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Precision high throughput proton NMR spectroscopy of human urine, serum and plasma for large-scale metabolic phenotyping Anthony C. Dona, Beatriz Jimenez, Hartmut Schaefer, Eberhard Humpfer, Manfred Spraul, Matthew R. Lewis, Jake Thomas Midwinter Pearce, Elaine Holmes, John C Lindon, and Jeremy Kirk Nicholson Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/ac5025039 • Publication Date (Web): 02 Sep 2014 Downloaded from http://pubs.acs.org on September 11, 2014

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Analytical Chemistry

Precision high throughput proton NMR spectroscopy of human urine, serum and plasma for large-scale metabolic phenotyping Anthony C Dona1,3, Beatriz Jiménez1,4, Hartmut Schäefer2, Eberhard Humpfer2, Manfred Spraul2, Matthew R. Lewis1, 3, Jake T.M. Pearce1, 3, Elaine Holmes1, John C. Lindon1 and Jeremy K. Nicholson1,3,4*

1

Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial

College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, United Kingdom. 2

Bruker Biospin GmbH, Silberstreifen, 76287 Rheinstetten, Germany.

3

MRC-NIHR National Phenome Centre, IRDB Building, Imperial College London, Hammersmith

Hospital, London W12 0NN, United Kingdom. 4

Imperial Clinical Phenotypng Centre, QEQM Building, Imperial College London, Saint Mary’s

Hospital, London W21NY, United Kingdom.

*Author for correspondence: Jeremy K. Nicholson, email: [email protected] Tel: +4420 7594 3195

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ABSTRACT Proton NMR-based metabolic phenotyping of urine and blood plasma/serum samples provides important prognostic and diagnostic information and permits monitoring of disease progression in an objective manner. Much effort has been made in recent years to develop NMR instrumentation and technology to allow the acquisition of data in an effective, reproducible and high throughput approach that allows the study of general population samples from epidemiological collections for biomarkers of disease risk. The challenge remains to develop highly reproducible methods and standardized protocols that minimise technical or experimental bias, allowing realistic interlaboratory comparisons of subtle biomarker information. Here we present a detailed set of updated protocols that carefully consider major experimental conditions including sample preparation, spectrometer parameters, NMR pulse sequences, throughput, reproducibility, quality control and resolution. These results provide an experimental platform that facilitates NMR spectroscopy usage across different large cohorts of biofluid samples, enabling integration of global metabolic profiling that is a prerequisite for personalized healthcare.


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INTRODUCTION Metabolic phenotyping based on the exploratory biochemical analysis of biological fluids, tissues and tissue extracts

1,2

involves systematic profiling of multiple metabolite concentrations and their

fluctuations in response to genetic modulations, lifestyle, the environment, drugs, diet and other stimuli in order to characterize the beneficial and adverse effects of these kinds of interactions and to evaluate the biochemical mechanisms involved in such changes 3-15. Metabolic phenotyping has also found application in many disease studies 16-18 and also in complex interacting systems such as between humans, their nutrition and their symbiotic gut microflora 19,20. The possibility of predicting post-dose drug effects from baseline metabolic profiles has been demonstrated (pharmaco-metabonomics) as a potential effector for personalized medicine

21,22

.

These and other large-scale applications, for example, in epidemiological studies [30-32], show the necessity for standardized protocols to ensure high reproducibility and the possibility of confidently comparing spectra from various laboratories. Metabolic phenotyping generally uses biofluids or cell or tissue extracts as primary sources of metabolic fingerprint data (although intact tissue samples can be analysed using magic-anglespinning NMR spectroscopy)

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. Urine and serum or plasma are the most commonly studied

biofluids, and are easily obtained and prepared when compared with intact or extracted tissues 24. These metabolic profiling technologies use mainly NMR spectroscopy 25-27 and chromatography-mass spectrometry (MS)

28

. The multivariate spectroscopic data produced are typically analyzed using

chemometric techniques to identify significant metabolic combinations that can be used for sample classification and global biomarker discovery 29. NMR spectroscopy has been used extensively for multivariate metabolic profiling of cells, tissues and biological fluids since the 1980’s, and more recently, many NMR-based applications of metabonomics have been published, including studies in experimental animals on male/female



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differences, age-related changes, dietary modulation, diurnal effects and phenotyping of mutant and transgenic animals and toxicological applications to identify specific biomarkers of organ toxicity 30,31. Many papers have used human metabolic phenotyping to address improved disease diagnosis and prognosis

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, and more recently some studies have been published of large sample cohorts from

epidemiological studies to identify biomarkers of disease risk in populations 33-35. High-resolution NMR spectroscopy is a quantitative non-destructive, non-invasive, technique that provides detailed information on solution-state molecular structures. NMR spectroscopic methods can also be used to probe metabolite molecular dynamics and mobility (such as ligand–protein binding) through the interpretation of NMR spin relaxation and molecular diffusion properties

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.

