MS

Journal of Chromatography B, 934 (2013) 79–88

Contents lists available at ScienceDirect

Journal of Chromatography B journal homepage: www.elsevier.com/locate/chromb

Analysis of chloroformate-derivatised amino acids, dipeptides and polyamines by LC–MS/MS Baljit K. Ubhi a,∗,1 , Peter W. Davenport a,1 , Martin Welch a , John Riley b , Julian L. Griffin a,c , Susan C. Connor a a

Department of Biochemistry and The Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK Clinical Respiratory MDC, GlaxoSmithKline, Stockley Park, Uxbridge, UK c MRC Human Nutrition Research Centre, Cambridge, UK b

a r t i c l e

i n f o

Article history: Received 25 March 2013 Accepted 22 June 2013 Available online 29 June 2013 Keywords: LC–MS/MS Amino acids Metabolites Metabolomics

a b s t r a c t A liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was developed which, with sample preparation using a commercially available kit, allows rapid quantitation of 39 chloroformatederivatised amino acids (AAs), polyamines (PAs) and dipeptides (DPs) in complex biological matrices. Lower limits of quantitation (LOQ) were 20–150 nM for putrescine, spermine, spermidine, cadaverine, agmatine, and below 5 ␮M for all analytes. Responses were linear for all analytes between 0.5 and 50 ␮M. Quantitative measurements of all 39 metabolites were achieved within a 15 min runtime. The method was evaluated with a Pseudomonas aeruginosa cell extract study (n = 24) and a larger human urine study (n = 308). Batch effects were observed in the urine study and an investigation of instrument and sample stability showed a wave-like pattern in the MS responses. Both the run order and interbatch variation were successfully corrected by normalising to pooled urine quality control data. Thus, this method should be suitable for diverse biological matrices and for large as well as small sample sets. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Metabolomics studies typically apply either proton nuclear magnetic resonance spectroscopy (1 H NMR) and/or mass spectrometry (MS) to the measurement of low molecular weight metabolites (molecular weight < 1000 Da) in biological fluids or tissue extracts. However, the diverse chemical properties and large range of concentrations of metabolites mean that it is not possible to characterise a complete metabolome with one analytical platform. Hence analytical platforms are required that can comprehensively characterise subsets of metabolites, preferably with high-throughput and compatibility with diverse biological matrices. Amino acids (AAs), polyamines (PAs) and dipeptides (DPs) are key components of many biological processes and their metabolism are tightly integrated, so a method for their simultaneous quantitation is highly desirable to probe a range of biological processes. Many techniques have been developed for analysis of AAs, PAs and DPs, based on separation techniques such as TLC, LC, GC,

∗ Corresponding author. E-mail address: [email protected] (B.K. Ubhi). 1 Joint first authors. 1570-0232/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jchromb.2013.06.026

with various chemical derivatisations [1–4]. However, methods dependent upon chromatographic separation for discrimination of analytes often require challenging optimisation, especially when applied to complex matrices [4]. Use of MS in tandem with chromatographic separation can improve sensitivity and specificity: for example, methods have been developed to quantitate AAs [5,6] or PAs [2,7] separately, or basic AAs and PAs simultaneously [8]. Various commercial kits are available to meet demand for convenient, robust, high-throughput bioanalysis of AAs and related metabolites (for example, Phenomenex EZ:faast, Waters Masstrak, AB Sciex aTRAQ, Perkin Elmer neogram AAAC kit). The EZ:faast kit (Phenomenex Inc, Torrence, CA, USA) comprises solid-phase extraction and chloroformate derivatisation and has been used to prepare a wide range of AAs, DPs and related metabolites for quantitation using LC–MS and GC–MS [5,6]. We have extended this method to analyse PAs and agmatine (a compound central to PA metabolism) using a liquid chromatography tandem–mass spectrometry (LC-MS/MS) assay based on EZ:faast sample preparation to quantitate AAs, DPs, PAs. Utilising a short sample pretreatment step with fast derivatisation times of 1 min and mass spectrometric detection (for enhanced selectivity and specificity) this approach can analyse AAs, DPs and PAs together in a 15 min runtime.

