High Resolution “Ultra Performance” Liquid Chromatography Coupled to oa-TOF Mass Spectrometry as a Tool for Differential Metabolic Pathway Profiling in Functional Genomic Studies Ian D. Wilson,† Jeremy K. Nicholson,‡ Jose Castro-Perez,§ Jennifer H. Granger,| Kelly A. Johnson,| Brian W. Smith,| and Robert S. Plumb| AstraZeneca, Dept of Drug Metabolism and Pharmacokinetics, Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG, United Kingdom, Biological Chemistry, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, United Kingdom, Waters Corporation, MS Technologies Center, Floats Road, Wythenshawe, Manchester, M23 9LZ, United Kingdom, and Waters Corporation, Milford, Massachusetts Received December 9, 2004
The combination of a new 1.7 µm reversed-phase packing material, and a chromatographic system, operating at ca. 12 000 psi, (so-called ultra performance liquid chromatography, UPLC) has enabled dramatic increases in chromatographic performance to be obtained for complex mixture separation. This increase in performance is manifested in improved peak resolution, together with increased speed and sensitivity. Here, we show that UPLC offers significant advantages over conventional reversedphase HPLC amounting to a more than doubling of peak capacity, an almost 10-fold increase in speed and a 3- to 5-fold increase in sensitivity compared to that generated with a conventional 3.5 µm stationary phase. The first functional genomic application of UPLC-MS technology is illustrated here with respect to multivariate metabolic profiling of urines from males and females of two groups of phenotypically normal mouse strains (C57BL19J and Alpk:ApfCD) and a “nude mouse” strain. We have also compared this technology to conventional HPLC-MS under similar analytical conditions and show improved phenotypic classification capability of UPLC-MS analysis together with increased ability to probe differential pathway activities between strains as a result of improved analytical sensitivity and resolution. Keywords: metabolic profile • metabonomics • C57BL19J mouse • Alpk:ApfCD mouse • nude mouse • chemometrics • UPLC-MS • functional genomics
Introduction Over the past 15 years, high performance liquid chromatography coupled to mass spectrometry (HPLC-MS/LC-MS) has become the powerhouse of the pharmaceutical and biotechnology industries for metabolic profiling of drugs. Applications such as metabolite identification, rapid analysis for drug discovery, regulatory bioanalysis, impurity analysis, and combinatorial purity screening now use LC/MS(MS) as the technique of choice.1 Similarly, LC/MS(MS) has become a mainstay of proteomics for protein identification.2 However, despite the impressive capabilities of modern HPLC-MS-based systems, the ever increasing requirement for increased throughput, coupled to the need for higher sensitivity and chromatographic resolution, in these areas means that continuing improvements in analytical performance remain a key goal for both instrument manufacturers and users. Nowhere are these requirements more sought than in the area of complex mixture analysis exemplified by the biological fluids that are the focus of †
AstraZeneca. Imperial College. § Waters Corporation, MS Technologies Center. | Waters Corporation. ‡
10.1021/pr049769r CCC: $30.25
2005 American Chemical Society
metabonomic investigations. Metabonomics, defined as the “Quantitative measurement of time-related multiparametric responses of multicellular systems to pathophysological stimuli or genetic modifications”3 is based on the determination of global metabolite profiles in biological fluids and tissues with subsequent data analysis via a range of multivariate statistical approaches.4 Until recently, the majority of such studies have employed high field NMR spectroscopy as the primary method for sample analysis, and this remains a powerful, nondestructive, approach for initial screening and profiling.5-8 However, other analytical approaches to obtaining multiparametric metabolite profiles are also possible, e.g., GC-MS (widely used for microorganism and plant metabolomics9) CE-MS10-12 and, with the continued development of robust HPLC-MS instrumentation, an increasing number of applications of HPLCMS for metabonomic studies are also appearing in the literature.13-17 These latter studies have demonstrated the potential of HPLC-MS to complement and extend the NMRbased methods, providing further insight into the metabolic sequelae of factors such as gender, strain, diurnal variation, disease, and toxicity. Clearly however, such methods are a compromise between the need for rapid analysis, to obtain the Journal of Proteome Research 2005, 4, 591-598
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Figure 1. Chromatograms given in Figure 1 (A and B) were obtained analyzing the same sample with UPLC and HPLC. Chromatogtaphic separation of white female AM mouse urine using (A): 2.1 × 100 mm Waters Symmetry 3.5 µm C18 column,eluted with 0-95% linear gradient of water with 0.1% formic acid: acetonitrile with 0.1% formic acid over 10 min at a flow rate of 0.6 mL/min. The column eluent was monitored by ESI oa-TOF-MS from 50 to 850 m/z in positive ion mode. (B) Using a 2.1 × 100 mm Waters ACQUITY 1.7 µm C18 column, eluted with a linear gradient of 0-95% water with 0.1% formic acid: acetonitrile with 0.1% formic acid over 10 min at a flow rate of 0.5 mL/min. The column eluent was monitored by ESI oa-TOF-MS from 50 to 850 m/z in positive ion mode.
