at m/z of 337.5 Da can be clearly seenin the insert. The second inset in Fig. 1A shows three differentions with both different masses and mobilities and werehighlighted by red circles and labeled M1, M2, and M3. M1,M2, and M3 are identified based on their m/z and drift time(for example, M1 has a m/z 780.5 and a drift time of463.36 μs) as shown in a table format in Fig. 1B. The nor-malized intensities of the three ions are also shown in Fig. 1Bfor the seven samples measured for one rat. This table

Fig. 2 IMMS plots for thebackground, fasting, 1, 2, 3, 4,5, and 6 h after feeding. Eachfeature represents a metaboliteor background ion detected.M/zis on the x-axis and mobility(μs) is on the y-axis


included individual runs for different fasting and fed treat-ments from the same rat recorded by (m/z)/drift time.

Peaks were aligned based on m/z and drift time usingMerge Table Wizards for Microsoft Excel version 2.0.1.300by AbleBits (Homel, Belarus). Before the PCA analysis thesample intensities were normalized to peak areas, meancentered and scaled to standard deviation. The UnscramblerX by Camo Software Inc. (Woodbridge, NJ) was used tonormalize the intensity by peak area and used for PCA.Missing values were assigned a zero. The scaling and cen-tering was done so all metabolites features will have equalweight in the analyses. To visualize the PCA process, nor-malized intensities for M1, M2 and M3 are shown in line

graphs in Fig. 1C. From the line graphs, a 3-D plot repre-sents the M1, M2 and M3 as x, y and z shown in Fig. 1D(this example was limited to 3 dimensions for illustrationpurposes). A new co-ordinate axis that represents the newdirection of maximum variation through the data is drawn asprincipal component 1 (PC1). So PC1 was the minimumdistance fit to a line in space and the second principalcomponent (PC2) was a minimum distance fit to a line inthe plane perpendicular to PC1. Once the principal compo-nents were calculated, a score plot was generated. Thescores were the projection of the data to a new coordinatesystem. The score plot for PC1 and PC2 are shown inFig. 1E with 89 % of the data explained in the model. From

Fig. 3 IMMS plot zoom regionfrom 340 to 360 Da of a fastingrat metabolite extraction profilerepresented by purple featuresand circles with the black traceand the matrix/backgroundrepresented by green featuresand circles with the dotted redlines. Lines A-L shows the isobaric/isomeric separation ofcompounds in mobility space.With the addition of IMS, matrixions are separated in mobilityspace allowing for the detectionof small metabolite features

Fig. 4 Mass-mobility trendlines (TLs) for carbohydrates,peptides and lipids. Thematrix/background forms a TLand is separated out from themetabolites of interest


the score plot, patterns in the data are easily observed suchas clusters or outliers. For Fig. 1E, 1 and 2 h after feedingcluster together, 3 and 4 h after feeding group together, andfasting, 5, and 6 h after feeding are together with an overallcounterclockwise circular pattern from fasting all the wayaround to 6 h after feeding.

Results/discussion

Sensitivity Two-dimensional MALDI-IMMS metabolomeprofiles for the seven different time intervals are shown inFig. 2. Each feature in the 2-D plot represents background ormetabolite features detected. There are visual differences inthe IMMS plot for different time intervals especially at highmasses (m/z>600 Th). The majority of the metabolite featureswere detected between 501 and 750 Da for all but the fastingsample in which the majority of the peaks were detectedbetween 251 and 500 Da. The MALDI-IMMS was ableto detect 1200 to 3400 metabolite features after masking thebackground and meeting the threshold requirements asdescribed above. It should be noted that not all of the featuresrepresent individual compounds as some may result fromfragmentation or adduct formation (i.e. an ion can form[M+H]+ along with clusters and/or adducts with the matrix).MALDI-IMMS detected 564 reproducible metabolite featuresthat were present in all three fasting samples.

Resolving power The average resolving power for theMALDI-IMMS was ~20. Figure 3 shows an ion mobilitymass spectrum for a limited 20 Dalton region of the spec-trum a sample from a fasting rat metabolome. This expand-ed spectrum was typical of that found for all of the samplesin the low mass region (<500 Da). This expanded areademonstrated the increase in peak capacity when mobilityseparation is added to mass separation. In this spectrumthere were more than 45 isomers/isobars base line separatedover a 20 Dalton range. The ions circled in green were thebackground ions and were present in the spectrum whenonly the matrix was analyzed. At least 24 ions came fromthe metabolome of the fasting rat and 18 of those wouldhave experienced matrix interference if studied by massspectrometry alone. By separating matrix ions from metabo-lite features in mobility space metabolomics using conven-tional MALDI matrices was possible.