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We evaluated the method’s performance with cell extracts of the human pathogenic gammaproteobacterium Pseudomonas aeruginosa, as well as human urine. Previous experiments had implicated AA and PA metabolism in a P. aeruginosa virulence switch controlled by an intercellular signalling mechanism called quorum sensing (QS) [9]: we validated the method for the study of AA and PA metabolism by studying the effects of inactivating P. aeruginosa QS signal generation. We further evaluated the method on a large set of human urine samples from the ECLIPSE project which aims to identify biomarkers from patients with chronic obstructive pulmonary disease (COPD) [10]. It is often necessary to split data acquisition of large datasets into discrete batches in metabolomics experiments to maintain sensitivity [11,12]. Long data acquisition runtimes can cause the mass spectrometer source to become unclean; separating samples into batches allows the user to clean the source between each batch. Batch effects are frequently observed in LC/MS data, typically as a result of sample or MS instrument instability [13,14]. Run order and batch effects were observed for the larger human urine sample set (n = 308), and we have assessed several normalisation methods to mitigate batch effects. In summary, the presented method is capable of rapid, simultaneous quantification of all common AAs, PAs and DPs in diverse biological matrices, and is suitable for both small and large sample sets. 2. Experimental 2.1. Chemicals HPLC grade methanol was purchased from Fisher (Fisher Scientific GmbH, Ulm, Germany) and ammonium formate was purchased from Sigma–Aldrich (MO, USA). The Phenomenex EZ:faast LC–MS kit (Phenomenex Inc, Torrence, CA, USA) was used for the preparation of samples for amino acid analysis. 2.2. Sample preparation 2.2.1. Bacterial strains The PAO1 wild-type P. aeruginosa strain ATCC 15692 was supplied by John S. Mattick (Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia). Allelic-exchange mutations were performed as described in [15], yielding a lasIrhlI mutant with a Gentamycin resistance cartridge inserted into a unique EcoRI site of lasI and a Tc cartridge inserted into unique EcoRI site of rhlI. P. aeruginosa liquid cultures: Five millilitres AGSY growth medium (56 mM alanine, 17 mM K2 HPO4 , 86 mM NaCl, 100 ␮M CaCl2 , 10 mM MgSO4 , 5 ␮M FeCl2 , 7.5 ␮M ZnCl2 , 0.5% [vol/vol] glycerol, 3 g/l yeast extract, pH 7; [16]) in a sealed 25 mL test tube was inoculated from a single colony and grown overnight with vigorous shaking at 37 ◦ C. The cells were pelleted by centrifugation on the following morning, washed with 5 mL of growth medium, pelleted, re-suspended in 2 mL growth medium before being inoculated into 50 mL of fresh medium to a final optical density at 600 nm (OD600 ) of 0.05. These 50 mL cultures were then incubated in 500 mL conical flasks with vigorous shaking (300 rpm) at 37 ◦ C. A volume of liquid bacterial culture equivalent to 2.9 × 1010 viable cells (e.g. 15 mL at an optical density of 1.5 in AGSY medium) was pelleted by centrifugation (4000 × g, 15 min, 4 ◦ C), washed with 10 mL 0.9% NaCl, pelleted again by centrifugation (4000 × g, 15 min, 4 ◦ C), the supernatant discarded carefully and the resulting pellet frozen on liquid N2 . Metabolites were extracted using a modified version of the methanol–chloroform extraction [17]. In brief, frozen pellets were