highest possible throughput, and lengthy separations to obtain the most comprehensive profiles. One means of increasing chromatographic efficiency is to reduce the particle size. Thus, it is known that e.g., reducing the particle size by a factor of 2 increases separation efficiency by a factor of 2 and, hence, 592
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increases the resolution by 1.4.18 However, the, optimal linear velocity is inversely proportional to the particle size and the column back pressure is inversely proportional to the square of the particle size and this results in a 8-fold increase in back pressure when moving from a 3.5 to 1.7 µm stationary phase.
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Figure 2. Extracted ion chromatogram m/z ) 401 of white female AM mouse urine sample using either (A) a 2.1 × 100 mm Waters Symmetry C18 3.5 µm Column or (B) 2.1 × 50 mm Waters ACQUITY 1.7 µm C18 column. The columns were eluted with LC/MS conditions previously indicated.
Figure 3. 3-Dimensional Maps for (A) HPLC-MS and (B) UPLC-MS of white male mouse urine from AM collection mouse showing retention time, m/z and intensity.
Thus, the use of sub 2 µm particle stationary phases, while being attractive as a means of improving chromatographic performance, requires the use of higher back pressures than those conventionally applied in HPLC and, until recently, this has limited the use of this approach as reliable instrumentation and columns have not been available. Here we show how small particle size (1.7 µm) combined with high pressure (ca. 12 000 psi) can be combined in “ultra performance liquid chromatography” (UPLC) for the analysis of urine for a metabonomic investigation of gender, strain and diurnal variation in genetically distinct animals. These studies demonstrate that this approach is likely to be of great value for future functional genomic and clinical metabolic profiling studies.
