High-Throughput Microbore UPLCâMS Metabolic Phenotyping of

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High-Throughput Microbore UPLC-MS Metabolic Phenotyping of Urine for Large-Scale Epidemiology Studies Nicola Gray, Matthew R. Lewis, Robert Steven Plumb, Ian David Wilson, and Jeremy Kirk Nicholson J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00203 • Publication Date (Web): 28 Apr 2015 Downloaded from http://pubs.acs.org on May 9, 2015

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Journal of Proteome Research

High-Throughput Microbore UPLC-MS Metabolic Phenotyping of Urine for Large-Scale Epidemiology Studies

Nicola Gray, Matthew R. Lewis, Robert S. Plumb, Ian D. Wilson, Jeremy K. Nicholson*

MRC-NIHR National Phenome Centre, Division of Computational and Systems Medicine, Department of Surgery and Cancer, IRDB Building, Imperial College London, Hammersmith Hospital, London, W12 0NN, United Kingdom

*Corresponding Author: [email protected]. Tel: +4420 7594 3195

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Abstract A new generation of metabolic phenotyping centres are being created to meet the increasing demands of personalized healthcare and this has resulted in a major requirement for economical, high-throughput, metabonomic analysis by liquid chromatography-mass spectrometry (LC-MS). Meeting these new demands represents an emerging bioanalytical problem that must be solved if metabolic phenotyping is to be successfully applied to large clinical and epidemiological sample sets. Ultra-performance (UP)LC-MS-based metabolic phenotyping , based on 2.1 mm i.d. LC columns, enables comprehensive metabolic phenotyping but, when employed for the analysis of thousands of samples, results in high solvent usage. The use of UPLC-MS employing 1 mm i.d. columns for metabolic phenotyping rather than the conventional 2.1 mm i.d. methodology shows that the resulting optimized microbore method provided equivalent or superior performance in terms of peak capacity, sensitivity and robustness. On average we also observed, when using the microbore scale separation, an increase in response of 3-fold over that obtained with the standard 2.1 mm scale method. When applied to the analysis of human urine the 1 mm scale method showed no decline in performance over the course of 1000 analyses illustrating that microbore UPLC-MS represents a viable alternative to conventional 2.1 mm i.d. formats for routine large-scale metabolic profiling studies whilst also resulting in a 75 % reduction in solvent usage. The modest increase in sensitivity provided by this methodology also offers the potential to either reduce sample consumption or increase the number of metabolite features detected with confidence due to the increased signal-to-noise ratios obtained. Implementation of this miniaturized UPLC-MS method of metabolic phenotyping results in clear analytical, economic and environmental benefits for large-scale metabolic profiling studies with similar or improved analytical performance compared to conventional UPLCMS. 2 ACS Paragon Plus Environment

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Keywords Metabolic phenotyping, UPLC-MS, microbore columns, miniaturization

1

Introduction

One of the consequences of concepts such as personalized healthcare and large-scale biobanking initiatives is an emerging requirement for the deep metabolic phenotyping of tens or hundreds of thousands of samples. This, in turn, has led to the creation of a new generation of metabolic phenotyping centres where metabolic profiling is being applied to the analysis of samples from these large patient cohorts and populations to characterize the phenotypes associated with specific disease states, physiological stimuli or gene-environment interactions1-3. Meeting these increased demands for metabolic phenotyping brings with it unprecedented analytical requirements for analytical quality, the harmonisation of technologies, high-throughput analysis and cost minimization. A number of analytical techniques are routinely applied for the purpose of this type of metabolic phenotyping either singly or in combination. These techniques include NMR spectroscopy2,3, mass spectrometry (MS), either as a direct infusion (or injection) approach,4 or hyphenated to a variety of separation techniques such as liquid chromatography (LC)5,6,7, gas chromatography (GC)8,9 or capillary electrophoresis (CE)10,11. Ultra performance (or ultra-high performance) liquid chromatography-mass spectrometry (U(H)PLC-MS) has, as a result of the high peak capacities and rapid analysis that result from performing separations on sub-2 µm particles at pressures of up to 15 000 psi, become increasingly widely applied as a tool for metabolic profiling12,13,14.