re-suspended in methanol–chloroform (2:1, 900 ␮L) and cells were further disrupted by tip-sonication (1 min total in 2.5 s bursts, on ice). A chloroform:water (1:1, 600 ␮L) mixture was added. The samples were mixed by vortexing then pelleted by centrifugation (6000 × g, 20 min). The aqueous (top) and organic (bottom) fractions were separated and dried overnight in an evacuated centrifuge (Eppendorf, Hamburg, Germany). EZ:faast amino acid analysis sample procedure for urine: This kit produced by Phenomenex (Macclesfield, UK) consists of a solid phase extraction step followed by derivatisation of the extracted amino acids. This is followed by a liquid/liquid extraction, with the organic layer removed and dried under N2 . Aliquots of 50 ␮l of urine were prepared in duplicate with the addition of 100 ␮l of the internal standard, Reagent 1, which was diluted 1:100 with phosphate buffered saline (PBS). The internal standards included homoarginine (HARG), methionine-D3 (MET-D3 ) and homophenylalanine (HPHE), three components not naturally found in the matrices under investigation. The dried down organic extracts were reconstituted in 100 ␮L of a 1:2 (v/v) 10 mM ammonium formate in water:10 mM ammonium formate in methanol solution. 2.3. Liquid chromatography Amino acids were separated using a Waters Acquity Ultra Performance Liquid Chromatography (UPLC) system (Waters, Atlas Park, Manchester, UK). Sample aliquots of 5 ␮L were injected onto a Phenomenex EZ:faast AAA-MS column (250 mm × 2.0 mm) held at 25 ◦ C. The eluents were of 1 mM ammonium formate (Buffer A) and 10 mM ammonium formate in methanol (Buffer B). The amino acids were eluted using the following gradient: 0–11 min from 68 to 83% buffer B, 11–11.01 min 83 to 68% buffer B and 11.01–13.00 68% buffer B. A system re-equilibration was performed for 2 min prior to each injection. A constant eluent flow of 0.25 mL/min was used throughout the analysis. After each injection a strong needle wash (83:17 methanol:water) and a weak needle wash (10:90 methanol:water) cycle were used to eliminate carry-over. Amino acid standards in a mixture were used to create a 5-point calibration curve and run at the beginning and end of the analysis. A quality control (QC) sample was run every ten injections to allow the assessment of the system reproducibility. The QC sample consisted of the three internal standards, HARG, MET-D3 and HPHE, diluted 1:100. A pooled QC sample was created by taking 10 ␮l of each sample and prepared in the same way as a real sample, and also injected after every block of ten samples. The urine dataset consisted of a large number of samples (n = 308) which were acquired in three separate batches. Samples were randomised by generating the “=(rand)” formula in Excel. They were prepared over seven days, freeze dried, stored frozen (−80 ◦ C) and randomised again prior to acquisition. For the P. aeruginosa dataset (n = 24), samples were randomised and processed as a single batch over 36 h. 2.3.1. Stability experiment for exploration of batch effects Each urine sample was prepared as four aliquots to ensure enough sample volume to allow monitoring over several days. A pooled urine QC and a QC mixture (HARG, MET-D3 and HPHE as detailed above) were prepared. Ten AA standard mixtures containing 27 amino acids at 2 nmol (denoted SD1 from the EZ:faast kit) were prepared and the dried extracts stored at −80 ◦ C. Each individual AA standard mix was freshly reconstituted before analysis and injected three times (i.e. once every block of 6 urine samples, with sample change after 3 blocks). This sample was deemed a stable reference for measurement of time-course stability. Five pooled QC’s were prepared and stored until analysis; each pooled QC was reconstituted at time of analysis and injected six times (30 injections in total; once in every six urine sample block). Each sample aliquot was injected seven times, so that each sample was injected a total

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Fig. 1. (A) Total ion chromatogram, a = function 1, b = function 2 and c = function 3. (B) and (C) XICs of polyamines and of amino acids, respectively from a standard mixture of AAs, DPs and PAs. (D) TIC and XICs of 1- and 3-methylhistidine.