Mobility-mass trend lines Woods et al. [20] demonstratedthat compounds with similar structure or in the same chem-ical class travel along a TL, and the matrix/background forma separate TL from peptides, lipids and carbohydrates. In afasting sample shown in Fig. 4, ions of similar chemicaltype fell along well defined TLs (with deviations of ~3 %)when plotted in two-dimensional representations of ion

mobility as a function of m/z. In Fig. 4 there are TLs forcarbohydrates, peptides and lipids in the mass range be-tween 150 and 900 Da. The metabolite identification assign-ments were matched based on m/z and mobility from theliterature under the same conditions [16, 19]. Furtherinvestigation of TLs was conducted based on massalone (m/z<650 Da) and matching metabolites in the HumanMetabolome Database (HMDB) as [M+H]+, [M+Na]+, and[M+K]+ ions only with a mass tolerance of ± 0.25[30]. Withthe addition of IMS to MS metabolites that overlap by m/zwere separated in the mobility space.

Statistical treatment of data After the data was aligned andnormalized, PCA was used on 747 metabolite featuresdetected and the 3-D score plot of PC1, PC2 and PC3 isshown in Fig. 5A. (Note: not all samples had 747 peaks, thefasting sample only had 564 reproducible peaks and the

Fig. 5 3-D principal component analysis score plot for PC1, PC2, andPC3 that explains 65 % of the original data for 747 tentative metabolitepeaks a and the 3-D PC1, PC2, and PC3 loadings plot that demon-strates the metabolites that have the most influence on the PCA modelcircled and labeled with m/z:drift time (μs) b


missing values were assigned a zero). The three PCs explain65 % of the normalized original data. From this 3-D scoreplot, the samples show tight grouping within samples col-lected at the same time of feeding or fasting indicatingexcellent reproducibility of the data from rat to rat. Sampleseparation was achieved in the 3-D score plot for eachsample collection time with the exception of 5 and 6 hintervals overlap. From Fasting to 6 h after feeding a coun-terclockwise circular pattern is observed in 3-D indicatingdistinct differences among the metabolomes after being fed.A similar circular pattern was observed in the ESI-IMMSstudy. Also shown in Fig. 5 is the loadings plot from thePCA analysis (5B). The loadings plot highlights whichmetabolites are “outliers” and contribute the most to thedifferences observed in the samples from the score plot.The metabolite features circled are labeled by m/z:drift timeand are the ones that have the most influence on the model.With the m/z and drift time information the metabolites ofinterest can be tentatively identified if they fit along a trendline. In order to positively identify the metabolites, a stan-dard must be used.

Conclusion

This work is an example of an application where MALDI-IMMS gives comparable results to ESI-IMMS. The inser-tion of an ion mobility spectrometer between a mass spec-trometer and a MALDI source reduces interference frommatrix ions substantially, enabling metabolic profiling ofcomplex biological samples. Although metabolic samplesfor MALDI-IMMS analysis have to undergo severe samplepreparation condition (extraction, evaporation, matrix addi-tion, insertion into a vacuum, etc.) rat to rat reproducibilitywas excellent and MALDI-IMMS could follow a changingmetabolome as it moved from a fasting condition to a fedcondition and back to a fasting condition. Trend lines,obtained from ion mobility—mass spectra serve as an effec-tive tool for qualitative information that aids mass for com-pound identification. Trend lines for carbohydrates, peptides,lipids, and matrix were reported.

Acknowledgements Authors would like to acknowledge AndreaChoiniere for her help in peak alignment with the data. This projectwas supported in part by a research grant from Department of Healthand Human Service: Public Health Services organization (Road MapGrant No. R21 DK070274). We would like to acknowledge thework performed by the Cincinnati Mouse Metabolic PhenotypeCenter support by DK 59630. Ionwerk’s J. Albert Schultz andThomas Egan for instrumental and software help, NIDA contract #N44DA-3-7727and HHSN271200677593C, HHSN271200677563Cfor building the instrument used in this work, and National Instituteon Drug Abuse Intramural program for funding to A.S. Woodslaboratory.

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monitoring dynamic changes in lymph metabolome of fasting and fed rats by matrix-assisted laser...

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