26 | Mass Spectrometry ADVERTISING SUPPLEMENT THE APPLICATION NOTEBOOK – MARCH 2006

Principle Component Analysis of UrineSamples Based upon ESI-TOF-MS Data

Gabriela Zurek, Birgit Schneider and Carsten Baessmann, BrukerDaltonik GmbH

The workflow for metabolic profiling of urine sam-ples is described: Urine samples are subjected to afast chromatographic separation, the LC–MS dataare processed using principle component analysis(PCA) in ProfileAnalysis. The statistical result is thecriterion to find compounds relevant for the discrim-ination of sample groups and identify those com-pounds according to their molecular formula.

M etabolomics or metabolic profiling comprises the com-prehensive analysis of low-molecular weight metabolites

in biological samples using information rich analytical tech-niques (LC–MS, GC–MS, and NMR) combined with patternrecognition for data evaluation. The target of metabolomics is toidentify small molecule biomarkers and changes in metabolicpathways characteristic for a certain state of an organism (e.g.,disease, drug treatment, toxic response). Measuring metabolitesin urine is of special interest because metabolic endpoints can bemonitored and urine samples can be obtained non-invasivelyfrom animals and humans.

We describe a method for the analysis of urine samples byESI-TOF-MS combined with principle component analysis(PCA). ESI-TOF-MS data represents an indispensable tool forthe identification of compounds based on accurate mass dataand the true isotopic pattern. A typical metabolic profilingworkflow is outlined in Figure 1.

ExperimentalUrine samples. Human urine samples were centrifuged anddiluted 1/1 (v/v) with water or spiked with1. phenylalanine (A: 36; B: 89; C: 180 ng/�L);2. homovanillic acid (D: 29; E: 58; F: 116 ng/�L HVA) and

vanilmandelic acid (D: 22; E: 43; F: 87 ng/�L MVA).

LC–MS measurements. LC-MS measurements were performedusing a micrOTOF ESI-TOF mass spectrometer (BrukerDaltonik, Bremen, Germany) coupled to an Agilent 1100HPLC system consisting of binary pump, well-plate autosam-pler, column oven at 35 °C and UV detector at 254 nm (Agilent,Waldbronn, Germany). The separation was carried out with abinary acetonitrile-water gradient with 0.2% formic acid asmodifier and a reversed phase column with polar embeddedgroups (Synergi Fusion RP, 50 � 2 mm, 4 �m particles, 80 Å;Phenomenex, Aschaffenburg, Germany) at a flow rate of 0.5mL/min. The flow was split 1:20 into mass spectrometer andUV detector. Samples were measured in electrospray positiveand negative mode in a scan range from 50–800 m/z. Sodiumformiate solution was injected as external calibration standard inthe void volume of each chromatographic run.

Data evaluation: Data evaluation was performed withProfileAnalysis for PCA and generate molecular formula (GMF)in DataAnalysis (Bruker Daltonik, Bremen). The LC–MS datawas prepared for PCA in ProfileAnalysis using a bucketingapproach of the raw data. The LC–MS was integrated from0.5–11.5 min and 50.5–330.5 m/z in time- and m/z-buckets of1 min and 1 m/z. The data was normalized to the total intensityin an analysis.

ResultsHuman urine samples were spiked at different concentrations ofphenylalanine and homovanillic and vanilmandelic acid to simu-late inborn errors of metabolism. The sample preparation is keptminimal in order to avoid loss of any metabolites. The selectedstationary phase for LC separation allowed good separation of thevery polar urine components. A human urine profile acquiredwith ESI in the negative mode is presented in Figure 2.

The result of the PCA for positive and negative mode was thenevaluated. The target of PCA is the visualization of the variancewithin a set of samples. The LC–MS data is prepared for the sta-

Figure 1: Workflow for metabolic profiling.

Figure 2: LC–MS chromatogram of urine sample (ESI/negativemode) and MS spectrum of phenylalanine.

THE APPLICATION NOTEBOOK – MARCH 2006

tistical analysis by generating a so-called variable or bucket table.The buckets represent pairs of retention time and m/z values. Theintensities of these buckets are listed in the bucket table for allanalyses. PCA then calculates the covariance matrix of the buckettable. The principle components (PC) are the result of the calcu-lation. The PCs are ranked according to the variance they explain.In our example, spiked samples of different concentrations werefirst used for method optimization and demonstration purposes.

In the PCA scores plots (PC1 versus PC2), the separation ofgroups according to the spikes (Black: no spike; A–C: phenylala-nine; D–F: MVA/HVA) is clearly observed in the ESI negativedata (Figure 3). The loadings plot indicates the masses andretention time values responsible for this separation. Loadingsvalues located far away from the centre have a high contributionto the variance in the data. The directions of loadings values cor-respond to separation of groups in the scores plots. The largecluster of loading values centered at zero does not contribute tothe variance in the data.

The black group represents the original urine samples. Theloading 164.07 m/z at 2.9 min is characteristic for group A–Cbeing the phenylalanine spike. The loading values 196.93 m/z at3.5 min and 181.94 m/z at 8.8 min is characteristic for groupD–F being the MVA/HVA spike. All different spiking concen-trations are also clearly separated from each other indicating thatPCA is indeed quantitative.

Characteristic mass spectra of the groups then are subjected toGMF to identify their elemental composition. GMF evaluatesthe formula suggestion using both accurate mass and isotopicpattern information. To improve confidence in the results, themeasured isotopic pattern is compared with the theoretical one,generated from the suggested elemental composition (sigmavalue). The smaller the sigma value the better the fit. The insetin Figure 2 shows a mass spectrum of phenylalanine from urine:The upper spectrum is the measured one, while the simulationof the spectrum after GMF is depicted below.

The result of GMF of phenylalanine from urine is shown inFigure 4. If no values are entered in the Min/Max section for theelements, a CHNO distribution is assumed and the carbon num-

ber can be estimated from the (A�1)/A ratio. Based on the meas-ured m/z value, the possible formulae are calculated taking notonly mass accuracy but also the complete isotopic pattern intoaccount. The formula of phenylalanine is the first hit within asearch range of 4 mDa for mass accuracy. The measured masserror is –2.2 ppm and the sigma value 0.0004. All other formulasuggestions are not reasonable due to higher mass errors, wrongelectron configuration or not matching isotopic pattern.

ConclusionThe present example study shows that ESI-TOF-MS data is wellsuited for analysis with multivariate statistics like PCA. A pre-requisite is a sensitive and robust analytical method develop-ment. An example has been given how the relevant informationcan be extracted from PCA scores and loadings plots and howthe accurate mass information is then used for identification viathe elemental composition.

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Figure 3: PCA scores and loadings plot of spiked urine samples PC1 versus PC2.

Figure 4: Generate molecular formula dialogue for phenylalanine.