As methods for metabolic phenotyping have matured from relatively small scale “proof of principle” studies, involving 10s to 100s of samples, there has been a trend towards the 3 ACS Paragon Plus Environment


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comprehensive analysis of much larger sample sets comprising 1000s of samples to gain an understanding of disease state in large cohort studies. A result of this has been the development of dedicated phenotyping centres, such as the UK MRC-NIHR National Phenome Centre (NPC), established in 2013. The NPC is resourced to process the large numbers of samples generated by epidemiological studies, for biomarker discovery that enables the risk of developing disease to be assessed, and clinical studies aimed at providing biomarkers for patient stratification and prognostic markers to guide therapy. The NPC performs comprehensive metabolic profiling of human biofluids using a range of 1H NMR3 and UPLC-MS-based parallel assays to provide comprehensive metabolic phenotyping. However, the multiplatform analyses, requiring significant amounts of sample, and large sample numbers involved inevitably demand large volumes of solvent for the chromatographic analysis. Solvent use, whilst not often considered in the normal course of analysis can, in high-throughput environments processing tens to hundreds of thousands of samples, result in a significant economic and environmental burden when purchase and waste disposal costs are taken into account. For example, running 10 UPLC-MS instruments with 2.1 mm i.d. columns operated at a flow rate of 0.6 mL/min requires over 3000 L of solvent a year, at an estimated cost of $370 000. Scaling down to 1 mm i.d. columns at a flow rate of 0.14 mL/min would require 700 L of solvent costing approximately $86 000, thereby reducing solvent usage by 2300 L and saving $284 000 a year15. One obvious opportunity for improved methods that maintain performance whilst reducing solvent consumption is miniaturization. Potential reductions in sample requirements also represent an additional advantage of miniaturization in situations where sample volumes are limited, or where micro sampling has been used such as e.g., dried blood spots, which can represent an attractive alternative for facile and minimally-invasive sampling 16.


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The use of microbore columns (internal diameter (i.d.) < 2.1 mm or ≥ 0.5 mm) has received a certain amount of interest in metabolite profiling17,18, offering enhanced analytical sensitivity combined with reduced mobile phase consumption resulting from the lower flow rates employed (typically 50 -400 µL/min)15. Due to the lower analyte elution volume, which increases analyte concentration and subsequent increase in detector response, a theoretical 4fold increase in sensitivity is obtained by employing 1 mm i.d. rather than 2.1 mm i.d. columns. For large-scale epidemiological studies comprising thousands of samples, the use of methods based on standard narrowbore (2.1 mm i.d.) columns requires large volumes of solvent to perform the separations, which is both expensive and environmentally unfriendly. When column i.d. is reduced, the volumetric flow rate must be scaled down by the square of the column diameter in order to maintain the same mobile phase linear velocity and obtain comparable separations, resulting in a typical reduction in flow rate of 75 % when scaling from 2.1 mm i.d. conditions to 1 mm i.d. Such a reduction in solvent consumption has an obvious and significant environmental and economic impact, providing a more efficient approach to analysing large sample numbers. Lower flow rates are also more compatible with ESI-MS, where smaller droplet formation aids desolvation and thus results in higher sensitivity. These benefits are very attractive in the area of metabolic phenotyping, enabling the detection of low level analytes and/or permitting smaller sample volumes, and less expensive analyses with reduced solvent consumption. However, despite the theoretical advantages of microscale LC, the technique has not been widely adopted for routine, highthroughput analysis. This has largely been attributable to the difficultly in translating the theoretical advantages into practical observations due to a lack of appropriate LC instrumentation and poor packing performance of microbore columns. Extra-column effects have a much higher contribution to band-broadening on smaller diameter columns which can result in a significant loss in resolution and sensitivity on conventional systems19. For many



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years, the major challenge has been the inherent dispersion volume of HPLC systems, typically focused on the volumetric needs of 4.6 mm i.d. columns. However, with the advent of sub-2 µm particle columns packed into 2.1 mm i.d. geometries, more modern UHPLC systems are equipped with lower delay and dwell volumes and are more applicable to separations with low peak volumes. Such advances in instrumentation, together with improved column packing capabilities, minimize band broadening and permit the increased performance of 2.1 mm i.d. columns to be realized. While nano-LC instruments and chipbased formats have been commercialized and offer even further increases in assay sensitivity and reductions in solvent consumption, this comes at the cost of replacing the entire inlet system. For laboratories operating UHPLC instrumentation with 2.1mm i.d. columns, transfer to 1 mm i.d. may offer a facile and economical approach to significantly minimize sample and solvent consumption while retaining comparable chromatographic performance.

Here the potential applicability of microbore LC for large-scale metabonomic studies has been evaluated with emphasis on chromatographic performance, sensitivity, robustness, and data quality. A critical comparison of 2.1 mm and 1 mm i.d. column formats for reversedphase separations was made to determine whether micro LC represents a viable approach for the routine metabolic phenotyping of large numbers of human urine samples.

