Recommended strategies for spectral processing and post-processing of 1D <Superscript>1</Superscript>H-NMR data of biofluids with a particular focus on urine

¹H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.     

Quantitative analysis of metabolite concentrations in human urine samples using 13C{1H} NMR spectroscopy

Targeted profiling is a library-based method of using mathematically modeled reference spectra for quantification of metabolite concentrations in NMR mixture analysis. Metabolomics studies of biofluids, such as urine, represent a highly complex problem in this area, and for this reason targeted profiling of ¹H NMR spectra can be hampered. A number of the issues relating to ¹H NMR spectroscopy can be overcome using ¹³C{¹H} NMR spectroscopy. In this work, a ¹³C{¹H} NMR database was created using Chenomx NMR Suite, incorporating 120 metabolites. The ¹³C{¹H} NMR database was standardized through the analysis of a series of metabolite solutions containing varying concentrations of 19 distinct metabolites, where the metabolite concentrations were varied across a range of values including biological ranges. Subsequently, the NMR spectra of urine samples were collected using ¹³C{¹H} NMR spectroscopy and profiled using the ¹³C{¹H} NMR library. In total, about 30 metabolites were conclusively identified and quantified in the urine samples using ¹³C{¹H} NMR targeted profiling. The proton decoupling and larger spectral window provided easier identification and more accurate quantification for specific classes of metabolites, such as sugars and amino acids with overlap in the aliphatic region of the ¹H NMR spectrum. We discuss potential application areas in which ¹³C{¹H} NMR targeted profiling may be superior to ¹H NMR targeted profiling.     

Methodology for the identification of the urinary metabolites of the analgesic-narcotic antagonist, codorphone, by gas chromatography mass spectrometry

Methodology is presented for the identification of codorphone and its metabolites in urine samples using gas chromatography mass spectrometry. The procedure focuses on the clean-up of biological samples and a derivatization technique suitable for these samples. Sep-Pak C-18 cartridges were employed in the clean-up procedure permitting the biological sample to be derivatized in a relatively small volume of reagents. The derivatization procedure incorporated a one-step trimethylsilyloxime reaction to prevent enol formation while simultaneously derivatizing free hydroxyl groups with the excess trimethylsilylimidazole present in the reaction mixture. This was followed by the addition of BSTFA directly to this reaction mixture to complete derivatization of any metabolites possessing dealkylation of the nitrogen. Using this derivatization scheme, synthetic metabolites were analyzed by gas chromatography mass spectrometry, and their mass spectra were characterized emphasizing the diagnostic fragment ions observed in the spectra. To illustrate the usefulness of this methodology, a urine sample obtained from a dog that had been dosed with codorphone was analyzed by gas chromatography mass spectrometry, and the metabolites were identified by comparison to the mass spectra of the synthetic derivatives.     

Use of the Local False Discovery Rate for Identification of Metabolic Biomarkers in Rat Urine Following Genkwa Flos-Induced Hepatotoxicity

Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR) and HPLC/MS (high-performance liquid chromatography with mass spectrometry). Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR) is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF) treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA), LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.     

Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry

The role of urinary metabolic profiling in systems biology research is expanding. This is because of the use of this technology for clinical diagnostic and mechanistic studies and for the development of new personalized health care and molecular epidemiology (population) studies. The methodologies commonly used for metabolic profiling are NMR spectroscopy, liquid chromatography mass spectrometry (LC/MS) and gas chromatography-mass spectrometry (GC/MS). In this protocol, we describe urine collection and storage, GC/MS and data preprocessing methods, chemometric data analysis and urinary marker metabolite identification. Results obtained using GC/MS are complementary to NMR and LC/MS. Sample preparation for GC/MS analysis involves the depletion of urea via treatment with urease, protein precipitation with methanol, and trimethylsilyl derivatization. The protocol described here facilitates the metabolic profiling of ～400-600 metabolites in 120 urine samples per week.     

Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots

Background

Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in ¹H NMR spectra has previously been successfully employed. Similar correlation of 2D ¹H-¹³C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).

Results

From 50 ¹H-¹³C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.

Conclusions

Correlation plots prepared by statistically correlating ¹H-¹³C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0413-z) contains supplementary material, which is available to authorized users.     

