UPLC-MS-Based Analysis of Human Plasma for Metabonomics Using Solvent Precipitation or Solid Phase Extraction Filippos Michopoulos,†,‡ Lindsay Lai,† Helen Gika,‡ Georgios Theodoridis,‡ and Ian Wilson*,† Clinical Pharmacology, Drug Metabolism and Pharmacokinetics, AstraZeneca, Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG, United Kingdom, and Laboratory of Analytical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, 541 24 Greece Received December 5, 2008
Abstract: A study of the factors involved in obtaining valid global metabolite profiles from the LC-MS of human plasma for the purposes of metabonomic analysis has been undertaken. Plasma proteins were either precipitated with 3 vol of organic solvent (methanol or acetonitrile) or subjected to solid phase extraction (SPE) on a C18bonded phase. For chromatography, a reversed-phase gradient system, based on acidified water/methanol, was used. Ultra performance liquid chromatography (UPLC) was performed on a C18-bonded stationary phase using sub 2 µm particles packed into a 2.1 × 100 mm column. The eluent from the column was subjected to analysis by positive electrospray ionization using a time-of-flight mass spectrometer. To obtain reproducible results for solventprecipitated plasma, the “conditioning” of the system with injections of matrix prior to the main analytical run was essential. The repeatability of the methodology was improved significantly when the sample preparation was performed using solid phase extraction. Keywords: Global metabolite profiling • metabonomics • metabolomics • plasma • UPLC-MS
Introduction Metabolic profiling, as performed for the purposes of metabolomic or metabonomic studies, is heavily dependent upon the delivery of reliable analytical data. While reproducible methods based on NMR spectroscopy and, to some degree, GCMS have been developed, LC-MS methods are still evolving.1,2 Methodology is clearly required for a range of sample types including urine, plasma/serum and tissue extracts and, given the differences in the composition of these matrices, it is unlikely that a single method will be suitable for them all. We have recently reported HPLC-MS3 and UPLC-MS4 methods for the metabonomic analysis of human urine, based on the direct injection of diluted samples. Repeatability was assessed in these methods via a combination of the use of both standard mixtures of test compounds and, more importantly, biological QC samples prepared from the biofluid under investigation. By placing these QC samples at regular intervals throughout * Author for correspondence. E-mail,
[email protected]; tel, 00 44 1625 513424; fax, 00 44 1625 516962. † AstraZeneca. ‡ Aristotle University of Thessaloniki.
2114 Journal of Proteome Research 2009, 8, 2114–2121 Published on Web 01/27/2009
the analytical run, we could effectively monitor the variation in important analytical parameters such as retention time, peak shape, peak intensity and mass accuracy.3,4 Plasma also represents an important biofluid for metabonomic analysis but unlike urine, which can be analyzed with minimal sample preparation, requires (as a minimum) the removal of proteins prior to injection in order to preserve the integrity of the chromatographic system. In addition, the presence of both polar analytes such as amino acids, glucose and so forth and nonpolar lipids in many ways means that this biofluid represents a rather more formidable challenge than urine. A number of studies have reported methods for the production of global metabolite profiles from either serum or plasma, using either GC (for example,5,6) or LC-MS (both HPLC and UPLC),7-12 or indeed both technologies (for example,13,14). In general, these methods have used solvent precipitation, with either methanol or acetonitrile-based systems, followed by centrifugation, to effect the removal of protein. Providing that a reasonably high solvent-to-sample ratio is used (e.g., 3:1 v/v), efficient protein precipitation can be achieved. Such methodologies are simple, and clearly amenable to relatively high-throughput methods. However, the use of approaches involving solvent precipitation may well affect the measured metabolite profile by resulting in the recovery of otherwise strongly protein-bound metabolites. An alternative approach to sample preparation for plasma, that does not involve solvent precipitation, is solid-phase extraction (SPE). SPE is also suitable for high-throughput analytical methods and this approach has not yet, to our knowledge, been applied to the metabonomic analysis of plasma. Here, we describe the results of studies aimed at producing reliable and repeatable UPLC-MS-based global metabolic profiles for human plasma samples using either solvent-based protein precipitation or sample preparation via solid phase extraction.