NMR is a robust and reliable technique for metabonomic applications in which high reproducibility is paramount

37-39

. It allows the detection of a wide range of structurally diverse metabolites

simultaneously, providing a metabolic ‘snapshot’ at a particular time point (for serum and plasma this is the situation at the time of sampling but for urine the metabolic profile is an integration over time of the period that the urine has spent in the bladder). Metabolite concentrations down to the low micromolar concentration range are detected in a 4–5 min data acquisition time using current commercially-available spectrometers. Usually, the whole spectral information is used for further chemometric analysis, but specific NMR pulse sequences can be employed to select subsets of metabolites, if necessary. On the other hand, using no selection or extraction of metabolites upfront to investigate all possible variables is advantageous in profiling studies in which no prior information about metabolites is known or in which it still remains to be established whether several metabolites are linked to the outcomes of interest. NMR spectra of biofluids have been described many times

8,40

. In summary, a typical 1H NMR

spectrum of urine contains thousands of sharp lines from predominantly low molecular weight metabolites. Blood plasma and serum contain both low and high molecular weight components, and these give a wide range of signal line widths. Standard NMR pulse sequences, where the observed


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peak intensities are edited on the basis of molecular diffusion coefficients or on NMR relaxation times [such as the Carr–Purcell–Meiboom–Gill (CPMG) spin-echo sequence], can be used to largely enhance only the contributions from macromolecules, or to selectively highlight the signals from the small molecule metabolites, respectively. Identification of biomarkers can involve the application of a range of techniques, but 1H NMR spectra of urine and other biofluids, even though they are complex, allow many resonances to be assigned directly based on their chemical shifts, signal multiplicities and by adding authentic material, and further information can be obtained by using spectral editing techniques, 2-dimensional (2D) NMR spectroscopy or interrogation of spectral databases of authentic substances. DEDICATED PHENOME CENTERS FOR LARGE SAMPLE COHORT STUDIES The requirement to study large cohorts of samples such as for epidemiological research means that a substantial number of parallel assays have to be undertaken if the results are to be available in a reasonable time. This then leads to the need to be able to combine data or results from such simultaneous analyses. For these reasons, rigorous protocols have to be in place combined with strong quality control procedures. This approach is optimally pursued in a dedicated phenotyping center. The first such center, the UK MRC-NIHR National Phenome Centre (NPC) was established in 2013 and funded to Imperial College and Kings College, London to conduct metabolic phenotyping studies on cohorts of samples from large scale epidemiological studies to assess metabolic biomarkers of disease risk in populations

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. The is based in London at the Hammersmith Hospital and South

Kensington campuses, within the Department of Surgery and Cancer, Imperial College London. Substantial additional funding, equipment and know-how have also been provided by NMR and MS instrument manufacturers, Bruker Biospin GmbH and the Waters Corporation. Access to the centre is controlled by an academic Access Committee, which selects and prioritises the projects to take forward. All projects must have a clear potential for improving human health. This phenome centre 5 ACS Paragon Plus Environment


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is the first of its type in the world and provides a possible template for future studies. It complements the Imperial Clinical Phenotyping Centre (CPC) concurrently established within the Department of Surgery and Cancer at St. Mary’s Hospital Paddington, London. This latter facility is directed at metabolic phenotyping individual patient journeys with the aim of improved diagnostics, therapy management and prognosis of outcomes 29,41,42. As the phenome centres are established they will initially provide a limited range of assays and over time the number and range of analyses will gradually be expanded. For the first 12-18 months the NPC will focus on metabolic analysis using high field proton NMR spectroscopy and multiplatform MS analysis of urine or serum/plasma samples from epidemiology studies. Thus the NPC will only analyze human samples, which must be fit for purpose and these must have relevant associated clinical or other meta-data e.g. genomics. The quality of samples is vital to the success of any major screening project, and so too is the quality of the associated clinical and physiological metadata and how it will be combined with metabolic phenotyping. Consent and ethics approval for the requested studies must be in place and relevant documentation provided. The range of detectable metabolic features includes amino acids, carbohydrates, carbohydratephosphates and other glycolysis metabolites, Krebs’-cycle metabolites, low molecular weight organic acids, organic amines, fatty acids, eicosanoids, steroids, bile acids, phospholipids, ceramides, triglycerides, peptides, vitamins, co-factors, among many others. A comprehensive coverage of the exposome, and xenometabolome