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2.1

Experimental

Chemicals and Reagents

Optima grade LC-MS water was purchased from Fluka (Leicester, UK). Acetonitrile (LC-MS grade), formic acid (LC-MS grade), ammonium acetate (LC-MS grade), leucine enkephalin 6 ACS Paragon Plus Environment

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acetate salt hydrate, acetaminophen, caffeine, sulfadimethoxine, sulfaguanidine, verapamil, hippurate and sodium formate solution were purchased from Sigma Aldrich (Gillingham, UK). The chromatographic performance was evaluated with a series of probe analytes used for the LC/MS systems suitability test mix. The test mix comprised of sulfaguanidine (50 ng/mL), acetaminophen (125 ng/mL), caffeine (50 ng/mL), hippurate (500 ng/mL), leucine enkephalin (100 ng/mL), sulfadimethoxine (50 ng/mL) and verapamil (50 ng/mL).

2.2

Samples and Sample Preparation

A subset of 80 randomized control human urine samples (obtained from both sexes) were used to compare the analytical platforms for metabonomic studies. Samples were stored at 80 °C prior to analysis, where a 50 µL aliquot was transferred into an EppendorfTM tube for centrifugation for 10 min at 13 000 g. A 40 µL volume of the supernatant was then added to 40 µL water in a HPLC vial and 5 µL of each sample was combined to generate a pooled quality control (QC) sample (representative of the entire data set)20,21. Ten consecutive injections of the pooled samples were made at the start of the chromatographic run to ‘condition’ the column, previously shown to be necessary for the analytical system to equilibrate. The pooled sample was injected after every 10 samples to monitor instrument stability.

2.3

Liquid Chromatography-Mass Spectrometry (LC-MS)

2.3.1 Liquid Chromatography for System Suitability Test Mix Liquid chromatographic analysis was performed on an Acquity UPLC system, equipped with a binary solvent manager, Acquity UPLC sample manager and column heater (Waters Corp.,



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Milford, MA, USA), and interfaced with a Xevo TQ-S tandem quadrupole mass spectrometer (Waters Corp., Manchester, UK). Separation was performed on a HSS T3 2.1 x 100 mm, 1.8 µm column or a HSS T3 1 x 100 mm, 1.8 µm column (Waters Corp., Milford, MA, USA). The chromatographic mobile phase was composed of 0.1 % formic acid in water (A) and 0.1 % formic acid in acetonitrile (B). The column temperature was maintained at 40 °C and linear gradient elution was performed at 0.6 mL/min or 0.14 mL/min on the 2.1 mm or 1 mm i.d. columns respectively. The starting composition was 5 % B, increasing to 75 % over 3 min and returning to 5 % B for a 1.4 min re-equilibration step. A 2 µL injection loop was installed and full loop injection was performed. The weak and the strong washes were 95:5 (v/v) H2O/CH3CN and 100% isopropanol respectively.

The use of 1 mm i.d. columns, meant that a number of system modifications were necessary to reduce peak dispersion. For these microbore separations the standard outlet tubing i.d. (0.004”) was reduced to 0.0025” of minimum length and the ESI stainless steel capillary (125 µm i.d.) was replaced with a narrow bore variant (50 µm i.d.).

2.3.2 Liquid Chromatography for Metabolic Profiling Liquid chromatographic analysis was performed on an Acquity UPLC I-class system, equipped with a binary solvent manager, sample manager and column heater (Waters Corp., Milford, MA, USA), interfaced with a Synapt G2-S HDMS mass spectrometer (Waters Corp., Manchester, UK). The separations were performed on a HSS T3 2.1 x 100 mm, 1.8 µm column or a HSS T3 1 x 100 mm, 1.8 µm column. The chromatographic mobile phase was composed of 0.1 % formic acid in water (A) and 0.1 % formic acid in acetonitrile (B). The column temperature was maintained at 40 °C and linear gradient elution was 8 ACS Paragon Plus Environment

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performed at 0.5 mL/min or 0.11 mL/min on the 2.1 mm or 1 mm ID columns respectively. The starting composition was 1 % B, held for 1 min before increasing to 15 % at 3 min, 50 % at 6 min, 95 % at 9 min for a 1 min wash and returning to 1 % B for a 2 min re-equilibration step. A 0.5 µL injection of sample was performed using the flow through needle. The purge solvent was 95:5 (v/v) H2O/CH3CN and sample manager wash was CH3OH. For use with 1 mm i.d. columns, minor system modifications were made as described above (Section 2.3.1).

2.3.3 Mass Spectrometry for System Suitability Test Mix The probe analyte analysis was performed on a Xevo TQ-S tandem quadrupole instrument operated with electrospray ionization (ESI) operated in both positive (ESI+) and negative (ESI-) ion MS/MS modes. Nitrogen was used as the desolvation gas and argon was used as the collision gas. The following generic source conditions were used: capillary voltage, 3.0 kV (ESI+) and 2.0 kV (ESI-); source offset, 50 V; desolvation temperature, 600 °C; source temperature, 150 °C, desolvation gas flow, 1000 L/hr; cone gas flow, 150 L/hr; nebulizer gas, 7.0 bar; collision gas, 0.15 L/hr. Compound specific parameters, including cone voltage, ion transitions and collision energy are detailed in Table S1.