Experimental Procedures Solvents and Reagents. All solvents used for LC-MS analysis (acetonitrile (ACN), MeOH) were of HPLC grade, and were purchased from Fisher (Loughborough, Leicestershire, U.K.). Formic acid was of analytical grade and was also obtained from Fischer. Leucine enkephaline was purchased from SigmaAldrich (Gillingham Dorset, U.K.). Water (18.2 Ω) was obtained from a Purelab Ultra system from Elga (Bucks, U.K.). The Text Mix used was the standard Waters (Milford, MA) metabonomics 10.1021/pr801045q CCC: $40.75
2009 American Chemical Society
UPLC-MS-Based Analysis of Human Plasma for Metabonomics mixture containing theophylline, hippuric acid, nortriptyline, benzoic acid and caffeine. Samples and Sample Preparation. Blood samples were obtained from 14 control male volunteers, on two separate occasions 2 weeks apart, and collected into heparinized tubes. Plasma was prepared by centrifugation at 3000g at between 0 and 4 °C and samples were stored frozen at -20 °C until analyzed. As part of the “system conditioning” and quality control process, a pooled “quality control” (QC) sample was prepared by mixing equal volumes (100 µL) from each of the 28 samples.3,4,15 The plasma samples from each volunteer, for each collection, were analyzed in duplicate to provide technical replicates. Solvent Precipitation. For the solvent precipitation method, aliquots of 50 µL of test plasma were mixed with 150 µL of cold solvent (MeOH or ACN kept on ice) and vortexed to precipitate the proteins. At the same time, an aliquot of 150 µL of the “pooled” QC plasma was mixed with 450 µL of cold solvent and processed in the same way as the test samples. The precipitated proteins were then removed by centrifugation at 17 900 rcf for 10 min (Eppendorf Centrifuge 5417 C/R). Aliquots (160 µL) of the resulting clear supernatants were then placed into the glass inserts of the HPLC vials and mixed with 80 µL of H2O (in order to adjust the injection sample solvent composition to 50/50 aqueous/organic). Prior to analysis by UPLC-TOF-MS, the samples were centrifuged for 20 min at 2135 rcf and 10 °C (Sorvall RT7 PLUS Centrifuge with RTH250 Swinging Bucket Rotor). Solid Phase Extraction Procedure. For SPE, samples were extracted onto a C18 96 well plate (Varian, Palo Alto, CA). Prior to extraction, the phase was activated with 2 × 300 µL of MeOH and then further conditioned with 2 × 300 µL of H2O. Aliquots of 400 µL of plasma (diluted 1:3 v/v with H2O) were loaded onto the plate and drawn through the solid phase under vacuum. The phase was then washed with 2 × 200 µL of H2O and eluted with 2 × 250 µL of MeOH. The eluates were evaporated with N2 at 40 °C (Porvair MinVap 96 wells) and then reconstituted in 200 µL of H2O/ACN 95:5 (v/v). The SPE extractions were performed on an automated, computer controlled TOMTEC Quadra 96 model 196-320 instrument (TOMTEC, Inc., Hamden, CT). UPLC-MS. Sample analysis was performed on an Acuity UPLC-Q-TOF Micro (Waters) (see below). Following method development (as described in Results), the final methodology adopted here for the analysis of plasma was as follows. System Conditioning: Initially, 10 system “conditioning” injections of 20 µL of the pooled “QC” sample were made with a rapid gradient for chromatography as follows: The starting conditions for the rapid gradient were 90% A (0.1% formic acid in H2O) and 10% B (0.1% formic acid in MeOH) changing, in a linear gradient, to 100% B over 3 min. This solvent composition was held for 2.5 min before returning to the starting conditions, which were also held for 2.5 min, prior to the next conditioning QC injection. This gave an overall cycle time of 8 min/ conditioning sample. Sample Analysis: For analysis of the test samples, a longer gradient program, using the same solvents as the conditioning gradient, was used. The starting conditions were 90% solvent A and 10% solvent B (v/v) for the period 0-0.5 min, changing (in a series of linear gradients) first to 80% A, over 3 min, then to 30% A at 5 min and finally to 100% B at 13 min. This solvent composition was maintained at 100% B for 2.5 min followed by a return to the starting conditions and re-equilibration of
technical notes