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including common drugs, food additives, environmental

pollutants and metabolites produced by gut microbiota will also be provided. Given the large numbers of samples to be analyzed and need for exceptional levels of reproducibility, new analytical protocols have had to be developed. This manuscript describes a significantly enhanced and augmented version of the NMR spectroscopy protocol that supersedes that published in 2007 for any large scale phenotyping projects utilizing the latest generation digital spectrometers 45. We consider that the previous version of the protocol is still suitable for small scale 6 ACS Paragon Plus Environment

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studies that do not rely on multiple batch analysis and where interstudy and interlaboratory comparisons are less important. FACTORS THAT NEED TO BE ADDRESSED IN LARGE METABOLIC PHENOTYPING STUDIES BY NMR SPECTROSCOPY NMR spectroscopy is intrinsically both quantitative and, with the current state of commerciallyavailable equipment, highly reproducible. With modern digital electronics-based machines exact parameter settings can be set but there are still other considerations and procedures required to obtain reproducible sample chemistries. Before performing the spectroscopic analysis, it is necessary to consider aspects such as sample numbers per group and randomization. If this is a pilot study, smaller sample numbers per group are often sufficient to identify trends between groups. In order to be able to validate results, sufficient sample numbers should be analysed to achieve statistically significant results (the numbers required can be estimated using multivariate power calculations 46,47

).

Generally, NMR-based metabolic studies of biofluids have shown very high reproducibility

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, but

new protocols linked to new technology as described herein has enabled even greater levels of reproducibility to be obtained without compromising the level of high throughput. Using current automation methods, samples can be kept chilled whilst awaiting analysis and problems with insertion of samples into the magnet are rare. It is important to keep aliquots of samples at the sample collection stage in order to be able to repeat acquisitions or for subsequent 2D NMR spectroscopy necessary for biomarker identification. Consideration has to be given to quality control at all stages of the phenotyping process., i.e. quality of the sample subject, quality of the study design, quality of the sample collection, quality of sample aliquoting, quality of the sample storage, quality of the preparation of samples for NMR, quality of the acquiring of data, quality of the upkeep of the parameters desired over time. Ultimately, to



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achieve a spectral snapshot of the metabolic content of a biofluid at the time of collection, care must be taken at each stage of sample handling to ensure a true indication of the biofluid content is observed and the typical work flow is captured in Figure 1. Conceptually, researches following the same protocol should then be able to analyze biofluid data on any modern 600 MHz NMR spectrometer to add to and draw from an expanding database of the human metabolic space. To facilitate information exchange it is planned to deposit all data sets into the Metabolights database (http://www.ebi.ac.uk/metabolights/). There are however many hurdles to overcome regarding permissions, ethical approvals, and data ownership before major deposits can become routine.

MATERIALS AND METHODS The composition of all reagents and their preparation, and the details of the NMR spectroscopic equipment are given in the Supplementary material. In addition the following samples should also be prepared. Study Reference: A study reference should be prepared by aliquoting 50 µL of each sample into a large glass beaker. The composite mix should be stirred for 10 min and then prepared as a regular sample (see Sample Preparation). It is advisable to run 2 or 3 study references for each 100 individual samples as it will ultimately provide an idea of the total variation across the preparation and analysis stages. Long Term Reference: A long term reference can be either produced in house or purchased externally. A long term reference should be a relatively large pool of biologically relevant quality control which has been aliquoted into smaller volumes. It should be prepared as usual (see Sample Preparation) and run across multiple studies (in the order of 1 long term reference per 100 individual samples). It will provide an idea of the analytical variation over a relatively long period of time.


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Biofluid sample preparation Urine: The collected human urine should be spun down and aliquoted into 2 mL cryo-vials. Aliquots should be not less than 700 µL. To avoid bacterial contamination it is advisable to add 0.05 % wt/vol of NaN3 water solution. Samples are stored frozen at -80 °C until analysed and always kept at 4 °C otherwise 37. Each urine sample is centrifuged at 12 000 g for 5 min at 4 °C. Urine samples are prepared into 96well plates for NMR spectroscopy using a Bruker Sample Track system and a Liquid Handler 215 preparation robot or similar. Alternatively, samples can be made up manually and the mixture should be transferred into a SampleJet NMR tube. See the Supplementary Material for details. Blood-derived samples: Preferably blood serum is collected into tubes with no additives (red cap BD vacutainer tubes or similar). Blood plasma should be collected in approximately 8 mL aliquots into Liheparin or EDTA containing tubes (BD vacutainer, Li-heparin or EDTA). It should be noted that collecting into EDTA tubes will produce high intensity NMR peaks from EDTA and its complexes with Mg2+ and Ca2+ ions, causing known signals overlapping with other small molecules and thus complicating the metabolic interpretation of certain areas of the spectrum. Collection into heparin tubes results in a spectrum which, although not obviously altered, contains broad heparin proton resonances that are not easily identifiable. These heparin signals complicate analysis of other resonances including characterisation of broad lipoprotein peaks into subclasses 48. For these processes, standard site-specific procedures should be used. EDTA and citrate based anticoagulants cause signals that will appear on the NMR spectrum which often subsequently must be removed during data processing causing valuable spectral regions to be left unanalysed. Also, collection tubes should be avoided that use gel to separate blood cells from plasma because gel might bind metabolites significantly varying the measured profile. Clotting time should be reduced to 30 min, and the sample should be kept on ice. Spin down the blood sample at 1600 g for 15 min