Experimental Section Chemicals. Acetonitrile (HPLC grade) was purchased from JT Baker (NJ), ammonium formate and formic acid (spectroscopic grade) was purchased from Sigma/Aldrich (MO). Distilled water was purified “in-house” using a MilliQ system Millipore (MA). Leucine-enkephalin and xanthurate was obtained from Sigma-Aldrich (Mo, USA Animal Studies. Urine was obtained from 8 week old male and female mice from black C57BL19J, white Alpk:ApfCD and nude mouse strains (n ) 10 of each sex). The animals were housed in polypropylene cages and allowed free access to water and food. Urine samples were collected for morning (AM) and evening (PM) time periods providing a total of 120 samples. Journal of Proteome Research • Vol. 4, No. 2, 2005 593
research articles These samples were stored frozen prior to analysis at -20 °C. For analysis the samples were diluted 1:5 by making 20 µL of sample up to 100 µL with distilled water and transferred to a total recovery auto-sampler vial for analysis by HPLC-MS, to reduce the salt concentration in the sample. Chromatography. Chromatographic separations were performed on a 50 or 100 × 2.1 mm ACQUITY 1.7 µm column (Waters Corp, Milford, USA) using a ACQUITY Ultra Performance Liquid Chromatography system. The column was maintained at 40 °C and eluted with a linear gradient of 0-95%B, where A ) water with 0.1% formic acid and B ) acetonitrile with 0.1% formic acid. The gradient duration was either 2 or 10 min at a flow rate of 800 µL/min, or 500 µL/min respectively, with either a 0.5 or 2 min recycle time giving a total cycle time of 2.5 or 12 min. The column eluent was split such that approximately 150 µL/min was directed to the mass spectrometer. The conventional chromatographic separations were performed on a 100 × 2.1 mm Symmetry 3.5 µm column (Waters Corp, Milford, USA) using an Alliance 2795HT liquid chromatography system. The column was maintained at 40 °C and eluted with a linear gradient of 0-95% B, where A ) 0.1% formic acid and B ) acetonitrile 0.1% formic acid. The gradient duration was 10 min at a flow rate of 600 µL/min. The samples were diluted 1:5 in distilled water and a 10 µL injection of each sample was made onto the column. The column eluent was split such that approximately 150 µL/min was directed to the mass spectrometer Mass Spectrometry. Mass spectrometry was performed on a Micromass LCT Premier (Waters MS Technologies, Manchester, UK) operating in positive ion mode. The nebulization gas was set to 300 L/hr at a temperature of 250 °C the cone gas set to 0 L/hr and the source temperature set to 110 °C. A capillary voltage and a cone voltage were set to 3200 V and 60 V, respectively. The LCT-Premier was operated in W optics mode with 12 000 resolution using dynamic range extension (DRE). The data acquisition rate was set to 0.1 s, with a 0.1 s interscan delay. All analyses were acquired using the lock spray to ensure accuracy and reproducibility; leucine-enkephalin was used as the lock mass (m/z 556.2771) at a concentration of 50 fmol/µL and flow rate of 30 µL/min. Data were collected in centroid mode, the lockspray frequency was set at 5 s, and data were averaged over 10 scans. Chemometric Analysis. HPLC-MS and UPLC-MS data were divided into subsets for analyses to assess strain and gender differences, as well as diurnal variation. The raw data were analyzed by the Micromass MarkerLynx applications manager Version 1.0 (Waters, UK); this application manager integrates peaks in the LC/MS data by using ApexTrack peak detection. The LC/MS data are peak-detected and noise-reduced in both the LC and MS domains such that only true analytical peaks are further processed by the software (e.g., noise spikes are rejected). A list of the intensities of the peaks detected is then generated for the first sample, using the retention time (RT) and m/z data pairs as the identifier for each peak. An arbitrary number is then assigned to each these RT- m/z pairs in order of elution, (1,2,3,4,....etc). This process is repeated for each LC/ MS run; once this is completed the data from each LC/MS analysis in the batch are then sorted such that the correct peak intensity data for each RT- m/z pair is aligned in the final data table. The ion intensities for each peak detected are then normalized, within each sample, to the sum of the peak intensities in that sample. The resulting normalized peak 594
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Figure 4. Data generated by the PCA of 10 min UPLC-MS analysis of black white and nude AM female mouse urine, (A) scores and (B) loadings plot. White mice ) Green diamond, Black ) Black square, nude ) Magenta triangle.
intensities are then multiplied by 10 000. The resulting 2-dimensional data, peak number (RT - m/z pair), were analyzed by Principal Components Analysis (PCA).