the column for 2 min with 90% A 10% B (v/v) prior the next injection. All separations were performed using a Waters UPLC BEH C18 2.1 × 100 mm, 1.7 µm Acquity Column with an Acquity UPLC Van-Guard C18 2.1 × 5 mm Column using an ACQUITY UPLC System (Waters, Milford, MA) at 50 °C, at a flow rate of 400 µL/min. QC and test mix samples were analyzed repeatedly within the analytical run every 10 and 20 plasma samples, respectively. Mass Spectrometry. Mass spectrometry was performed using a Waters Micromass q-TOF Micro operating in positive ion electrospray (ESI) mode. The capillary and cone voltages were set at 3.5 kV and 28 V. The desolvation temperature was set to 300 °C and the source temperature to 120 °C. The cone gas was set to a flow rate of 50 L/h and the desolvation gas flow was maintained at 605 L/h. For mass accuracy, a LockSpray interface was used with leucine enkephalin (556.2771 amu) solution 0.25 µg/L at 30 µL/min as the lock mass. Full scan data were collected from 100 to 800 m/z over a period of 18 min with a scan time of 0.3 s and interscan delay of 0.1 s. MassLynx software (Waters) was used for system controlling and data acquisition. Data Processing. The raw spectrometric data acquired were processed using MarkerLynx application manager (Waters). MarkerLynx uses ApexTrack peak integration to detect chromatographic peaks. The track peak parameters were set as follows: Peak width at 5% height 14 s, peak-to-peak baseline noise was calculated automatically, minimum intensity 100, mass window 0.08 (Da), retention time window 0.2 min, noise elimination level 8, mass tolerance 0.08 (Da) with exclusion of deisotopic data. Data was only used for the period 0-13 min, that is, up to the point at which the column washing phase of the analysis commenced. Peak height rather than peak area was used for data analysis. Peak list data obtained by MarkerLynx were further processed by Simca P version 11 from Umetrics (Windsor, U.K.) for multivariate data analysis. Basic applications such as Principal Components Analysis and other statistical tools were implemented.
Results As we have reported previously, for urine analysis by HPLC and UPLC-MS,3,4 we employed a relatively simple acidified acetonitrile/water gradient for separating the polar metabolites present in the sample. However, as discussed in the Introduction, plasma presents a more complex analytical problem as the sample contains both polar/ionisable metabolites and nonpolar lipids covering a wide range of lipophilicity. We therefore investigated a range of different solvent compositions and gradients including acidified acetonitrile/water, acidified methanol-water and acidified acetonitrile/methanol (2:1 v/v)-water-based systems. From examination of the data from these various systems (retention time stability, numbers of features, peak shapes, peak distributions, ion intensities, etc., data not shown), it became clear that, of the combinations examined, the acidified methanol-water gradient systems had the best analytical characteristics for the components present. Parallel studies on conventional HPLC-MS analysis of plasma samples [Lai et al., in preparation] resulted in similar conclusions. Further method development was therefore concentrated on the use of methanol-based gradients. In developing procedures for the determination of the global metabolite profile of human plasma, a major factor that needs to be considered is sample preparation. First, given that direct Journal of Proteome Research • Vol. 8, No. 4, 2009 2115
technical notes injection of plasma is not possible without the rapid degradation of the column, methods for the removal of proteins are essential. There are a number of methods that can be used to remove proteins, solvent precipitation being the most widely employed. In previous studies (for example,13), where only short runs of a few tens of samples were performed, we employed solvent precipitation with 3 vol of cold acetonitrile, conditions which are known to provide an efficient removal of proteins, and chose this as a starting point, comparing it with the results obtained with methanol. Multiparametric data analysis of the UPLC-MS results for the plasma extracts from these subjects was performed (Figure 1A,B) using PCA, and showed good repeatability of the test mixtures (data not shown) and clustering of the QC samples. Of the two solvents used for precipitation, methanol