2.3.4 Mass Spectrometry for Metabolic Profiling Mass spectrometry for metabolic profiling was performed on a Synapt G2-S HDMS accurate mass instrument with electrospray ionization (ESI) operated in both positive (ESI+) and negative ion modes (ESI-). The same MS conditions were applied to both column dimensions. The capillary voltage was 1.5 kV or 2.0 kV for ESI+ and ESI- respectively,



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cone voltage was 30 V, source temperature was set at 120 °C with a cone gas (nitrogen) flow rate of 50 L/h, a desolvation gas temperature of 500 °C and a nebulization gas (nitrogen) flow of 900 L/h. The instrument was operated in resolution (V optics) mode and set to acquire data over the m/z range 50-1200 with a scan time of 0.1 s. All mass spectral data were collected in centroid mode using the MSe data acquisition22 function to obtain fragmentation data simultaneously. In function one a low collision energy (4 eV) was used and in the second function a high collision energy (ramp 15-45 eV) was used for fragmentation. For mass accuracy, leucine enkephalin (MW = 555.62) was used as a lock mass at a concentration of 200 pg/µL (in 50:50 CH3CN/H2O, 0.1 % formic acid) infused at a flow rate of 20 µL/min via a lock spray interface. Lockmass scans were collected every 30 s and averaged over 3 scans to perform mass correction. The instrument was calibrated before analysis with 0.5 mM sodium formate solution. The data were collected using MassLynx V 4.1 software (Waters Corp., Manchester, UK).

2.4

Data Analysis

The raw data from the system suitability test mix were analysed by the TargetLynx application manager in MassLynx software; this application manager integrates peaks in the LC-MS data by using ApexTrackTM peak detection.

The raw data from the metabolic profiling experiments were processed by Progenesis QI data analysis software (Nonlinear Dynamics, Newcastle, UK) for peak picking, alignment and normalization, to produce peak intensities for retention time (RT) and m/z data pairs. Further statistical analysis was performed on the resulting normalized peak intensities using SIMCA P 13.0.2 (Umetrics, Umea, Sweden). 10 ACS Paragon Plus Environment

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3

Results and Discussion

In order to evaluate the potential of microscale LC for metabolic phenotyping, columns of the same length and particle size were used to compare the analytical sensitivity, reproducibility and robustness to the conventional 2.1 mm i.d. reversed-phase methodology currently used13. In addition, to achieve the same linear velocities for both column formats, the volumetric flow rate was scaled from the 600 µL/min used for the 2.1 mm i.d. column to 140 µL/min for the test 1 mm i.d. column. The advantages of reduced flow rates for increasing sensitivity when using ESI-MS15 are well known and indeed significant increase for both positive and negative modes were seen using direct infusion experiments that mimicked the scaled conditions to be used for U(H)PLC-MS, with average increase of 3.2- (positive ESI) and 4fold (negative ESI) as shown in Supplementary Figure S1.

3.1

System Optimization for Microscale Separations

Initially the chromatographic performance of the 2.1 mm and 1 mm i.d columns were compared on an unmodified UPLC system which, when operated under identical conditions for both column geometries apart from flow rate, did provide a small increase in sensitivity for the microbore system. However, the overall chromatographic performance for the 1 mm i.d. column was poor compared with the conventional 2.1 mm i.d format, with significant band broadening evident such that the final two eluting peaks were barely resolved (see Supplementary Figure S2 A and B). In order to eliminate these extra-column band broadening effects, the system was modified to minimize dispersion by reducing pre- and post-column dead volumes and ensuring good connections. Several components of the conventional UPLC system, including tubing length and i.d., were modified to investigate, and minimize, their contribution to band-spreading. The effects of these modifications on



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peak shape and intensity are shown in Supplementary Figure S3 and quantified in Supplementary Table S2 whilst the effects on peak capacity are illustrated in Supplementary Figure S4. Peak capacity was greatly improved by reducing both the length and the i.d. of the outlet tubing (from 0.004” i.d. to 0.0025” i.d. PEEK tubing). Reducing the i.d. of the stainless steel ESI capillary from 125 µm to 50 µm also resulted in a significant improvement in the overall system chromatographic performance. However, due to the already low dispersion from the inlet system, further attempts to minimize pre-column volumes had little effect on peak capacity (see Supplementary Figure S5). The final optimized set up employed for the 1 mm i.d. column used in this study was as described in the experimental methods.