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and collect the supernatant. Samples are aliquoted into 1-2 mL cryo-vials with a minimum volume of 500 µL and store at -80 °C until analysed. Samples are stored at -80 °C for the entirety of the duration between collection and sample preparation. There ideally should be no freeze-thaw cycles of samples. Collecting several aliquots will prevent the freeze-thaw cycle. As soon as the samples are thawed the preparation and analysis should be carried out without refreezing. Further details on sample storage can be found in the literature

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. From this point onward serum will be treated

identically to plasma, unless otherwise stated. Plasma/serum samples are prepared into 96-well plates for NMR spectroscopy using a Liquid Handler 215 preparation robot or similar. Alternatively, samples can be made up manually into an Eppendorf and then transferred into a SampleJet NMR tube. See the Supplementary Material for details. Quality Controls: Samples should be randomized, and subsequently organized in groups of 95 or 94 samples. The type of randomization needs to be appropriate for the study design and whilst block randomisation is a commonly-used method, in the case of a longitudinal study for instance it is often not possible. Each 96-well plate should contain at least one quality control (QC) reference (if possible two or three). Ideally each rack contains composite QC samples that are composed of equal parts of all specimens within a large study and a long term reference (LTR) which is a QC sample composed of specimens which are completely independent of the study. A composite QC ultimately helps to elucidate intra-study variation in preparation and analysis while a LTR (if measured across many studies) can be used to elucidate inter-study variation in preparation or deterioration and in instrument analytical performance. Additionally, by replacing a sample with pure water, the preparation of a buffer blank elucidates whether the robot is behaving correctly and the buffer has been prepared without contamination. Obviously as more blanks/quality controls/references are added to a 96 rack this will decrease the amount of real samples analysed per 96 plate. Equipment set-up 10 ACS Paragon Plus Environment

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There are several aspects that need to be considered, namely, choice of spectrometer console, probe,

sample

changer,

temperature

control

and

optimum

operating

temperature,

setup/optimization of the instruments, long term instrumental drift and changes, software, and ultimately introduction of new products and technology. Whatever the equipment set up of choice, this must provide, in automation, highly reproducible experiments with very good resolution and no base-line distortion. The instrumentation illustrated here for running high throughput 1H NMR spectra consists of a Bruker Avance III console combined with a 14.1 T magnet for 1H 600 MHz at a temperature of 300 K. It is equipped with a 5 mm broad-band inverse configuration probe with a zaxis magnetic field-gradient capability. The instrument must have a Bruker SampleJet system set to 5 mm shuttle mode with a cooling rack of refrigerated tubes at 6°C. The data acquired is processed using Topspin 3.2 and run under automation by IconNMR (see Supplementary material for equipment details). High throughput studies by NMR spectroscopy rely heavily on both the instrument calibration before the analysis and the automated methods performed on each sample. It is then of paramount importance to ensure that every experiment has good water peak suppression and consistent magnetic field homogeneity (shim) quality. It is also important to optimize an automation routine that will ensure the high-throughput without compromising the quality of the spectra. Before a sample set is acquired the instrument must be calibrated to ensure particular parameters are met during the run (see Supplementary material, Figures S1 and S2). Sample Set-Up: One of the samples will be used to set up the parameters that are not automatically calculated, such as the centre of the frequency window for 1H spectroscopy and the shimming file which will be used afterwards as a base shimming file for each sample to be analyzed. It is advisable to use one of the composite QCs to do this set up. These updated NMR-based metabonomics protocols have been designed to optimize the



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automation parameters to ensure the rapid and efficient acquisition of NMR data. The parameters here described should be adopted by any user who wants to produce reproducible results and aims to acquire spectra that will be afterwards comparable with those obtained in the Phenome Centre The set of experiments that is acquired for each sample will still depend on the biochemical question that one wants to answer. The basic pulse sequences remain the same to those previously described in Beckonert et al. 2007