Results and Discussion Resolution. A typical conventional HPLC analysis of a urine sample obtained from a white female mouse is given in Figure 1a. The chromatographic resolution and throughput was optimized on the HPLC and UPLC systems, in terms of flow rate and gradient duration. This separation was obtained on a 100 mm by 2.1 mm, 3.5 µm particle size, column eluted at 0.6 mL/min using a 10 min gradient elution. The equivalent chromatogram obtained from the same sample analyzed on a -UPLC chromatography system, with the same gradient duration etc., using a 2.1 × 100 mm, 1.7 µm C18 column operated at a flow rate of 500 µL/min and 8500 psi, is given in Figure 1b. A simple visual comparison of the two data sets shows that the UPLC peaks were much sharper and distinct and that, overall, the data set was more information rich. The chromatographic peaks generated in this UPLC chromatogram were in the order of 1.8 s wide at the base compared to 8 s for the conventional 3.5 µm column (see Figure 2a and b) giving a peak capacity of ca. 250 for a 10 min separation. This represents a significant increase in resolving power over that achieved with a conventional HPLC system, which typically generates peak capacities in the order of 60-80.13 This increased resolution resulted in a reduction in the number of coeluting compounds
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Figure 5. Comparison of the PCA analysis for HPLC-MS and UPLC-MS data obtained from male and female white mouse urines (from the AM urine collection), A) ) PCA score plot for HPLC analysis B) ) the corresponding loadings plot for HPLC, C) ) the scores plot by UPLC D) ) the corresponding loadings plot for UPLC.
and hence a commensurate reduction in ion suppression. This reduction in ion suppression resulted in an increase in the number of metabolites detected from a ca. 1800-2000 with HPLC/MS to 10 000-13 000 with UPLC/MS for a typical mouse urine sample. The increased number of ions detected is illustrated in the 3-dimensional maps shown in Figure 3a and b, which show mass vs chromatographic retention time and signal intensity for HPLC (Figure 3a) and UPLC (Figure 3b). This increase should prove to be advantageous for metabonomic and metabolomic studies where it might be anticipated that the more comprehensive the metabolite profile that is obtained the greater the chance that potential biomarkers will be detected. The UPLC columns were found to be robust and reproducible with no change in peak width or resolution after 500 injections of urine samples. The MS data generated from the UPLC/MS analysis of the female AM black C57BL19J, white Alpk:ApfCD and nude mouse urine samples were then subjected to data deconvolution by the MarkerLynx metabonomics algorithm and Principal Components Analysis (PCA). The data clustering observed for the female AM samples is given in Figure 4a, where there is clear resolution of the samples into three distinct groups (the corresponding loadings plot is given in Figure 4b). This result is similar to that generated in previous proton NMR19 and LC/MS-ao-TOF14 studies which have shown strain discrimination based on multivariate metabolic profiles. The improvement in discrimination that the use of UPLC-MSgenerated data affords over conventional HPLC-MS-based metabonomics is illustrated in Figure 5a and c for the analysis of white mouse urine from male and female animals for the AM collection period. The number of ions detected by UPLC was ca. 8000 whereas with HPLC < 1000 marker ions were found. Thus the UPLC approach gave significantly more information on the endogenous biochemicals present in the sample. In fact, there are many more detected species than