clearly provided superior results in terms of group classification, with distinct clustering of the samples by sample collection period shown for the methanol-precipitated group but not for the acetonitrile-processed samples. Examination of the UPLC-MS profiles from both types of solvent-precipitated plasma sample revealed that there was a reduction in the amount of high molecular mass ions eluting during the column-washing step (13-16 min) for the methanol extracts. The finding that methanol precipitation provided a better result than that seen for acetonitrile is consistent with other published reports (for example,8-10). Under these conditions, ca. 1300 features were noted for methanol-precipitated plasma samples (with 2200 seen with acetonitrile), eluting between 0 and 13 min postinjection (i.e., up to the period when the column washing phase was initiated). As we (for example,3,4,15), and others12 have noted elsewhere with complex biofluid samples, it seems to be important to preequilibrate, or condition, the system before commencing analysis of the samples themselves in order to obtain repeatable results. For example, in the case of urine, it was necessary to “condition” the LC-MS with at least 5 of injections of matrix so as to stabilize variables such as retention times and signal intensities (as assessed by monitoring the performance of the test mixture and the QC samples).3,4 From our preliminary studies (data not shown) using both HPLC (Lai et al., in preparation) and UPLC, it became clear that, in the case of plasma, adequate system conditioning required more than five injections of the QC sample used for this purpose. Similar observations also apply to the UPLC-MS analysis of human serum.12 We therefore investigated the use of larger numbers of injections for the system conditioning step. As a result of this work, it became clear that at least 10 injections of 10 µL of matrix were required in order to achieve even a modest degree of stability, but even this was insufficient to achieve the repeatability required for routine metabonomic analysis. UPLCMS studies with methods for human serum have also shown a requirement for at least 10 of these conditioning injections.12 The need for such large numbers of injections of blank matrix clearly represents an inefficient use of instrument time, so we therefore explored two approaches to increase the completeness and efficiency of conditioning. Reasoning that the function of the conditioning injections was simply to “modify” the stationary phase in the column, the first approach was simply to double the amount of matrix sample injected onto the column from 10 to 20 µL thereby effectively doubling the number of conditioning injections for the same number of runs. Having established the utility of this approach for system conditioning, we then combined 10 × 20 µL injections of matrix 2116
Journal of Proteome Research • Vol. 8, No. 4, 2009
Michopoulos et al. with a more rapid gradient program, much shorter than the time used for the test samples, thereby reducing the overall time required prior to beginning analysis of the main run. As a result it appeared that 10 injections of 20 µL of the conditioning samples combined with the shorter gradient elution program gave reasonable repeatability as judged by the QC samples. This combination produced the results shown in the PCA Scores plots shown in, for example, Figure 1A,B in which the QC samples present within the run are tightly clustered. In Figure 2A,B, the results are shown for both methanol and acetonitrile-precipitated samples with the QC samples removed and a number of technical replicates highlighted (all samples from the first collection time point). As these figures show, while not identical, the technical replicates are generally similar, with some dispersion in PC1 related to run order. While the data from the test mixture (retention times, signal intensities, mass accuracy and peak shapes) and PCA of the QCs indicated that the system was reasonably stable, a more detailed assessment of repeatability of the system was undertaken. As a first step, the stability of the chromatographic separation with respect to retention time and signal intensity repeatability for selected ions detected in the QC samples was performed for QC1 to QC 7 (i.e., all of the QCs after the 10 conditioning injections used for column conditioning). The result for a number of these ions, selected to cover a range of masses and retention times, is shown in Table 1. Excellent retention time stability is seen for these ions over the whole run for both the acetonitrile- and methanol-precipitated plasma QCs, respectively (variation in retention was negligible with CV % values less than 1% in all these cases). The ions that were examined covered the retention time range from ca. 4 to 12.5 min (m/z 393.203_4.39 min, m/z 320.255_7.06 min, m/z 520.330_8.07 min, m/z 496.310_8.37 min, m/z 304.310_8.97 min, m/z 413.272_9.98 min and m/z 758.558_12.41 min). When the peak height (or peak area) repeatability of these particular components was examined, variation was found to be broadly acceptable with CV% values ranging between 0.58 and 16.17% for methanol and 0.35 and 11.14% for acetonitrile. This is also illustrated in Table 1 where the repeatability of the peak height determinations for these ions is given for QCs 1-7 (covering an analysis time of 19 h (63 injections)) for the acetonitrileand methanol-precipitated plasma samples, respectively. As we noted for the LC of urine some ions, for example, m/z 758.558, gave very stable responses while others, such as m/z 304.310, were more variable. We also examined the use of peak area measurements (data not shown) but found peak height to give more repeatable results. Mass accuracy for these analyses was also excellent for these ions, with variability less than 0.008 amu. The criteria for acceptable repeatability in metabolic profiling experiments are still being defined. However, as we have noted elsewhere,3,4 for bioanalytical methods used to monitor drugs, the FDA recommends that a coefficient of variation (CV) of 15% of the nominal value is considered to represent an acceptable degree of reproducibility (except for concentrations close to the LOQ where 20% is considered to be adequate).16,17 In the case of biomarkers, the criteria are somewhat less strict. The latest FDA guidance suggests the acceptance of up to 30% of total error for targeted LC-MS analysis.17 When the methanol-precipitated UPLC-MS data were examined, for all peaks across the QC samples, using similar acceptance criteria to those recommended by the FDA, the number of peaks fitting the 15% limit amounted to 46% (using
UPLC-MS-Based Analysis of Human Plasma for Metabonomics
technical notes
Figure 1. PCA of UPLC-MS of (A) methanol precipitated plasma, (B) acetonitrile precipitated plasma and (C) SPE extracted plasma. Open circles indicate samples obtained in the first urine collection, while those from the second collection are shown as solid “stars”. The QC cluster (solid squares) is highlighted within the red ellipses. Journal of Proteome Research • Vol. 8, No. 4, 2009 2117
technical notes
Michopoulos et al.
Figure 2. PCA scores plot of the UPLC-MS data from (A) methanol precipitated plasma, (B) acetonitrile precipitated plasma and (C) the SPE extracted plasma, showing the repeatability of the technical replicates for samples from the first collection period. Red squares represent the first injection of the sample, while blue squares the second. 2118
Journal of Proteome Research • Vol. 8, No. 4, 2009
technical notes
UPLC-MS-Based Analysis of Human Plasma for Metabonomics
Table 1. Variation in Retention Time and Response for Selected Ions Following Treatment of Human Plasma with Methanol Precipitation, Acetonitrile Precipitation and SPE on a C18-Bonded Phasea MeOH ret time (min)
ACN peak height
ret time (min)
C18 peak height
ret time (min)
peak height
m/z
aver.
CV (%)
aver.
CV (%)
aver.
CV (%)
aver.
CV (%)
aver.
CV (%)
aver.
CV (%)
304.310 520.330 496.310 320.255 413.270 393.203 758.558
8.96 8.06 8.37 7.05 9.98 4.39 12.41
0.04 0.09 0.08 0.05 0.05 0.09 0.03
10720 17588 20732 4772 1901 1150 19062
15.33 5.78 0.76 13.93 7.73 16.17 0.58
8.97 8.07 8.37 7.05 9.98 4.39 12.40
0.14 0.06 0.66 0.10 0.05 0.09 0.12
15152 17394 20662 4154 1899 3108 16667
11.14 1.77 0.33 5.52 1.68 5.43 3.65
8.98 8.09 8.39 7.06 9.99 4.40 12.42
0.06 0.22 0.14 0.10 0.04 0.09 0.06
1639 2334 4871 1204 18304 10429 7031
11.94 18.00 18.36 4.70 2.03 3.39 20.99
a
Analysis using UPLC-MS (positive ion ESI) with reversed-phase gradient elution.