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. The methods for setting up normal one-pulse sequences (zgpr), 1D

nuclear Overhauser enhancement spectroscopy (NOESY)-presat (noesypr1d), CPMG-presat (cpmgpr), J-resolved (jresgpprqf) and diffusion-edited (ledbpgppr2s1d) experiments are given below. All the experiments here described follow the Bruker Biospin nomenclature. Other manufacturers have analogous pulse sequences to those described here. The current automation programs include not only the acquisition routine for locking, tuning and matching and shimming, pulse calibration and optimized presaturation power for individual samples, but also there are now automated routines for the processing that includes phasing, base line correction and calibration. These routines can be formulated in many different fashions, but they are essential for the analysis of large sample cohorts. This manuscript concentrates on the results expected to accurately analyze such a sample cohort. Sample Preparation: The volumes described apply for 5 mm NMR tubes. When using 3 mm NMR tubes, these volumes will need to be adjusted. A) Urine: i) In automation: 600 µL of each urine sample is added into a 96-well plate. The well plate is centrifuged at 1,800 g for 5 min to sediment insoluble material, before positioning in the NMR flowinjection Liquid Handler robot. The robot will mix 540 µL of sample with 60 µL of urine buffer (see Reagent Setup) into the NMR tube for a total volume of 600 µL. ii) Manually: Eppendorf tubes containing 600 µL of sample are centrifuged at 12,000 g for 5 min at 4 12 ACS Paragon Plus Environment

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°C and 540 µL of the supernatant is transferred into a SampleJet NMR tube along with 60 µL of urine buffer (see Reagent Setup) and mix well. The final concentration of sodium azide in urine samples should be 0.2 mM after preparation, whilst for plasma/serum it is 3.1 mM. iii) NMR tube caps with preparation holes are sealed with POM balls by pressing a ball firmly into the tube caps. B) Plasma/serum: i) Each plasma sample (at least 400 µL) is centrifuged at 12 000 g for 5 min at 4 °C. ii) In Automation: Samples are prepared into 96-well plates by adding 350 µL of sample to each well of the 96-well plate. 300 µL of sample is mixed with 300 µL of serum/plasma buffer (see Reagent Setup) into a SampleJet NMR tube. iii) Manually: Samples can be made up manually by adding 350 µL of sample and 350 µL of serum/plasma buffer (see Reagent Setup) to an Eppendorf tube. Eppendorf tubes are centrifuged at 12,000g for 5 min at 4 °C and 600 µL of the supernatant is transferred into a SampleJet NMR tube. iv) NMR tube caps with preparation holes are sealed with POM balls by pressing a ball firmly into the tube caps. For a detailed description of reagent preparation, equipment required, NMR calibration, experimental set up, optimal parameter settings and analytical quality assurance see Supplementary Material. RESULTS Timing A Liquid Handler 215 takes 3 h to prepare a 96 tube rack, including the addition of reagent to the sample, injection into the tube, and a mixing and cleaning procedure for each sample. For the NMR experiments, a throughput of 96 urine samples (excluding reruns) can be achieved within 24 h ( ~ 15 13 ACS Paragon Plus Environment


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min per sample) when running a NOESY-presat and JRES. Plasma and serum samples NMR data can be acquired at a rate of 72 samples (excluding reruns) per 24 h when running a NOESY-presat, CPMG, and JRES ( ~ 19 min a sample) spectrum set. An acceptable rate of reruns is of the order of 5 %. This time calculation (for urine and plasma/serum) does not include analysis of quality controls, references and blank samples which should be added to the timing. Unfortunately, as soon as a sample is thawed, measurable physical and chemical processes occur in biofluids (particularly plasma/serum) which are yet to be addressed for high throughput studies. Notwithstanding this is has been shown that storage of samples up to 36 h at 4 °C does not cause major changes in the small molecule profiles 50. For large sample cohorts the current methods are a compromise between the amounts of manual handling required, length of time before acquisition and accuracy of measured metabolite concentrations. Typical acceptable spectra A key facet of successfully undertaking metabolic phenotyping studies by spectroscopic analysis is that reproducibility is maintained and technical variation is minimized. It is well known that metabolic profiles of human biofluids are influenced by food intake, life style, sleep patterns, as well as different health conditions together with age, gender and ethnicity. Ideally, experimental preparation and analytical disparity should not be introduced to the variability factors. The aim here is to establish protocols that will ensure the reproducibility of sample preparation as well as the experimental data obtained from the same sample over time, across different instruments and across sites. The intended outcome is that studies of key biofluids, if run under such strict protocols, can be directly compared to any other data sets collected in a similar manner. The conceptual design of inter-laboratory comparison of biofluid studies opens up a realm of analytical capacity and statistical power unperceived before in the area of global metabolic profiling. Urine: NMR spectra prepared from the same urine should be superimposable from one another when overlaid. An example of this expected reproducibility is shown in Figure 2. The difference in 14 ACS Paragon Plus Environment