are listed in any texts on mammalian biochemistry suggesting that some of these at least are of sym-xenobiotic origin (a combination of mammalian and gut microbial metabolism20). The endogenous components contributing most significantly to the variance in the data/clustering the male and female white mice are shown in the corresponding loading plot (Figure 5b and d). One of the ions identified from the loading plot as being responsible for the variance in the data set was the [M-H+] ion m/z ) 206, eluting with a retention time of 2.5 min in the UPLC separation. Subsequent accurate mass analysis of this peak in one of the samples resulted in a measured mass of 206.0452 giving a proposed elemental composition of C10H8NO4. The calculated mass for this atomic composition would be 206.0453, thus giving a mass error of -0.6 ppm or -0.1mDa. MS/MS analysis of this M-H+ 206 ion gave rise to the major ions 132, 104, and 76 (Figure 6). From these data and cochromatography with an authentic standard it was possible to identify the peak as xanthurate (4,8-dihydroxyquinoline-2carboxylic acid), an intermediate in the tryptophan catabolism pathway (Figure 7). Xanthurate is a metabolite of kynurenine, which is normally excreted in the urine, and has been previously reported to be observed at reduced levels in renal toxicity studies.16 Detection Sensitivity. The more efficient chromatographic separation afforded by the UPLC methodology appears not only to result in a decrease in ion suppression as discussed above, with a consequent increase in the number of components detected in the samples, but also provides an increase in sensitivity. This is illustrated in Figure 2 which shows the positive ion extracted ion chromatogram m/z ) 401 mouse by both HPLC/MS and UPLC/MS and shows that the peak intensity has increased from 210 with HPLC to 756 with UPLC. This is a 3.5-fold increase. This increase in sensitivity is much more than the 1.7-fold increase predicted by chromatographic Journal of Proteome Research • Vol. 4, No. 2, 2005 595
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the latter has a better signal-to-noise ratio than the corresponding HPLC data. Thus, in the UPLC data for this component, there are four distinct ions visible at m/z ) 401, 313, 225, and 113. However, while, the m/z ) 401 and 113 ions are visible in the HPLC data the m/z 225 ion is largely lost in the spectral noise, and the m/z 313 ion is undetectable. The extra resolution produced by UPLC, therefore, generates superior spectral data that should enable easier interpretation for metabolic studies and for other problems involving complex organic mixture analysis.
Figure 6. The MS/MS spectrum of the m/z 206 peak identified as xanthurenate by comparison of HPLC-MS/MS using an authentic standard.
theory, and observed in LC-UV studies, and this is attributed to a general reduction in ion suppression in the mass spectrometer that results from multiple peak overlap; this increase in sensitivity also contributes to the increase in the number of components detected with UPLC/MS. Spectral Quality. The increased sensitivity produced by UPLC also has a positive effect on the quality of spectral data produced as illustrated using the m/z ) 401 ion discussed above. The MS spectra data generated under each peak by HPLC and UPLC are shown in Figure 8a and b indicating that
Figure 7. Tryptophan catabolism. 596
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Speed. One of the major advantages of using the 1.7 µm particles is increased separation speed which is due to the increase in optimal linear velocity, the reduction in column length needed to effect the desired separation and the flat nature of the van Deemter plot, allowing high flow rates to be employed.18 As an illustration of the potential benefits resulting from this combination of this ability to perform rapid separations the mouse urine samples were re-analyzed with a one minute run time. The samples were separated on a 2.1 × 50 mm column, using a 0 to 95% acetonitrile:0.1% aqueous formic acid gradient over 2 min, with a 0.5 min recycle period at a flow rate of 0.8 mL/min. This separation generated a maximum back-pressure of 6500 psi. The resulting PCA of the UPLC/MS data (chromatogram not shown) for male and female AM/PM mouse urine samples is shown in Figure 9. Even with this abbreviated analysis time, and consequent reduced peak resolution, the samples from the black and white animals are
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Figure 8. Single scan spectra of m/z 401 peak from (A) HPLC peak at 4.8 min and (B) UPLC peak at m/z ) 3.9 min.
Figure 9. (A) Statistical PCA analysis of black white nude mouse urine analyzed using a 1 min UPLC/MS separation. Nude samples ) green triangle, White ) red rectangle, Black ) black diamond. (B) Positive ion TIC chromatogram from UPLC/MS analysis of male white mouse AM urine sample.