Once optimized for microbore LC, chromatographic performance was compared to that of the existing 2.1 mm scale LC-MS system with the results shown in Figure 1 and Supplementary Table S3 for the test mixture. The small difference in retention times between the two column geometries is a result of the delay volume of the UPLC system, but it is evident that the quality of the separation was not affected by this. The increase in peak intensity associated with the 1 mm compared to the 2.1 mm i.d. format is shown in Supplementary Figure S5. The average increase in peak intensity for positive ESI was 2.5-fold, with caffeine and hippurate exhibiting the greatest increase in response of 3.4-fold and sulfadimethoxine showing the lowest (1.1-fold). In negative ESI mode the average increase in peak intensity was 2.3-fold, with all analytes exhibiting similar increases in response. These data are summarized in Supplementary Table S2, where a clear trend for improved performance with respect to peak area, peak height and signal-to-noise (S/N) is seen for the results generated on the 1 mm compared to the 2.1 mm i.d. column format. S/N was calculated within MassLynx software, using Peak-to-Peak values where the greatest height of the signal range above the mean noise value is divided by the variance. 12 ACS Paragon Plus Environment

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3.2

Application to Global Metabolic Profiling

The optimized 1 mm i.d. method was then compared with a conventional 2.1 mm i.d. method for metabolic profiling using a model set of 100 human urine samples in positive and negative ESI with the same chromatographic conditions (except for flow rate) and mass spectrometer (data not shown for negative ESI mode). The resulting profiles of the same, pooled quality control (QC) sample obtained on 2.1 mm (A) and 1 mm (B) i.d. columns are shown in Figure 2 and show similar separation profiles for both geometries. Again, as alluded to previously, the difference in retention times between the two column geometries is a result of the delay volume of the UPLC system. To demonstrate that the migration from 2.1 mm to 1 mm i.d. column format does not compromise chromatographic performance, the peak width and peak capacity were calculated for several common components in human urine (hippurate, kynurenic acid, phenylalanine, tryptophan). The data showed that the average peak width for both the 1 mm and 2.1 mm i.d. columns was 3 sec at the base of the peak, exemplified by kynurenic acid in Figure 3. This results in an overall peak capacity of 200 over the 10 min gradient for both methods and shows equivalence between both separations for metabolic phenotyping. The gains in MS detector response (peak area and height) as well as the increase in signal-to-noise (S/N) that can be realized by migrating to smaller column internal diameters are also illustrated by kynurenic acid in Figure 3, while the general trend was at least a 2-3-fold increase in peak area, peak height and S/N.

3.2.1 Principal Components Analysis (PCA) The principal components analysis (PCA) of the data obtained from the 1 mm and 2.1 mm i.d.-based metabolic profiling of the 80 human urine samples is displayed in Figure 4 and Figure 5, illustrating the scores and loadings plots respectively. From the scores plots shown in Figure 4, the tight clustering of the QC samples (blue) in the centre of the plots provide an 13 ACS Paragon Plus Environment


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initial indication of acceptable reproducibility of the data for both column formats. It can also be seen from these Figures that the samples showing the greatest separation from the mean were the same, irrespective of column format (samples A1, A4, B5, C4, D6, D7 and E8). The feature ions contributing most significantly to the PCA separation were m/z 259.094, 405.153, 435.128, and 421.149 in both sets of data, further suggesting comparability of data generated by either 1 or 2.1 mm i.d. column formats. However, nearly 10% more features were detected in the data obtained using the 1 mm i.d. column compared with the conventional 2.1 mm platform, presumably as a result of the increased sensitivity of the method. The behaviour of the QC samples throughout each analytical run was also examined (see Supplementary Figure S6, which shows the first component t[1] versus the QC samples, with the 2 and 3 standard deviation (SD) limits for peak intensities) and showed acceptable variation over the course of the analysis. Whilst standards for the acceptance of data in global metabolic profiling studies (metabonomics/metabolomics) are still evolving, various pragmatic practices have been proposed using retention time and peak area coefficients of variance (CV) for each metabolite detected in the QC samples to determine the degree of reproducibility21,23. Acceptance criteria for individual peaks similar to those suggested by the FDA (Food and Drug Administration) as applied to biomarker assays (≤ 30 % CV)24,25 or more strict regulatory guidance, such as those used for bioanalytical methods (≤ 15 % CV)26, can then be employed depending on the application and degree of rigor required. Here, the approach used to compare the 2.1 and 1 mm i.d. column-derived data was to determine the percentage of peaks that fulfilled the acceptance criteria of either 15 or 30 % CV for each feature identified in the QC samples. The number of features displaying ≤ 15 or 30 % CV in positive ESI mode for the 2.1 and 1 mm i.d. column formats was 62 and 71% respectively using the ≤15% CV criterion and 87% vs 92% if a 30% CV cut off was used. Together, these


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data lend confidence that the 1 mm scale is able to perform as well as, or better than, the currently accepted methodology based on 2.1 mm i.d. columns.