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Table 1 Parent and product ions used for MS optimisation for AAs, DPs and PAs and quantitation of AAs, DPs and PAs using HPHE as an internal standard. Amino acids/dipeptides/polyamines

Abbreviated name

Parent ion (m/z)

Product ion (m/z)

LOD (nM)

R2

Arginine Glutamine Citrulline Serine Asparagine 4-Hydroxyproline 3-Methylhistidine 1-Methylhistidine Glycine Glycine-proline Threonine Alanine Hydroxylysine ␥-Aminoisobutyric acid Sarcosine ␤-Aminoisobutyric acid ␣-Aminobutyric acid Ornithine Methionine Proline Lysine Aspartic acid Histidine Thiaproline Valine Glutamic acid Tryptophan Aminoadipic acid Leucine Phenylalanine Isoleucine Aminopimelic acid Cystathionine Cystine Tyrosine Agmatine Cadaverine Putrescine Spermine Spermidine

ARG GLN CIT SER ASN HYP 3MIS 1MIS GLY GPR THR ALA HLY GABA SAR ␤AIBA ABA ORN MET PRO LYS ASP HIS TPR VAL GLA TRP AAA LEU PHE ILE APA CTH C-C TYR AGM CAD PUT SPM SPD

303.23 275.27 304.80 234.12 243.23 260.02 298.11 298.11 204.21 301.22 248.16 217.98 262.19 232.02 217.98 232.02 232.02 347.20 278.10 244.20 361.21 304.08 370.16 262.19 256.13 318.25 333.25 332.11 260.70 294.18 260.70 346.20 479.03 497.00 396.17 303.44 275.45 261.39 461.53 461.52

156.04 172.10 287.10 146.32 157.06 171.97 214.06 210.08 171.83 240.84 188.00 130.40 174.26 172.03 115.96 130.00 144.10 287.24 189.84 156.13 170.07 216.21 195.91 174.26 115.94 172.16 245.02 244.48 172.07 206.16 129.91 286.06 229.20 248.02 308.15 243.31 215.25 201.21 155.31 284.29

11.2 13 3.4 467.3 65.1 74.7 38.8 1.9 133.5 4.9 210.8 21.9 13.7 5.7 15.6 2.1 4.2 3.9 39.9 70.7 10.3 124.4 1.7 216.5 330.5 62.9 1.6 9.9 0.8 16.4 11.2 0.6 0.1 0.2 6.2 15 8.9 8.9 0.8 0.2

0.9889 0.9871 0.9902 0.9962 0.9878 0.8997 0.9928 0.9981 0.9863 0.9980 0.9837 0.9832 0.9964 0.9973 0.9865 0.9948 0.9832 0.9956 0.9931 0.9803 0.9922 0.9894 0.9853 0.9721 0.9879 0.9950 0.9969 0.9920 0.9834 0.9936 0.9812 0.9978 0.9977 0.9944 0.9946 0.9868 0.9959 0.9975 0.9961 0.9984

of 28 times over the four day data acquisition process. The experimental design therefore comprised 10 blanks followed by the five point calibration curve, a blank and then the following sequence repeated 28 times through the run: pooled QC, QC, AA standard mix, blank, six samples, blank. Blank injections were made to eliminate any carryover and contaminates during the analysis. This concept has been taken from published works by Dunn et al. [13]. 2.4. Mass spectrometry Mass spectrometric data were collected using a Waters Quattro Premier XE Triple Quadrupole mass spectrometer equipped with an electrospray source used in positive ionisation mode (Waters, Manchester, UK). The source temperature was set to 120 ◦ C with a cone gas flow of 50 l/h, a desolvation temperature of 350 ◦ C and a desolvation gas flow of 700 l/h. A capillary voltage of 1000 V was applied. The optimal MS tuning conditions (e.g. cone voltage) for each analyte derivative m/z are usually different, requiring individual tuning in positive ionisation mode. The parent mass was detected to two decimal places and fragmented using a collision gas at optimised collision energy. The resulting daughter ions were then observed using a daughter scan experiment. At least one unique daughter ion was selected for quantification of each amino acid and another as a qualifying ion to confirm identification. A multiple reaction monitoring (MRM) method was then created using specific transitions for each analyte and its unique fragment(s) to allow quantification for all of the analytes in one method. A scan