peak height) of the total across all the peaks intensities. If the acceptance criteria are widened to encompass CVs of 20, 25 and 30%, then the number of peaks found to have an acceptable repeatability increased marginally to 52%, 54%, and 55%, respectively. Similar results were seen for the acetonitrileprecipitated samples with 43% of peaks being acceptable at the 15% CV level rising to 49% at a CV of 30%. As we noted for urine,3,4 there was a, not unexpected, relationship between the likely reproducibility of a particular ion and its intensity, with the more intense ions showing generally lower CVs. This relationship is shown in Figure 3, panels A and B for acetonitrile- and methanol-precipitated plasma, respectively. SPE. Ideally, in global metabolic profiling experiments, sample preparation should be kept to a minimum, but as indicated in the Introduction, plasma (and serum) analysis by LC-based methods requires protein removal. That being the case, we have also explored the use of SPE on a C18-bonded phase, with elution from the phase using methanol, for these samples. As with the solvent precipitation experiments, the data from the test mixture were excellent in terms of retention times, signal intensities, mass accuracy and peak shapes. In these SPE extracts, some 1500 features were noted. The effect of SPE on the analytical repeatability of QCs and samples is illustrated in Figures 1C and 2C where the PCA of these samples is shown. The PCA of these data show good clustering of the QC samples together with very good clustering of the technical replicates representing a clear improvement over simple solvent precipitation. Thus, Figure 1C (PCA scores plot t1 vs t2) clearly illustrates the highly repeatable profiles of replicate analyses randomized along a typical LC-MS run (ca. 90 injections in a 24 h run). Examination of the stability of the chromatographic separation with respect to retention time and signal intensity for selected ions detected in the QC samples of the type performed for the solvent-precipitated samples was also performed using the data obtained for QC1 to QC 7. The results for these ions, which were the same as those selected for the methanol/acetonitrile precipitated samples, once again showed excellent retention time stability (see Table 1) over the whole run, and as also seen for solvent precipitated samples, CV % values of less than 1% were obtained. Peak height (or peak area) repeatability for most of these ions was found to be within 5 and 20 CV% values (with little improvement if a 30% cutoff was used) with the variation summarized in Table 1. Similarly, mass accuracy for these SPE extracts was also excellent, with variability lower than 0.008 amu for these ions. For the whole SPE data set, the number of ions showing repeatability at CVs of 15 and 20% was 44% and 47%,
respectively, compared to 46% and 52% for methanol-precipitated samples. Similarly for CVs of 25 and 30%, the number of peaks found to have an acceptable repeatability increased only very slightly to 48% and 48%, respectively, compared to 54% and 55% for the methanol-precipitated plasma. Clearly, as would be expected, repeatability generally increased for the most intense ions as shown in Figure 3C.
Discussion It is quite clear, though perhaps not unexpected given the nature of the sample, that obtaining robust and repeatable analytical results for metabonomic/metabolomic profiling of human plasma appears to be more challenging than for samples such as urine. However, the need for such extensive column conditioning in order to obtain repeatable data was perhaps more surprising and the reasons for it remains to be determined. This observation is, however, not unique, and studies on method development for human serum undertaken as part of the HUSERMET project12 have also shown the same requirement for extensive column conditioning. Presumably, there are components in the sample (e.g., lipophilic compounds) present in the supernatant from the solvent precipitation step that are resistant to removal from the column in the washing step, that eventually saturate some site or sites on the stationary phase. It should be noted that this requirement for extensive conditioning is not a phenomenon that is confined to sub 2 µm stationary phases as we have obtained similar results with conventional 3.5 µm packing materials (Lai et al., in preparation). If this hypothesis is true, it might also be anticipated that the amount of column conditioning required would be different for new columns compared to those that have already gone through one or more cycles of analysis. This does indeed appear to be the case to some extent in that columns that have been used for the analysis of plasma when are reused they do appear to come to “equilibrium” more quickly, so that “column history” may well be a factor in column performance (data not shown). However, the previous use of the column does not remove the need for column conditioning, but may merely reduce it, and, as it does not seem to be possible to “over condition” columns, it is probably easier to standardize the method using a single set of conditions rather than attempt to vary them based on column use. Following suitable column condition procedure, it was possible to obtain repeatable results for both methanol and acetonitrile-precipitated samples. However, it is clear from the PCA that the choice of organic solvent employed had an effect on the result with better discrimination between the two Journal of Proteome Research • Vol. 8, No. 4, 2009 2119
technical notes
Michopoulos et al.