3.3

Robustness and Repeatability for Metabolic Profiling of Urine

Robustness and repeatability are essential prerequisites for large-scale metabolic phenotyping studies on large cohorts of subjects comprising thousands of samples. Thus, factors such as good retention time stability, critical for peak alignment and identification, and stable peak shape and intensity are required in order to perform such studies. The robustness and reproducibility of 2.1 mm scale UPLC-MS is well established for metabolic phenotyping where, with well-prepared samples, it is possible to obtain good results over the course of analysing thousands of injections of samples such as urine13,27 or blood-derived samples (e.g. serum/plasma)14. Therefore, for 1 mm scale UPLC-MS to be accepted as a means of routine metabolite phenotyping it has be able to deliver a similar level of performance and robustness to existing 2.1 mm-based methods. To examine the properties of the microbore format, 1000 consecutive injections of human urine, spiked during the dilution step with the system suitability test mix, were analysed using a 1 mm i.d. column. System back pressure, analyte retention time, peak area stability and peak shape were monitored to assess overall system performance. The pressure traces resulting from the first and last injection of the urine samples are displayed in Figure 6. The two traces completely overlap, indicating no increase in back pressure during the analysis. With respect to retention time, peak shape and peak intensity, Figure 7 shows a comparison of the peaks for sulfaguanidine, acetaminophen and sulfadimethoxine obtained from the first and last injected urine samples demonstrating that there was no significant changes in any of these properties (retention time < 0.2 % and peak area response < 4.02 %) over the course of the run (ca. 170 hr of continuous operation). These data provide further confidence that microbore UPLC-MS, using 1 mm i.d. columns, is 15 ACS Paragon Plus Environment


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capable of providing robust data even with extended run times covering a large number of samples in a single batch.

4

Conclusion

These data demonstrate that the application of 1 mm scale chromatography, which combines high chromatographic efficiencies and fast separations for high-throughput analysis, is feasible for large-scale epidemiological and clinical studies requiring the metabolic phenotyping of urine. The results here demonstrate that with minor modification to the conventional system, the 1 mm scale separation achieved the same or better performance compared to the 2.1 mm scale separation. The average peak widths of the two systems were identical within the limits of the analytical measurement and the mass chromatograms were visually comparable. In order to achieve this level of performance, system optimization, through reducing dispersion (particularly post-column dispersion) and ensuring appropriate connections, is a critical factor with narrow bore columns to minimize band-spreading. The increased sensitivity that results from this approach suggests that as well as reducing solvent consumption the methodology affords the potential to also reduce sample requirements which, although not further explored here, is potentially of significant benefit where sample volume is limited or samples must be subject to multiple analyses. The low solvent volumes required by miniaturized LC systems compared to more conventional formats make them both cost effective and environmentally sensitive platforms when screening large number of samples. Combined with the potential to reduce sample volumes, without loss of metabolic coverage, this approach has much to commend it in high-throughput metabolic phenotyping environments.


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Supporting Information Available Additional information including comparison of MS response by direct infusion (Figure S1), comparison of 2.1 and 1 mm i.d. columns on a non-optimized system (Figure S2), chromatograms illustrating contribution of different system components on performance (Figure S3), peak capacity bar graph (Figure S4), peak intensity bar graph (Figure S5), comparison of pooled quality control sample stability (Figure S6), Xevo TQ-S MS parameters (Table S1), peak width, resolution and peak capacity data of system suitability test mix components with different column geometries (Table S2) and MS response, peak area, peak height and signal-to-noise data for system suitability test mix components with different column geometries (Table S3). This material is available free of charge via the Internet at http://pubs.acs.org:

Acknowledgments The MRC-NIHR National Phenome Centre is supported by the UK Medical Research Council [in association with National Institute of Health Research (England)] Grant MC_PC_12025. The financial support of Bruker Biospin, Waters Corporation, Metabometrix LTD and Imperial College is also gratefully acknowledged by the NPC. The views expressed are those of the authors and not necessarily those of the NHS, or NIHR or the Department of Health.