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the reference samples spectra can provide a measurable approach to the variation in shift and magnitude of each metabolites resonance. Since the allowable variation will vary from study to study, it is not possible to provide an absolute value for allowed variability, but this variation must be taken into consideration when interpreting the results. The NMR peaks of several small molecules which are sensitive to very small variations in buffer volumes and pH can be used to evaluate the reproducibility in sample preparation. Particularly histidine and 1-methylhistidine with their imidazole rings (both commonly present in urine samples) have pKa values very close to physiologically relevant pH values making their proton chemical shifts extremely sensitive small pH changes caused by preparation differences. Preparative and analytical variance is measured by the variation across a set of pooled quality control samples. The variation in separation after calculation of 10 principal components of quality control samples should be no more than 1% of the variance across a healthy population. (Figure 3). In a sample set of healthy urine samples there will exist a distribution of the peak resonance frequencies of all proton signals (each distribution different to one another) due to variations in the complex biochemical matrix and hence intermolecular interactions in each of the urine samples. Accurate preparation of samples should lead to minimal variation in the chemical shift of each metabolite resonance creating a dataset less prone to errors during multivariate analysis, alignment, binning, and metabolite identification (Figure 3). Consistency in the chemical shift of peaks has two main advantages; producing a spectral set in which it is easier to predict a given metabolite peak location. It also reduces the amount of overlap in the regions where resonances from metabolites with similar chemical shifts are expected to appear providing a spectral set with more predictable signal patterns. Plasma/serum: Similarly to urine, samples prepared of the same plasma or serum should be indistinguishable (apart from noise variance) from one another when overlaid (Figure 4). Chemical change with time is a factor in all biofluid analysis so obviously this should be monitored within



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sample sets by rerunning a small percentage of samples at recorded intervals. Particularly susceptible to chemical change (with varying temperature, pH or preparation accuracy) are the large lipoprotein assemblies present in plasma/serum samples. Furthermore, plasma/serum samples are vulnerable to physical change in a relatively small period of time as very low density lipoprotein (VLDL) and low density lipoprotein (LDL) slowly settle in the NMR tube and out of the spectrometer detection region. In a period of 24 hours there is a significant decrease in signal caused by large lipoproteins present in plasma/serum as a compositional gradient forms across the sample due to gravity. Subsequent analysis of the same sample after homogenisation causes the lipoprotein resonances intensities to essentially return to their original measured value. Any remaining discrepancy is inherently due to aging in the composition of the sample over the course of time (active enzymes can be present in plasma/serum), during the several heating cycles, and also during the subsequent homogenization procedure (Figure 4, inset). These results inherently show that for lipoprotein analysis, plasma/serum samples should be measured only once. Methods which aim to model features of the aliphatic region of plasma NMR spectra to predict lipoprotein fraction concentrations 51 are susceptible to such errors due to physical and chemical change in this complex matrix if the individual time between preparation and the analytical run is not kept consistent and to an absolute minimum.


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The NMR approaches detailed herein have now been employed to measure several thousand NMR spectra and we believe that they are fit for the purpose of application to large scale epidemiological cohorts or samples from repositories or bio-banks. The protocol is capable of being applied across multiple batches from various studies with minimal analytical variation, as shown previously by the authors

52

. The protocol is designed to enhance science through motivating ever greater inter-

laboratory compatibility, leading to an ever growing understanding of human health by metabolic mapping.

ACKNOWLEDGEMENTS The MRC-NIHR National Phenome Centre is supported by UK Medical Research Council (in association with National Institute of Health Research (England)) grant MC_PC_12025. The financial support of Bruker Biospin and the Waters Corporation is also gratefully acknowledged.

SUPPORTING INFORMATION AVAILABLE Technical details of the methods described within the manuscript are outlined in the supporting information. This information is available free of charge via the internet at http://pubs.acs.org/.



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FIGURE CAPTIONS

Figure 1. A typical sample workflow highlighting the key stages during which the biological ‘snapshot’ must be preserved in order for a biofluid NMR spectrum to reflect the biology.