time of 0.05 s with an inter-scan delay of 0.005 s was used for all the analyses. For each amino acid to be considered quantitative at least 15 data points across the peak were required. Analytes detected by MS were compared with authentic standards for confirmation. 2.5. LC–MS data processing Raw data were processed using Waters QuanLynx software version 4.1 (Waters, Milford, MA, USA). QuanLynx was employed to quantitate the data using the internal standards and amino acid concentrations set out in the calibration curves. An integration window extent parameter of 10 was applied. QuanLynx output includes the retention time and area under each peak for each detected metabolite. 2.6. Normalisation For P. aeruginosa samples, responses were calculated relative to the internal standard HPHE. Concentrations were calculated from calibration curves of each amino acid. For urine normalisation, large variation in urine output/dilution per individual is common resulting in highly variable analyte concentrations. Several accepted methods for scaling exist and four of these have been evaluated on the ECLIPSE urine dataset; these included scaling to total amino acid area (TA), internal amino acid standards (IS), external creatinine measurements, (Cre) and a combination of total area and creatinine (TA Cre). For this comparison HARG was used as the internal standard for the elution of arginine and citrulline. MET-D3 was used

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as the internal standard for the elution of glutamine, citrulline, serine, asparagine, proline-hydroxyproline, 4-hydroxyproline, 3methylhistidine, 1-methylhistidine, glycine, glycine-proline, threonine, alanine, hydroxylysine, ␣-aminobutyric acid, sarcosine, aminoisobutyric acid, ␥-aminobutyric acid, ornithine, methionine, proline, lysine, aspartic acid, histidine, thiaproline, valine, glutamic acid, tryptophan and aminoadipic acid. HPHE was used as an internal standard for the elution of leucine, phenylalanine, isoleucine, aminopimelic acid, cystathionine, cystine and tyrosine. 2.7. Statistical data analysis Samples were prepared in duplicate where possible and the means of these duplicates were used for statistical analysis (SimcaP+ v12.0.1; Excel 2007; Rv2.11.0). For P. aeruginosa cell extracts: (1) the following analytes were excluded from analysis because their concentrations were below the LOQ in extracts: SPM, ASP, HYP, 3MHIS, 1MHIS, AAA, -AIB and APA; (2) ALA was excluded from analysis because, despite two washes of cells, there was still relatively high carryover from the growth medium; and (3) levels of analytes are reported relative to the total concentration of all analytes. Prior to multivariate statistical analyses, data were mean-centred by subtracting the variable mean from each data point. Unit variance scaling was applied by dividing each variable by its standard deviation. This resulted in a constant data dispersion of each variable (standard deviation = 1). Principal components analysis (PCA) was applied to look for patterns in the data and check for instrumental and biological outliers. Unpaired Student’s t tests (with no equal variance assumption) were used to calculate statistical significance; Benjamini–Hochberg correction, ˛ = 0.1, was applied to control the false discovery rate [18].

3. Results and discussion

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Table 2 Batch 1 and batch 2 data mean, standard deviation (s.d.) and % batch difference values in the concentrations of the pooled QC sample.

1MHIS 3MHIS AAA ABA ALA ARG ASN ␤AIBA C-C CIT CTH GABA GLA GLN GLY GPR HARG HIS HLY HPHE HYP ILE LEU LYS MET MET-D3 ORN PHE PRO SAR SER THR TPR TRP TYR VAL

Concentration batch 1

Batch 2

Mean

s.d.

Mean

s.d.

% diff

18.01 27.14 1.43 0.60 7.42 0.97 3.91 8.86 3.42 0.26 10.84 0.25 0.79 15.70 188.67 0.89 0.05 29.31 0.01 0.05 0.37 0.60 1.08 7.57 0.74 0.12 0.83 3.11 0.50 0.04 10.86 11.55 2.23 2.86 5.64 2.05

0.97 1.46 0.09 0.04 1.22 0.05 0.24 0.65 0.19 0.03 1.60 0.05 0.04 0.39 24.96 0.27 0.00 2.97 0.01 0.00 0.03 0.16 0.21 0.38 0.19 0.04 0.04 0.19 0.08 0.01 0.92 2.15 0.59 0.23 0.21 1.05

34.03 52.44 1.85 0.70 37.42 2.16 6.76 13.01 4.82 0.43 31.92 0.44 1.27 23.37 143.87 0.47 0.08 31.41 0.02 0.04 0.12 0.99 1.43 13.20 2.89 0.02 2.03 4.30 0.98 0.10 36.62 9.24 15.40 3.95 15.07 1.72