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References 1. J. K. Nicholson, J. C. Lindon, E. Holmes, 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica 1999, 29. 1181-1189. 2. J. C. Lindon, J. K. Nicholson, E. Holmes, J. R. Everett, Metabonomics: Metabolic processes studied by NMR spectroscopy of biofluids, Concepts in Magnetic Resonance 2000, 12. 289-320. 3. A. C. Dona, B. Jiménez, H. Schäfer, E. Humpfer, M. Spraul, M. R. Lewis, J. T. M. Pearce, E. Holmes, J. C. Lindon, J. K. Nicholson, Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping, Anal. Chem. 2014. 4. N. N. Kaderbhai, D. I. Broadhurst, D. I. Ellis, R. Goodacre, D. B. Kell, Functional Genomics via Metabolic Footprinting: Monitoring Metabolite Secretion by Escherichia coli Tryptophan Metabolism Mutants Using FT-IR and Direct Injection Electrospray Mass Spectrometry, Comp. Funct. Genomics 2003, 4. 5. R. S. Plumb, C. L. Stumpf, M. V. Gorenstein, J. M. Castro-Perez, G. J. Dear, M. Anthony, B. C. Sweatman, S. C. Connor, J. N. Haselden, Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development, Rapid Commun. Mass Spectrom. 2002, 16. 1991-1996. 6. R. Plumb, J. Granger, C. Stumpf, I. D. Wilson, J. A. Evans, E. M. Lenz, Metabonomic analysis of mouse urine by liquid-chromatography-time of flight mass spectrometry (LCTOFMS): detection of strain, diurnal and gender differences, Analyst 2003, 128. 819-823. 7. E. M. Lenz, J. Bright, R. Knight, I. D. Wilson, H. Major, Cyclosporin A-induced changes in endogenous metabolites in rat urine: a metabonomic investigation using high field 1H NMR spectroscopy, HPLC-TOF/MS and chemometrics, J. Pharm. Biomed. Anal. 2004, 35. 599-608. 8. O. Fiehn, Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry, TrAC, Trends Anal. Chem. 2008, 27. 261-269. 9. P. Begley, S. Francis-McIntyre, W. B. Dunn, D. I. Broadhurst, A. Halsall, A. Tseng, J. Knowles, R. Goodacre, D. B. Kell, Development and Performance of a Gas Chromatography−Time-of-Flight Mass Spectrometry Analysis for Large-Scale Nontargeted Metabolomic Studies of Human Serum, Anal. Chem. 2009, 81. 7038-7046. 10. M. R. N. Monton, T. Soga, Metabolome analysis by capillary electrophoresis–mass spectrometry, J. Chromatogr. A 2007, 1168. 237-246. 11. R. Ramautar, G. W. Somsen, G. J. de Jong, CE-MS in metabolomics, ELECTROPHORESIS 2009, 30. 276-291. 12. R. Plumb, J. Castro-Perez, J. Granger, I. Beattie, K. Joncour, A. Wright, Ultraperformance liquid chromatography coupled to quadrupole-orthogonal time-of-flight mass spectrometry, Rapid Commun. Mass Spectrom. 2004, 18. 2331-2337. 13. E. J. Want, I. D. Wilson, H. Gika, G. Theodoridis, R. S. Plumb, J. Shockcor, E. Holmes, J. K. Nicholson, Global metabolic profiling procedures for urine using UPLC-MS, Nat. Protocols 2010, 5. 1005-1018. 14. W. B. Dunn, D. Broadhurst, P. Begley, E. Zelena, S. Francis-McIntyre, N. Anderson, M. Brown, J. D. Knowles, A. Halsall, J. N. Haselden, A. W. Nicholls, I. D. Wilson, D. B. Kell, R. Goodacre, Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry, Nat. Protocols 2011, 6. 1060-1083. 18 ACS Paragon Plus Environment