Figure 2. A) Superposition of 8 representative 1D 1H NMR noesygppr1d spectra of a single human urine sample analysed after 8 separate preparations in 8 separate tubes across 2 × 600 MHz spectrometers. The level of analytical and preparative reproducibility anticipated throughout large cohort analysis is displayed. Constancy of chemical shifts for histidine (7.12 ppm) and 1methylhistidine (7.04 ppm) singlet resonances is a good indicator of acceptable sample preparation. B) Two separately prepared urine spectrum (blue and red) and the difference (green). Inset: A stacked plot of spectra.

Figure 3. The distance from the origin of a PCA analysis after 10 components were generated of a set of 2000 unaligned urine samples compared to the corresponding 40 pooled study samples for quality control.

Figure 4. Superposition of representative 1D 1H NMR noesygppr1d spectra of a human plasma sample analysed in 9 separate tubes across 3 × 600 MHz spectrometers. Inset: 1H NMR spectra of a single human plasma sample analysed 5 times (blue), reanalysed 5 times after 24 hours of settling due to gravity (green) and then homogenised and reanalysed another 5 times (red).


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(29) McPhail, M. J. W.; Shawcross, D.; Coltart, I.; Want, E. J.; Veselkov, K. A.; Crossey, M.; Willars, C.; Auzinger, G.; O'Grady, J.; Bernal, W.; Holmes, E.; Wendon, J. A.; Taylor-Robinson, S. D. Gut 2012, 61, A202-A203. (30) Lindon, J. C.; Nicholson, J. K.; Holmes, E.; Antti, H.; Bollard, M. E.; Keun, H. C.; Beckonert, O.; Ebbels, T. M. D.; Reily, M. D.; Robertson, D.; Stevens, G.; Luke, P.; Breau, A. P.; Cantor, G. H.; Bible, R. H.; Niederhauser, U.; Senn, H.; Schlotterbeck, G.; Sidelmann, U. G.; Laursen, S. M.; Tymiak, A.; Car, B. D.; Lehman-McKeeman, L.; Colet, J. M.; Loukaci, A.; Thomas, C. Toxicology and Applied Pharmacology 2003, 187, 137-146. (31) Lindon, J. C.; Keun, H. C.; Ebbels, T. M. D.; Pearce, J. M. T.; Holmes, E.; Nicholson, J. K. Pharmacogenomics 2005, 6, 691-699. (32) Holmes, E.; Li, J. V.; Marchesi, J. R.; Nicholson, J. K. Cell Metabolomics 2012, 16, 559-564. (33) Holmes, E.; Loo, R. L.; Stamler, J.; Bictash, M.; Yap, I. K. S.; Chan, Q.; Ebbels, T. M. D.; De Iorio, M.; Brown, I. J.; Veselkov, K. A.; Daviglus, M. L.; Kesteloot, H.; Ueshima, H.; Zhao, L.; Nicholson, J. K.; Elliott, P. Nature 2008, 453, 396-400. (34) Smith, L. M.; Maher, A. D.; Want, E. J.; Elliott, P.; Stamler, J.; Hawkes, G. E.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Analytical Chemistry 2009, 81, 4847-4856. (35) Loo, R. L.; Coen, M.; Ebbels, T. M. D.; Cloarec, O.; Maibaum, E. C.; Bictash, M.; Yap, I. K. S.; Elliott, P.; Stamler, J.; Nicholson, J. K.; Holmes, E. Analytical Chemistry 2009, 81, 5119-5129. (36) Lui, M.; Nicholson, J. K.; Lindon, J. C. Analytical Chemistry 1996, 68, 3370-3376. (37) Teahan, O.; Gamble, S.; Holmes, E.; Waxman, J.; Nicholson, J. K.; Bevan, C.; Keun, H. C. Analytical Chemistry 2006, 78, 4307-4318. (38) Keun, H. C.; Ebbels, T. M. D.; Antti, H.; Bollard, M. E.; Beckonert, O.; Schlotterbeck, G.; Senn, H.; Niederhauser, U.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Chemical Research in Toxicology 2002, 15, 1380-1386. (39) Dumas, M. E.; Maibaum, E. C.; Teague, C.; Ueshima, H.; Zhou, B.; Lindon, J. C.; Nicholson, J. K.; Stamler, J.; Elliott, P.; Chan, Q.; Holmes, E. Analytical Chemistry 2006, 78, 2199. (40) Bouatra, S.; Aziat, F.; Mandal, R.; Chi Guo, A.; Wilson, M. R.; Knox, C.; Bjorndahl, T. C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; Dame, Z. T.; Poelzer, J.; Huynh, J.; Yallou, F. S.; Psychogios, N.; Dong, E.; Bogumil, R.; Roehring, C.; Wishart, D. S. Public Library of Science One 2013, 8, e73076. (41) Mirnezami, R.; Kinross, J.; Vorkas, P. A.; R., G.; Holmes, E.; Nicholson, J. K.; Darzi, A. Annals of Surgery 2012, 255, 881-889. (42) Nicholson, J. K.; Everett, J. R.; Lindon, J. C. Expert Opinion on Drug Metabolism and Toxicology 2012, 8, 135-139. (43) Crockford, D. J.; Keun, H. C.; Smith, L. M.; Holmes, E.; Nicholson, J. K. Analytical Chemistry 2005, 77, 4556-4562. (44) Loo, R. L.; Coen, M.; Ebbels, T. M. D.; Cloarec, O.; Maibaum, E. C.; Bictash, M.; Yap, I. K. S.; Elliott, P.; Stamler, J.; Nicholson, J. K.; Holmes, E. a. f. t. I. R. G. Analytical Chemistry 2009, 81, 5119-5129. (45) Beckonert, O.; Keun, H. C.; Ebbels, T. M. D.; Bundy, J.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Nature Protocols 2007, 2, 2692-2703. (46) Chadeau-Hyam, M.; Ebbels, T. M. D.; Brown, I. J.; Chan, Q.; Stamler, J.; Huang, C. C.; Daviglus, M. L.; Ueshima, H.; Zhao, L.; Holmes, E.; Nicholson, J. K.; Elliott, P.; De Iorio, M. Journal of Proteome Research 210, 9, 4620-4627. (47) Nicholson, G.; Rantalainen, M.; Maher, A. D.; Li, J. V.; Malmodin, D.; Ahmadi, K. R.; Faber, J. H.; Hallgrimsdottir, I. B.; Barrett, A.; Toft, H.; Krestyaninova, M.; Viksna, J.; Neogi, S. G.; Dumas, M. E.; Sarkans, U.; Consortium, T. M.; Silverman, B. W.; Donnelly, P.; Nicholson, J. K.; Allen, M.; Zondervan, K. T.; Lindon, J. C.; Spector, T. D.; McCarthy, M. I.; Holmes, E.; Baunsgaard, D.; Holmes, C. C. Molecular Systems Biology 2011, 7, 1-12. (48) Ala-Korpela, M. Clinical Chemistry and Laboratory Medicine 2008, 46, 27-42. (49) Maher, A. D.; Zirah, S. F. M.; Holmes, E.; Nicholson, J. K. Analytical Chemistry 2007, 78, 52045211.