Metabolic profiling of gender: Headspace-SPME/GC–MS and <Superscript>1</Superscript>H NMR analysis of urine

This study aims to investigate the metabolic difference between male and female healthy adults using a combination of GC–MS and NMR metabolomics techniques. While metabolomics has shown wide applications in characterizing the status and progression of many diseases, physiological factors such as gender often contribute high levels of variability that can hinder the detection of biomarkers of interest, such as in disease detection. We carried out a detailed exploration of gender related metabolic profiling of human urine using a Headspace-SPME/GC–MS approach and detected over two hundred peaks. Fifty-nine metabolites were identified using the NIST library. ¹H NMR spectroscopy was also utilized, and resulted in the identification of eighteen metabolites. We find that both GC–MS and NMR are able to capture human gender metabolic differences, and their combination allows a significantly better understanding of this difference. Subtle differences between genders are found to be related to the metabolism of fats, amino acids, and TCA cycle intermediates.     

Characterization of data analysis methods for information recovery from metabolic 1H NMR spectra using artificial complex mixtures

The assessment of data analysis methods in ¹H NMR based metabolic profiling is hampered owing to a lack of knowledge of the exact sample composition. In this study, an artificial complex mixture design comprising two artificially defined groups designated normal and disease, each containing 30 samples, was implemented using 21 metabolites at concentrations typically found in human urine and having a realistic distribution of inter-metabolite correlations. These artificial mixtures were profiled by ¹H NMR spectroscopy and used to assess data analytical methods in the task of differentiating the two conditions. When metabolites were individually quantified, volcano plots provided an excellent method to track the effect size and significance of the change between conditions. Interestingly, the Welch t test detected a similar set of metabolites changing between classes in both quantified and spectral data, suggesting that differential analysis of ¹H NMR spectra using a false discovery rate correction, taking into account fold changes, is a reliable approach to detect differential metabolites in complex mixture studies. Various multivariate regression methods based on partial least squares (PLS) were applied in discriminant analysis mode. The most reliable methods in quantified and spectral ¹H NMR data were PLS and RPLS linear and logistic regression respectively. A jackknife based strategy for variable selection was assessed on both quantified and spectral data and results indicate that it may be possible to improve on the conventional Orthogonal-PLS methodology in terms of accuracy and sensitivity. A key improvement of our approach consists of objective criteria to select significant signals associated with a condition that provides a confidence level on the discoveries made, which can be implemented in metabolic profiling studies.     

Novel sample preparation method facilitating identification of urinary drug metabolites by liquid chromatography–tandem mass spectrometry

A simple, efficient procedure was developed for the preparation of urine samples, which greatly facilitated the identification of the urinary metabolites of a new antifungal agent SYN-2836. The urine samples following dilution with acetonitrile (ACN) formed distinct upper (ACN) and lower (aqueous) solution phases. The SYN-2836 metabolites were concentrated in the upper solution except that two glucuronides were concentrated in the lower solution. The upper solutions, containing concentrated metabolites and significantly reduced endogenous polar species, were ideally suitable for the metabolite identification. This novel sample preparation procedure would be applicable in identification of urinary metabolites of other drugs and drug candidates.     

Structural identification of methyl protodioscin metabolites in rats' urine and their antiproliferative activities against human tumor cell lines

Methyl protodioscin (MPD), a furostanol saponin, is a preclinical drug shown potent antiproliferative activities against most cell lines from leukemia and solid tumors. The metabolites of MPD in rats' urine after single oral doses of 80 mg/kg were investigated in this research. Ten metabolites were isolated and purified by liquid-liquid extraction, open-column chromatography, medium-pressure liquid chromatography, and preparative high-performance liquid chromatography. The structural identification of the metabolites was carried out by high resolution mass spectra, NMR spectroscopic methods including (1)H NMR, (13)C NMR and 2D NMR, as well as chemical ways. The 10 metabolites were elucidated to be dioscin (M-1), pregna-5,16-dien-3beta-ol-20-one-O-alpha-l-rhamnopyranosyl-(1-->2)-[alpha-l-rhamnopyranosyl-(1-->4)]-beta-d-glucopyranoside (M-2), diosgenin (M-3), protobioside (M-4), methyl protobioside (M-5), 26-O-beta-d-glucopyrannosyl(25R)-furan-5-ene-3beta, 22alpha, 26-trihydroxy-3-O-alpha-l-rhamnopyranosyl-(1-->4)-beta-d-glucopyranoside(M-6),26-O-beta-d-glucopyranosyl(25R)-furan-5-ene-3beta,26-dihydroxy-22-methoxy-3-O-alpha-l-rhamnopyranosyl-(1-->4)-beta-d-glucopyranoside (M-7), prosapogenin A of dioscin (M-8), prosapogenin B of dioscin (M-9), and diosgenin-3-O-beta-d-glucopyranoside (M-10), respectively. M-1 was the main urinary metabolite of MPD in rats. Some metabolites showed potent antiproliferative activities against HepG2, NCI-H460, MCF-7 and HeLa cell lines in vitro.     