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15. P. D. Rainville, J. L. Simeone, S. M. McCarthy, N. W. Smith, D. Cowan, R. S. Plumb, Investigation of microbore UPLC and nontraditional mobile phase compositions for bioanalytical LC–MS/MS, Bioanalysis 2012, 4. 1287-1297. 16. F. Michopoulos, G. Theodoridis, C. J. Smith, I. D. Wilson, Metabolite Profiles from Dried Biofluid Spots for Metabonomic Studies using UPLC Combined with oaToF-MS, J. Proteome Res. 2010, 9. 3328-3334. 17. E. M. Lenz, R. E. Williams, J. Sidaway, B. W. Smith, R. S. Plumb, K. A. Johnson, P. Rainville, J. Shockcor, C. L. Stumpf, J. H. Granger, I. D. Wilson, The application of microbore UPLC/oa-TOF-MS and 1H NMR spectroscopy to the metabonomic analysis of rat urine following the intravenous administration of pravastatin, J. Pharm. Biomed. Anal. 2007, 44. 845-852. 18. H. Ogiso, T. Suzuki, R. Taguchi, Development of a reverse-phase liquid chromatography electrospray ionization mass spectrometry method for lipidomics, improving detection of phosphatidic acid and phosphatidylserine, Anal. Biochem. 2008, 375. 124-131. 19. K. J. Fountain, U. D. Neue, E. S. Grumbach, D. M. Diehl, Effects of extra-column band spreading, liquid chromatography system operating pressure, and column temperature on the performance of sub-2-µm porous particles, J. Chromatogr. A 2009, 1216. 5979-5988. 20. H. G. Gika, G. A. Theodoridis, J. E. Wingate, I. D. Wilson, Within-Day Reproducibility of an HPLC−MS-Based Method for Metabonomic Analysis: Application to Human Urine, J. Proteome Res. 2007, 6. 3291-3303. 21. H. G. Gika, E. Macpherson, G. A. Theodoridis, I. D. Wilson, Evaluation of the repeatability of ultra-performance liquid chromatography–TOF-MS for global metabolic profiling of human urine samples, J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2008, 871. 299-305. 22. M. Wrona, T. Mauriala, K. P. Bateman, R. J. Mortishire-Smith, D. O'Connor, ‘All-inOne’ analysis for metabolite identification using liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry with collision energy switching, Rapid Commun. Mass Spectrom. 2005, 19. 2597-2602. 23. F. Michopoulos, L. Lai, H. Gika, G. Theodoridis, I. Wilson, UPLC-MS-Based Analysis of Human Plasma for Metabonomics Using Solvent Precipitation or Solid Phase Extraction, J. Proteome Res. 2009, 8. 2114-2121. 24. J. W. Lee, R. S. Weiner, J. M. Sailstad, R. R. Bowsher, D. W. Knuth, P. J. O’Brien, J. L. Fourcroy, R. Dixit, L. Pandite, R. G. Pietrusko, H. D. Soares, V. Quarmby, O. L. Vesterqvist, D. M. Potter, J. L. Witliff, H. A. Fritche, T. O’Leary, L. Perlee, S. Kadam, J. A. Wagner, Method Validation and Measurement of Biomarkers in Nonclinical and Clinical Samples in Drug Development: A Conference Report, Pharm Res 2005, 22. 499-511. 25. J. Lee, V. Devanarayan, Y. Barrett, R. Weiner, J. Allinson, S. Fountain, S. Keller, I. Weinryb, M. Green, L. Duan, J. Rogers, R. Millham, P. O'Brien, J. Sailstad, M. Khan, C. Ray, J. Wagner, Fit-for-Purpose Method Development and Validation for Successful Biomarker Measurement, Pharm Res 2006, 23. 312-328. 26. FDA, Bioanalytical Method Validation, http://www.fda.gov. 2001, May. 27. H. G. Gika, G. A. Theodoridis, M. Earll, I. D. Wilson, A QC approach to the determination of day-to-day reproducibility and robustness of LC–MS methods for global metabolite profiling in metabonomics/metabolomics, Bioanalysis 2012, 4. 2239-2247.



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Figures

Figure 1. Comparison of 2.1mm i.d. and 1mm i.d. scale separations using the optimized system in (A) ESI positive (illustrated using sulfaguanidine (1), acetaminophen (2), caffeine (3), hippurate (4), leucine encephalin (5), sulfadimethoxine (6) and verapamil (7)) and (B) ESI negative (illustrated using hippurate (1), leucine enkephalin (2), sulfadimethoxine (3) and cholic acid (4).

Figure 2. Scaled base peak intensity (BPI) chromatograms of pooled human urine quality control (QC) samples diluted 1:1 with water injected on 2.1 mm i.d. (A) and 1 mm i.d. (B) columns using the same gradient scaled from 0.5 mL/min to 0.11 mL/min and the same injection volume.

Figure 3. Extracted ion chromatograms (axis linked for detector response) for kynurenic acid (m/z 190.0504) from a human urine sample injected on 2.1 mm i.d. (A) and 1 mm i.d. (B) columns at 0.5 mL/min and 0.11 mL/min.

Figure 4. Principal components analysis (PCA) plots of the first (t[1]) versus the second (t[2]) principal components of human urine samples (green) and a pooled QC sample (blue) injected every 10 samples analysed using a 2.1 mm i.d. column with a flow rate of 0.5 mL/min (A) and a 1 mm i.d. column with a flow rate of 0.11 mL/min (B). Labels illustrate the same samples in both analyses, with the same samples highlighted as being different from the mean highlighted in red.


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Figure 5. Loadings plots derived from the PCA plots of the first (p[1]) versus the second (p[2]) principal components of a set of human urine samples analyzed using a 2.1 mm i.d. column with a flow rate of 0.5 mL/min (A) and a 1 mm i.d. column with a flow rate of 0.11 mL/min (B). Labels indicate the m/z values for discriminating ions.

Figure 6. Comparison of column backpressure for the first (1) and last (1000) injection of human urine on a 1 mm i.d. column. The two traces are overlaid and are indistinguishable.

Figure 7. Peak shape repeatability illustrated by overlaid chromatograms of injection 1 and 1000 of sulfaguanidine (215.1 > 92.1) (A), acetaminophen (152.0 > 92.9) (B) and sulfadimethoxine (311.11 > 92.1) (C) spiked into human urine.



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Figures Figure 1:

Figure 2:


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Figure 3:

Figure 4:



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Figure 6:

Figure 7:


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