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(50) Barton, R.; Nicholson, J. K.; Elliot, P.; Holmes, E. International Journal of Epidemiology 2008, 37, 37-40. (51) Vehtari, A.; Mäkinen, V. P.; Soininen, P.; Ingman, P.; Mäkelä, S. M.; Savolainen, M. J.; Hannuksela, M. L.; Kaski, K.; Ala-Korpela, M. BMC Bioinformatics 2007, 8. (52) Keun, H.; Ebbels, T.; Antti, H.; Bollard, M.; Beckonert, O.; Schlotterbeck, G.; Senn, H.; Niederhauser, U.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Chemical Resonance Toxicology 2002, 15, 1380-1386.



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A typical sample workflow highlighting the key stages during which the biological ‘snapshot’ must be preserved in order for a biofluid NMR spectrum to reflect the biology. 134x137mm (300 x 300 DPI)

ACS Paragon Plus Environment

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A) Superposition of 8 representative 1D 1H NMR noesygppr1d spectra of a single human urine sample analysed after 8 separate preparations in 8 separate tubes across 2 × 600 MHz spectrometers. The level of analytical and preparative reproducibility anticipated throughout large cohort analysis is displayed. Constancy of chemical shifts for histidine (7.12 ppm) and 1-methylhistidine (7.04 ppm) singlet resonances is a good indicator of acceptable sample preparation. B) Two separately prepared urine spectrum (blue and red) and the difference (green). Inset: A stacked plot of spectra. 195x217mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

The distance from the origin of a PCA analysis after 10 components were generated of a set of 2000 unaligned urine samples compared to the corresponding 40 pooled study samples for quality control. 55x43mm (300 x 300 DPI)


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Superposition of representative 1D 1H NMR noesygppr1d spectra of a human plasma sample analysed in 9 separate tubes across 3 × 600 MHz spectrometers. Inset: 1H NMR spectra of a single human plasma sample analysed 5 times (blue), reanalysed 5 times after 24 hours of settling due to gravity (green) and then homogenised and reanalysed another 5 times (red). 205x134mm (300 x 300 DPI)



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TOC Graphic 134x137mm (300 x 300 DPI)


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Precision High-Throughput Proton NMR ... - ACS Publications

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