1.35 3.44 0.13 0.03 19.58 0.07 1.24 1.14 0.14 0.02 9.53 0.06 0.08 3.54 12.95 0.12 0.01 1.15 0.01 0.00 0.01 0.29 0.36 1.41 0.58 0.01 0.15 0.51 0.14 0.01 12.60 2.28 3.01 0.35 3.26 0.61

52.72 51.75 77.31 85.50 19.83 44.74 57.82 68.06 71.04 61.36 33.95 55.36 61.99 67.19 131.14 187.15 58.34 93.32 54.48 107.95 315.63 60.49 75.75 57.36 25.65 567.27 41.00 72.45 51.62 44.52 29.65 125.08 14.46 72.43 37.45 119.37

3.1. Method development 3.1.1. Optimisation of HPLC and MS detection The EZ:faast chromatographic method was shortened by reducing both the run time from 21 to 15 min and the equilibration time from 4–7 min to 2 min with no loss of resolution or sensitivity. The internal standard (HARG, MET-D3 and HPHE) concentrations provided with the EZ:faast kit were too high, saturating the MS detector. These were therefore diluted 100-fold to reduce their responses to a level comparable to analytes in the biological matrices. Optimisation of the MS method for AA, DP and PA derivatives resulted in the parent and product ions shown in Table 1. Structural isomers with the same parent mass, such as leucine and isoleucine, were distinguished using different product masses and/or resolved by liquid chromatography. The resolution and sensitivity of the method was improved by acquiring the data in three time segments to increase the dwell time available per transition and optimising the dwell time for each transition to ensure more than 15 data points across each chromatographic peak. From the total ion chromatogram (TIC), extracted ion chromatograms (XICs) were generated for each analyte derivative. The TIC in Fig. 1 shows the XICs of the PAs (spermidine, spermine, cadaverine and putrescine), agmatine and selected AAs measured across the three functions. 3.1.2. Optimal biological sample volume To assess the suitability of the calibration curves to quantify each analyte in control human urine, 100 ␮l, 50 ␮l, 25 ␮l and 10 ␮l aliquots of the same urine sample were measured using the above method. The optimal volume was 50 ␮l. The optimal volume of 350 ␮l for bacterial cell extracts was determined in a similar way

by serial dilution and comparison of amino acid ranges with the standard curves. 3.2. Method evaluation The reproducibility and linearity of the assay was determined using duplicate sampling and single injections over a 1 pmol–10 nmol range. Relative standard deviations (RSDs) for retention times and responses for the assay were less than 1.5% and 15%, respectively, for each analyte. The linearity of the assay was established across the entire concentration range tested (0.2 pmol–2 nmol). The limit of detection (LOD) for each analyte was defined as three times the noise level for each analyte (Table 2) and calculated as the mean plus three times the standard deviation (SD) of signal in the QC samples. The limit of quantitation (LOQ) was defined as ten times the LOD. Analytes below the LOQ were excluded from the analysis. These criteria are in accordance with the FDA requirements for quantitative biochemical assays [19]. Using both the retention time and specific ion transition information it was possible to specifically measure these analytes (Table 1) in diverse biological matrices. The method was then evaluated for urine and bacterial cell extracts. 3.2.1. Metabolic profiling of bacterial cell extracts The application of the method to bacterial cell extracts was evaluated using samples from a study designed to explore the relationship between quorum sensing and metabolism in P. aeruginosa. Unpublished work by our groups suggests that quorum sensing by P. aeruginosa perturbs amino acid and polyamine

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Fig. 2. Principal component analysis (PCA) of AA and PA data for wild type and inter-cellular signalling mutant (lasIrhlI) P. aeruginosa. (A) Scores plot showing the divergence between wild type and mutant between exponential (OD1.5) and stationary phase (OD 4.5); (B) corresponding 2D loadings plot of PC1 versus PC2; (C) 1D bar plot showing loadings for PC1 and PC2 and the statistical significance of the corresponding analytes. Bar heights represent loadings, asterisks (*Benjamini–Hochberg-corrected p value < 0.05, **<0.01) represent t tests comparing wild type exponential versus stationary phase (above PC1) and wild type versus mutant at stationary phase (above PC2).