Rapid identification of Candida species by using nuclear magnetic resonance spectroscopy and a statistical classification strategy

Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a statistical classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms.     

Automated screening for metabolites in complex mixtures using 2D COSY NMR spectroscopy

One of the greatest challenges in metabolomics is the rapid and unambiguous identification and quantification of metabolites in a biological sample. Although one-dimensional (1D) proton nuclear magnetic resonance (NMR) spectra can be acquired rapidly, they are complicated by severe peak overlap that can significantly hinder the automated identification and quantification of metabolites. Furthermore, it is currently not reasonable to assume that NMR spectra of pure metabolites are available a priori for every metabolite in a biological sample. In this paper we develop and report on tests of methods that assist in the automatic identification of metabolites using proton two-dimensional (2D) correlation spectroscopy (COSY) NMR. Given a database of 2D COSY spectra for the metabolites of interest, our methods provide a list sorted by a heuristic likelihood of the metabolites present in a sample that has been analyzed using 2D COSY NMR. Our models attempt to correct the displacement of the peaks that can occur from one sample to the next, due to pH, temperature and matrix effects, using a statistical and chemical model. The correction of one peak can result in an implied correction of others due to spin–spin coupling. Furthermore, these displacements are not independent: they depend on the relative position of functional groups in the molecule. We report experimental results using defined mixtures of amino acids as well as real complex biological samples that demonstrate that our methods can be very effective at automatically and rapidly identifying metabolites.     

Urine stability for metabolomic studies: effects of preparation and storage

Metabolomic studies attempt to identify and profile unique metabolic differences among test populations, which may be correlated with a specific biological stress or pathophysiology. Due to the ease of collection and the metabolite-rich nature of urine, it is frequently used as a bio-fluid for human and animal metabolic studies. High-resolution ¹H-NMR is an analytical tool used to qualitatively and quantitatively identify metabolites in urine. Urine samples were collected from healthy male and female subjects and prepared: raw, following centrifugation, filtration, or the addition of the bacteriostatic preservative sodium azide and analyzed by NMR. In addition, these samples were stored at room temperature (22 °C), in a refrigerator (4 °C), or in a deep-freeze (−80 °C). Samples were analyzed by NMR every week for a month and changes in concentrations of 55 easily identifiable metabolites were followed. The degree of change in metabolite concentrations following storage over a 4-week period were influenced by the different methods of sample preparation and storage. Significant changes in urine metabolites are likely due to bacterial contamination of the urine. Our study demonstrates that bacterial contamination of urine in normal individuals significantly alters the metabolic profile of urine over time and proper preparation and storage procedures must be followed to reduce these changes. By identifying appropriate methods of urine preparation and storage investigators will preserve the fidelity of the urine samples in order to better reflect the original metabolic state.     

The NMR study of human biological fluids for detection of pathologies NMR study of biological fluids

The paper deals with the NMR spectra obtained using preparations of five different human biological body fluids. Characteristic metabolite signals of blood, urine, tears, saliva, and sweat spectra have been determined and classified. The biological body fluid samples were used for search and identification of biomarkers of cardiovascular disease. Absolute functional biomarkers for diseases such as coronary heart disease (CHD) have not been recognized even in the case acute myocardial infarction. A hypothesis explaining reasons of lack of such markers has been formulated. The results of comparative analysis of blood and urine samples from humans and some laboratory animals are given. Identify and analyze signals of metabolites of pathogenic microflora and their dynamics in the urine from patients with urogenital diseases have been determined and analyzed and characteristic biomarkers have been recognized.     

Structural characterization of metabolites of salvianolic acid B from Salvia miltiorrhiza in normal and antibiotic-treated rats by liquid chromatography-mass spectrometry

This study was conducted to compare the in vivo metabolites of salvianolic acid B (Sal B) between normal rats and antibiotic-treated rats and to clarify the role of intestinal bacteria on the absorption, metabolism and excretion of Sal B. A valid method using LC-MS(n) analysis was established for identification of rat biliary and fecal metabolites. And isolation of normal rat urinary metabolites by repeated column chromatography was applied in this study. Four biliary metabolites and five fecal metabolites in normal rats were identified on the basis of their MS(n) fragmentation patterns. Meanwhile, two normal rat urinary metabolites were firstly identified on the basis of their NMR and MS data. In contrast, no metabolites were detected in antibiotic-treated rat urine and bile, while the prototype of Sal B was found in antibiotic-treated rat feces. The differences of in vivo metabolites between normal rats and antibiotic-treated rats were proposed for the first time. Furthermore, it was indicated that the intestinal bacteria showed an important role on the absorption, metabolism and excretion of Sal B. This investigation provided scientific evidence to infer the active principles responsible for the pharmacological effects of Sal B.     