Figure 3. The relationship between ion intensity and CV(%) for methanol-precipitated plasma (A) acetonitrile-precipitated plasma (B), and plasma extracted using SPE (C).
different plasma collection times being shown with methanolic precipitates. As indicated earlier, this is consistent with other reports that describe methanol precipitation as providing better results than acetonitrile.8-10 As an alternative to solvent precipitation, the use of SPE may offer a valuable, and perhaps more easily automated, solution. It is not unreasonable to assume that substances that are prone to being strongly retained on a C18 phase contained in a chromatographic column will be equally strongly retained on a similar SPE material. That this may be the case was shown by the excellent repeatability of both QC and technical replicates for the SPEderived samples as shown in Figure 3C. There were differences in the intensity of ions depending upon the use of either solvent-based precipitation or the use of SPE, as illustrated in Table 1 (see also the work of Want et al.18 who also observed 2120
Journal of Proteome Research • Vol. 8, No. 4, 2009
such differences after the selective removal of serum phospholipids). Thus, the ion m/z 304.31, eluting at 8.96 min, was generally of high intensity in the solvent precipitated samples and low intensity in the SPE extracts, while the reverse was the case for the ion seen at m/z 413.27 min eluting at 9.98 min (Table 1). There are a number of possible reasons for these differences. For example, solvent precipitation results in a drastic alteration of the 3D-structure of proteins which might well result in the solubulization of metabolites (e.g., lipids) that are normally present bound to proteins. The SPE approach on the other hand would likely be less destructive and would perhaps selectively extract those compounds in free solution or more loosely bound to the surface of the proteins. Other possibilities include the potential for selective losses (due to non-retention or non-elution of metabolites) on SPE, leading
technical notes
UPLC-MS-Based Analysis of Human Plasma for Metabonomics to a loss in signal intensity due to a reduction in ion suppression as a result of the removal of coeluting metabolites. It can be argued that the loss of substances that are not retained on the SPE phase is unlikely to adversely affect the resulting metabolite profile as, given that as the retention mechanisms employed are identical, it is also unlikely that they would be retained on the HPLC column. Similar arguments can be made for any losses of compounds due to irreversible retention on the SPE column, but with the benefit that these compounds will now not be present in the extracts to degrade the HPLC column. Currently, we are unable to say which of these possibilities had occurred to give the observed differences and indeed a combination of all of these is likely to have occurred here. Clearly, further investigations are warranted. The method we have described here allows for the analysis of ca. 60 samples plus QCs and conditioning samples (effectively just under 100 plasma sample injections) in a single run for the methanol precipitated samples. It is our practical experience that attempts to increase the number of plasma samples analyzed above this number by UPLC-MS (or indeed HPLC-MS, Lai et al., in preparation) results in an increasing number of unacceptable/failed QCs toward the end of the run. These failed QCs are readily observed via PCA as they fall significantly outside the main group (e.g., see the examples provided in ref 2), and close examination of the data shows them to have significant changes in parameters such as retention time or, more frequently, signal intensity, leading to reduced repeatability. We have noted changes in the performance of the mass spectrometer that may be associated with such long runs including, for example, the degradation of detector sensitivity and mass accuracy (as also observed in the reference lock-mass signal which exhibited variability at the end of long runs). As a matter of “good practice”, we have found that cleaning the ion source of the mass spectrometer after the end of the run, prior to the commencement of the next batch, improves both the sensitivity and the mass accuracy of the mass spectrometer. Similar observations have been reported for serum,12 and it would appear that this loss of performance results from source contamination as performance is restored after cleaning. Like the HUSERMET consortium,12 we would therefore strongly recommend that run length does not exceed this sort of level of samples numbers, and that the mass spectrometer is cleaned between runs.
Conclusions The repeatable analysis of human plasma for the purposes of obtaining global metabolite profiles is technically demanding. With the use of adequate system conditioning, it is however possible to obtain suitable profiles for solvent-precipitated plasma samples, with some evidence that methanol is to be
preferred in this role. SPE, on a C18-bonded phase, also appears to offer good repeatability. Whichever sample preparation method is used for human plasma analysis, the combination of column conditioning, utilization of QC samples, strictly limited run length and regular cleaning of the mass spectrometer ion source are required to enable effective routine global metabolite analysis for human plasma samples.