metabolism. Strains with inactivated lasI and rhlI genes are unable to synthesise the quorum sensing signal molecules N-(3oxododecanoyl)-homoserine lactone and N-butyryl-homoserine lactone, respectively. Cell extracts were prepared from wild-type (PAO1) and lasIrhlI mutant strains harvested at late log (OD600 1.5) and early stationary (OD600 4.5) phases (n = 6 per group). PCA (Fig. 2A) showed that QS effects (PC2) could be separated from growth-phase effects (PC1) based on their AA and PA profiles (Fig. 2B and C). 3.2.2. Metabolic profiling of human urine The developed method was applied to urine from COPD patients from ECLIPSE (n = 308 acquired in duplicate). Data from the ECLIPSE study were acquired in three batches of 2.5 days duration per batch. PCA revealed large batch effects (Fig. 3A) with 14–568% mean differences of batch 2 relative to batch 1 for each analyte so that only very large group differences could be detected (Table 2). These two batches were only initially compared as their sample numbers were of comparable size. Previous experience with this method had confirmed that batch-to-batch variation was a problem and the variation between batches was larger than within-group sampleto-sample variation. 3.2.3. Investigation of batch and run order effects To evaluate the relative contributions of sample versus instrument instability, six urine samples were each injected 28 times over four days (the maximum stability period specified by Phenomenex). Pooled urine QC samples and analyte standards were interspersed with the six urine samples. The PCA scores plot (Fig. 4A) shows the six samples analysed (including the multiple aliquots), AA standards, pooled QCs and the QCs. The QCs (purple) are positioned to the far left of the plot, in a small tight cluster signifying that these results were less variable than the samples.

The amino acid standard (green) can be seen drifting from the top left towards the top middle of the plot. The pooled QCs (black) are nested tightly in the centre of the samples, representing the total sample population (Fig. 4C). Analysis of the samples revealed that the variation for each sample across time was substantially less than the inter-sample variation and that there was a reversible time-related response for each sample (Fig. 4B). The response time-course of each analyte in the samples, the pooled urine QC samples and pure analyte standards showed similar wave-like profiles (Fig. 5). This indicated that the derivatives were stable and not degrading. If the latter were occurring, a constant response dropoff would have been visualised with no recovery. The variation in this case must therefore be due to instrumentation performance/instability. By normalising to the pooled QC responses it was possible to correct for this instrument instability (Figs. 5 and 6A and B). Rather than normalising to the total pooled QC, as has been previously been reported [13,14,20–22], a moving average of the pooled QC was applied so that only QC values acquired close in time were applied to each sample. This was achieved using two different approaches; firstly by taking an average of the QC acquired either side of the block of samples and secondly by fitting LOESS local regression curves (using the R version 2.11.0 stats::loess function) to the wave of pooled QC responses. The former was more successful, and was used for the remainder of analyses in this publication; LOESS curve fitting performed poorly at the beginning and ends of batches. The batch effects in the ECLIPSE urine dataset were also corrected by normalisation to the pooled QC responses (Fig. 3). 3.3. Scaling techniques Different scaling methods using internal standards (IS), creatinine (Cre), total area (TA) and a combination of total area and creatinine (TA Cre) were investigated to determine which method

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Fig. 3. (A) Principal components analysis scores plot pre-normalisation highlighting the batch effect (R2 = 45.1% and Q2 = 33.1%). (B) PCA scores plot after batch correction to the pooled QC highlighting the merging of the three batches (R2 = 47.7% and Q2 = 37.5%). Key: batch 1 (䊉), batch 2 ( ), batch 3 ( ).

was better for scaling the data. Creatinine and total area methods have the added advantage of adjusting for differences in urine concentrations (Fig. 7); large variation in urine output per individual is common, resulting in highly variable analyte concentrations. The overall dataset variation was assessed after application of each

scaling method using PCA (Fig. 7A). The most tightly clustered data in PC1 were the TA-normalised data, with the IS-scaled data showing the most variation, possibly arising from urine volume variation. Fig. 7D describes the variation according to batch; the most batch variation was observed for the TA data, with two distinct