Can Urine Metabolomics Be Helpful in Differentiating Neuropathic and Nociceptive Pain? A Proof-of-Concept Study

The diagnosis of pain nature is a troublesome task and a wrong attribution often leads to an increase of costs and to avoidable pharmaceutical adverse reactions. An objective and specific approach to achieve this diagnosis is highly desirable. The aim of this work was to investigate urine samples collected from patients suffering from pain of different nature by a metabolomics approach based on ¹H NMR spectroscopy and multivariate statistical analysis. We performed a prospective study on 74 subjects: 37 suffering from pain (12 with nociceptive and 25 with neuropathic pain), and 37 controls not suffering from any kind of chronic pain. The application of discriminant analysis on the urine spectral profiles allowed us to classify these two types of pain with high sensibility and specificity. Although the classification relies on the global urine metabolic profile, the individual contribution in discriminating neuropathic pain patients of metabolites such as choline and phosphocholine, taurine and alanine, suggests potential lesions to the nervous system. To the best of our knowledge, this is the first time that a urine metabolomics profile is used to classify these two kinds of pain. This methodology, although based on a limited sample, may constitute the basis for a new helpful tool in the clinical diagnosis.     

NMR and plant metabolism

Recent advances in NMR methodology offer a way to acquire a comprehensive profile of a wide range of metabolites from various plant tissues or cells. NMR is a powerful approach for plant metabolite profiling and provides a capacity for the dynamic exploration of plant metabolism that is virtually unmatched by any other analytical technique.     

Method development in high-performance liquid chromatography for high-throughput profiling and metabonomic studies of biofluid samples

"Metabonomics" has in the past decade demonstrated enormous potential in furthering the understanding of, for example, disease processes, toxicological mechanisms, and biomarker discovery. The same principles can also provide a systematic and comprehensive approach to the study of food ingredient impact on consumer health. However, "metabonomic" methodology requires the development of rapid, advanced analytical tools to comprehensively profile biofluid metabolites within consumers. Until now, NMR spectroscopy has been used for this purpose almost exclusively. Chromatographic techniques and in particular HPLC, have not been exploited accordingly. The main drawbacks of chromatography are the long analysis time, instabilities in the sample fingerprint and the rigorous sample preparation required. This contribution addresses these problems in the quest to develop generic methods for high-throughput profiling using HPLC. After a careful optimization process, stable fingerprints of biofluid samples can be obtained using standard HPLC equipment. A method using a short monolithic column and a rapid gradient with a high flow-rate has been developed that allowed rapid and detailed profiling of larger numbers of urine samples. The method can be easily translated into a slow, shallow-gradient high-resolution method for identification of interesting peaks by LC-MS/NMR. A similar approach has been applied for cell culture media samples. Due to the much higher protein content of such samples non-porous polymer-based small particle columns yielded the best results. The study clearly shows that HPLC can be used in metabonomic fingerprinting studies.     

Variation of metabolites in normal human urine

Urine is often sampled from patients participating in clinical and metabolomic studies. Biological homeostasis occurs in humans, but little is known about the variability of metabolites found in urine. It is important to define the inter- and intra-individual metabolite variance within a normal population before scientific or clinical conclusions are made regarding different pathophysiologies. This study investigates the variability of selected urine metabolites in a group of 60 healthy men and women over a period of 30 days. To monitor individual variation, 6 women from the normal population were randomly selected and followed for 30 days. To determine the influence of extraneous environmental factors urine was collected from 25 guinea pigs with similar genetics, diet, and living environment. For both studies, 24 metabolites were identified and quantified using high-resolution ¹H nuclear magnetic resonance spectroscopy (NMR). The data demonstrated large inter and intra-individual variation in metabolite concentrations in both normal human and control animal populations. A defined normal baseline is essential before any conclusions may be drawn regarding changes in urine metabolite concentrations.     

Testing for fluoxymesterone (Halotestin) administration to man: identification of urinary metabolites by gas chromatography-mass spectrometry

Fluoxymesterone, an anabolic steroid, is metabolized in man primarily by 6 beta-hydroxylation, 4-ene-reduction, 3-keto-reduction, and 11-hydroxy-oxidation. These pathways of metabolism are suggested by the positive identification of 4 metabolites and the tentative identification of 3 other metabolites. Detection of the drug in urine is possible for at least 5 days after a single 10 mg oral dose to previously untreated adult males, by monitoring the presence of 2 metabolites, since the parent drug is not detectable more than 1 day after the dose.