Acknowledgment. This work was carried out in the context of an EU committee Transfer of Knowledge Industry-Academia partnership grant (TOK-IAP 29640) between the Aristotle University Thessaloniki, Greece, and AstraZeneca, U.K. References (1) Lenz, E. M.; Wilson, I. D. J. Proteome Res. 2007, 6, 443–458. (2) Theodoridis, G.; Gika, H. G.; Wilson, I. D. Trends Anal. Chem. 2008, 27, 251–260. (3) Gika, H. G.; Theodoridis, G. A.; Wingate, J. E.; Wilson, I. D. J. Proteome Res. 2007, 6, 3291–3303. (4) Gika, H. G.; Macpherson, E.; Theodoridis, G.; Wilson, I. D. J. Chromatogr., B 2008, 871, 299–305. (5) Trygg, A, J.; Gullberg, J.; Johansson, A. I.; Jonsson, P.; Antti, H.; Marklund, S. L.; Moritz, T. Anal. Chem. 2005, 77, 8086–8094. (6) O’Hagan, S.; Dunn, W. B.; Brown, M.; Knowles, J. D.; Kell, D. B. Anal. Chem. 2005, 77, 290–303. (7) Sabatine, M. S.; Liu, E.; Morrow, D. A.; Heller, E.; McCarroll, R.; Wiegand, R.; Berriz, G. F.; Roth, F. P.; Gerszten, R. E. Circulation 2005, 112, 3868–3875. (8) Want, E. J.; O’Maille, G.; Smith, C. A.; Brabdon, T. R.; Uritboonthai, W.; Qui, C.; Trauger, S. A.; Siuzdak, G. Anal. Chem. 2006, 78, 743– 752. (9) Bruce, S. J.; Jonsson, P.; Clorarec, C.; Trygg, J.; Marklund, S. L.; Moritz, T. Anal. Biochem. 2008, 372, 237–249. (10) O’Hagan, S.; Dunn, W. B.; Knowles, J. D.; Broadhurst, D.; Williams, R.; Ashworth, J. J.; Cameron, M.; Kell, D. B. Anal. Chem. 2007, 79, 464–476. (11) Dunn, W. B.; Broadhurst, D.; Brown, M.; Baker, P. N.; Redman, C, W. G.; Kenny, L. C.; Kell, D. B. J. Chromatogr., B 2008, 871, 288– 298. (12) Zelena, E.; Dunn, W.; Broadhurst, D.; Francis-McIntyre, S.; Carroll, K.; Begley, P.; O’Hagan, S.; Knowles, J. D.; Halsall, A.; Wilson, I.; Kell, D. Anal. Chem. 2009, 81 (4), 1357–1364. (13) Williams, R.; Lenz, E. M.; Wilson, A. J.; Granger, J.; Wilson, I. D.; Major, H.; Stumpf, C.; Plumb, R. Molecular Biosystems 2006, 2, 174–183. (14) Gao, P.; Lu, C.; Zhang, F.; Sang, P.; Yang, D.; Li, X.; Kong, H.; Yin, P.; Tian, T.; Lu, A.; Xu, G. Analyst 2008, 133, 1214–1220. (15) Sangster, T.; Major, H.; Plumb, R.; Wilson, A. J.; Wilson, I. D. Analyst 2006, 131, 1075–1078. (16) FDA Guidance for Industry, Bioanalytical method Validation, Food and Drug Administration, Centre for Drug Evaluation and Research (CDER), May 2001. A guidance. (17) Viswanathan, C. T.; Bansal, S.; Booth, B.; DeStefano, A. J.; Rose, M. J.; Sailstad, J.; Shah, V. P.; Skelly, J. P.; Swann, P. G.; Weiner, R. Am. Assoc. Pharm. Sci. 2007, 9, E30-E42. (18) Want, E. J.; Smith, C. A.; Qui, C.; van Horne, K. C.; Siuzdak, G. Metabolomics 2006, 2, 145–154.
PR801045Q
Journal of Proteome Research • Vol. 8, No. 4, 2009 2121