Fig. 4. Stability experiment data. (A) PCA scores plot of all the data. Notice the QC clustered tightly out in the far left (purple), R2 = 65.2%, Q2 = 61.1%. (B) PCA scores plot of all six samples and their repeated injections. A pattern exists according to run order, where the injections at the start of the run start out on the right, decreasing in signal (towards the left) and then increase again towards the start injections (a looping pattern), R2 = 68.9%, Q2 = 63.3%. (C) PCA scores plot displaying six samples and pooled QC. Notice the pooled QC nested in the centre of the scores plot, R2 = 68.9%, Q2 = 63.3%. (D) Loading plots highlighting metabolites responsible for the group separations in C.

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Fig. 5. Concentrations of arginine, phenylalanine, ornithine and GABA in sample 1, pooled QC and in sample 1 after normalisation to the pooled QC over the four day data acquisition.

Fig. 6. (A) PCA scores plot pre-normalisation highlighting the six samples and the pooled QC in the middle (䊉). R2 = 79.4% and Q2 = 59.2%. (B) PCA scores plot after normalisation to the pooled QC. R2 = 87.7% and Q2 = 75.2%.

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Fig. 7. Application of different normalisation method of urine data. (A) Highlighting the four different normalisation methods applied to the ECLIPSE urine dataset. R2 = 93.7% and Q2 = 93.2%. (B) Displaying the datasets normalised to Cre and TA Cre, highlighting that the two sets of data overlay meaning there are no differences when scaling further to total area. Highlighted (red circles) samples show duplicate injections (which lie in the same area of the scores plot). R2 = 54.6% and Q2 = 46.8%. (C) Two-dimensional PCA plot of (A). R2 = 94.9% and Q2 = 92.7% (second component not significant). (D) Same as plot (C) but coloured by batch. The batch effect is corrected more so in the Cre and TA Cre datasets (batch 1 䊉, batch 2 and batch 3 ). All data shown are unaveraged and include sample duplicates) normalised to the pooled QC.

clusters formed in PC 2. The TA Cre-scaled and Cre-scaled datasets were the most promising in terms of minimising variation and the observed batch effects. 4. Conclusions A rapid, reliable, high-throughput method was developed to simultaneously measure AAs, PAs and DPs in complex biological samples with a large dynamic range. The method was easily adapted to diverse biological matrices, requiring little optimisation. Batch to batch variation was observed, due to variability in instrument performance, but this could be adjusted using the pooled urine QC standard profiles. Detection limits were in the nM range for each analyte and responses were linear across the 0.5–50 ␮M range tested. Previous studies have demonstrated high sensitivity analysis of chloroformate derivatised AAs and DPs using LC–MS/MS; the current study demonstrates that this high sensitivity method can be extended to simultaneous analysis of PAs, AAs and DPs, using a shorter separation time of 15.5 min [5]. This study also draws attention to the importance of normalisation of LC–MS/MS data across and within batches to correct for run order and batch effects arising from instrument instability. This can only be achieved with a careful experimental design, incorporating regular injections of QC samples. Normalisation methods using

pooled QC samples have been used in the past [13,14,20–22] but none of these methods have used a moving average of the pooled QC to account for the wave-like MS response, such an approach may be applicable to a range of metabolomic studies using LC–MS/MS. Acknowledgements The authors would like to thank James Rudge and Ben Atkins, Phenomenex, UK for advice whilst setting up the methodology. Baljit K. Ubhi and Susan C. Connor were funded by GlaxoSmithKline. Baljit K. Ubhi and Peter W. Davenport were funded by the Biotechnology and Biological Sciences Research Council (BBSRC). Clinicaltrials.gov id Eclipse study details were NCT00292552; Study Code SCO104960. Julian L. Griffin is funded by the Medical Research Council (UD99999906). References [1] [2] [3] [4]

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