clearly classified as distinct from those of the nude mice; significant separation of the black and white mouse urines is also achieved as well as of males and females. With this analytical protocol all 120 samples were analyzed in just 150 min opening up the possibility of relatively high throughput methods. This is increased speed of analysis is obviously not without some cost. The 2 min separations generated PCA separations that were less distinct than those generated with the 10 min separation, no doubt as a result of the lower resolution generated by the shortened gradient. This reduced resolution decreased the number of ions detected from ca. 10 000 for the equivalent 10 min separation to ca. 2000 ions. However, it is perhaps worth noting that this value is still greater than that generated by conventional HPLC in a 10 min gradient. The 2
min taken to perform the chromatographic analysis by UPLC compares favorably with automated sample infusion techniques and may well offer a more discriminating alternative to them for metabolic studies. Clearly, the use of an intermediate run time (e.g., 5 min.) would be expected to produce a better result than the 2 min separation, and this has indeed proved to be the case (unpublished results)
Conclusions The use of UPLC-MS-based methods to enable functional genomic discrimination of metabolic phenotypes in three strains and two genders of mice has been demonstrated with improved results compared to conventional HPLC-MS. The combination of very high pressures and a 1.7 µm porous stationary phases provided significantly increased resolution Journal of Proteome Research • Vol. 4, No. 2, 2005 597
research articles and rapid analysis times as the result of the superior efficiency of the chromatographic process, and probable reduced ion suppression in the ion source of the mass spectrometer, compared to conventional HPLC. This increased resolution proved to be extremely advantageous for the chromatographic analysis of very complex mixtures such as urine for metabonomic studies.
References (1) Ayrton, J.; Dear, G. J.; Leavens, W. J.; Mallett, D. N.; Plumb, R. S. J. Chromatogr. 1998, 828, 199-207. (2) Wolters, D. A.; Washburn, M. P.; Yates, J. R., III. Anal. Chem. 2001, 73, 5683-5690. (3) Nicholson, J. K.; Lindon, J. C.;Holmes, E. Xenobiotica 1999, 29, 1181-1189. (4) Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Nat. Rev. Drug Disk. 2002, 1, 153-161. (5) Nicholls, A. W.; Nicholson, J. K.; Haselden, J. N.; Waterfield, C. J. Biomarkers 2000, 5, 410-423. (6) Robertson, D. G.; Reilly, M. D.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. Comp. Toxicol. 2002, Vol XIV 585-610. (7) Nicholson, J. K.; Buckingham, M. J.; Sadler, P.J. Biochem J. 1983, 211, 605-615.
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Wilson et al. (8) Nicholson, J.K; Wilson, I. D. Prog. NMR Spectrosc. 1989, 21, 449501. (9) Fiehn O. Plant Mol. Biol. 2002, 48, 155-171. (10) Johnson, S.K,; Houk, L. L.;Johnson, D. C.; Houck, R. S. Anal. Chim. Acta 1999, 389, 1-8. (11) Soga, T.; Heiger, D. N. Anal. Chem. 2000, 72, 1236-1241. (12) Soga, T.; Ohashi, Y.; Ueno, Y.; Naraoka, H.; Tomita, M; Nishioka, T. J. Proteome. Res. 2003, 2, 488-494. (13) Plumb. R. S.; Stumpf, C. L.; Gorenstein, M. V.; Castro-Perez, J. M.;, Dear, G. J.; Anthony, M.; Sweatman, B. C.; Connor, S. C.; Haselden, J. N. Rapid Commun. Mass Spectrom. 2002, 16, 19911996. (14) Plumb, R. S.; Granger, J. H.; Stumpf, C. L.; Wilson, I. D.; Evans, J. A.; Lenz, E. M. Analyst 2003, 128, 535-541. (15) Idborg-Bjorkman, H; Edlund, P. O.; Kvalheim, O. M.; SchuppeKoistinen, I.; Jacobsson, S. P. Anal. Chem. 2003, 75, 4784-4792. (16) Lenz. E. M.; Bright. J.; Knight. R.; Wilson, I. D.; Major, H. Analyst 2004, 129, 535-541. (17) Lenz. E. M.; Bright. J.; Knight. R.; Wilson, I. D.; Major, H. J. Pharm. Biomed. Anal. 2004, 35, 599-608. (18) Neue, U. D.; Mazzeo, J. R. J. Sep. Sci. 2002, 24, 921-929. (19) Gavaghan, C.; Wilson, I. D.; Nicholson, J. K. FEBS Lett. 2002, 530, 191-196. (20) Nicholson, J. K.; Wilson, I. D. Nat. Drug Disc. 2003, 2, 668-676.
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