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Published in final edited form as: Curr Opin Biotechnol. 2013 Sep 14;0:51–59. doi: 10.1016/j.copbio.2013.08.004

Universal Quantitative NMR Analysis of Complex Natural Samples

Charlotte Simmler, José G Napolitano, James B McAlpine, Shao-Nong Chen, Guido F Pauli †,*
PMCID: PMC3912461  NIHMSID: NIHMS524986  PMID: 24484881

Abstract

Nuclear Magnetic Resonance (NMR) is a universal and quantitative analytical technique. Being a unique structural tool, NMR also competes with metrological techniques for purity determination and reference material analysis. In pharmaceutical research, applications of quantitative NMR (qNMR) cover mostly the identification and quantification of drug and biological metabolites. Offering an unbiased view of the sample composition, and the possibility to simultaneously quantify multiple compounds, qNMR has become the method of choice for metabolomic studies and quality control of complex natural samples such as foods, plants or herbal remedies, and biofluids. In this regard, NMR-based metabolomic studies, dedicated to both the characterization of herbal remedies and clinical diagnosis, have increased considerably.

Introduction

NMR is an essential analytical tool for the structure elucidation of unknown synthetic and natural compounds. Additionally, NMR has the inherent advantage of providing simultaneous access to both qualitative and quantitative information. The latter is defined by the primary ratio rule: the signal intensity is directly proportional to the number of nuclei that give rise to a specific resonance. Improvements in NMR instrumentation and technology such as shielded high-field magnets, cryoprobes, solvent suppression techniques, and versatile pulse sequences have led many investigators to further exploit NMR as a viable quantitative tool, despite its lower sensitivity (limit of detection (LOD) in the low μM range) when compared to mass spectrometry (MS, LOD in the low pM range). To date, quantitative NMR (qNMR) measurements are at least as reliable and precise as those obtained by the more commonly used chromatography-based techniques, while providing several advantages including simple method development, easy sample preparation, relatively short analysis times, and multiple calibration options without the need for identical reference materials (Figure 1).

Figure 1. Principal characteristics of qNMR compared to hyphenated chromatographic methods.

Figure 1

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The properties highlighted in green and red are advantages and drawbacks, respectively. NMR bears the specific and inherent advantage of universal detection related to the structural properties of a given analyte. The different detections hyphenated with analytical chromatography (e.g., ultraviolet, UV; mass spectrometry, MS; evaporating light scattering detection, ELSD; refractive index, RI; charged aerosol detector, CAD) rely on physical properties of the different analytes in a given sample, thus restricting the detection to a certain type of metabolites. These detection properties explain the main differences in calibration and quantitation between the methodologies. The universal detection and inherent quantitative nature of NMR allows the use of multiple calibration methods with non-structurally identical calibrants such as any certified reference material [1][2]. The capability to detect all metabolites in a sample also prevents qNMR from being a selective/specific analytical method, especially with regard to overlapping resonances. Additionally, qNMR suffers from relatively low sensitivity of detection, particularly when compared with MS detection. The reproducibility of hyphenated chromatography can be hampered at each level of the analytical system (i.e., sample injection, pumps, column efficiency, and calibration of the detector). Conversely, qNMR combines sample analysis and detection in a one-step process. §Provided that the molecules bear readily observable nuclei (e.g., 1H, 13C, 15N, 19F, 31P) *Physical restrictions: low boiling point and volatility (ELSD), lack of chromophores (UV), and ionization conditions (MS). HPLC: high pressure liquid chromatography, UHPLC: ultra-high pressure liquid chromatography, GC: gas chromatography, DNP: Dynamic Nuclear Polarization.

Among the major applications of one-dimensional (1D) quantitative proton (1H) NMR (qHNMR) are, the purity assessment of organic compounds and the identification of potential impurities. The latter is directly associated with the determination of their molar concentration in a given sample (Figure 2A). Likewise, qHNMR has become a valuable technique for the analysis of complex mixtures and the quantitation of multiple metabolites without the need for chromatographic separation (Figure 2B). Given its near universal detection capability, qHNMR offers an unbiased overview of the sample composition. This advantage is particularly useful for metabolomic studies, which represent a second major application of qHNMR (Figure 3).

Figure 2. Analyte identification, purity determination, and quantitation by qHNMR.

Figure 2

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Panel A exemplifies the determination of compound purity for kaempferol, which is linked to the calculation of its concentration, along with the identification and quantitation of impurities. The universal nature of NMR detection enables the identification not only of structurally related impurities (e.g., quercetin), but also of traces of solvents (e.g., methanol, MeOH; ethanol, EtOH; water), which cannot be readily determined by other detectors such as UV, MS or ELSD. All quantitative values are reported on a weight basis (weight analyte/weight sample). The inherent quantitative nature of NMR, along with its ability to detect all proton-bearing compounds in a given sample, explains why the purity determination of organic compounds follows the same principle as the identification and quantitation of multiple compounds in a mixture, such as a food/plant extracts (Panel B) or biofluids. The quantitation or purity determination is performed by measuring the normalized integrals of the 1H signals of the analyte of interest with respect to the integrals of an internal/external standard of known purity, which does not need to be structurally identical. The possibility of quantifying multiple compounds in a complex natural matrix is illustrated in Panel B with a mixture of ginkgolides [44].

Figure 3. qHNMR-based metabolomics/metabonomics.

Figure 3

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Panel A illustrates the general workflow of NMR-based metabolomic and metabonomic (metabolomic studies associated with a stress or disease state) studies. Quantitative 1H NMR facilitates high-throughput analysis by offering simple sample preparation, while providing an unbiased overview of the entire metabolome. Metabolite profiling enables the identification of multiples markers and, therefore, differs from the metabolite fingerprinting approach. Global changes in a set of NMR spectra are evaluated through multivariate data analysis. Among the multiple methods for pattern recognition and classification, the most widely used are unsupervised PCA (principal component analysis) and supervised PLS-DA (partial least-square discriminant analysis). Other methods are described in recent reviews [28][29][41]. In order to obtain reproducible results and to develop public spectral databases, the sample preparation, spectral acquisition, and data processing steps should all be standardized. Panel B exemplifies the importance of metabolic profiling as a comprehensive link between genotypes and phenotypes, providing a better understanding of gene function and expression, and facilitating the identification of both novel and known metabolites that correlate with changes in genotype or phenotype [37][38][39]. The ultimate aim of qNMR-based metabolomics/metabonomics is to gain a better understanding of biological systems, along with the potential identification of otherwise inaccessible compounds, such as chemically unstable compounds.

This review focuses on the recent advances in qNMR applications for identity and purity assessment, quality control (QC) and quality assurance, overall characterization of foods and herbal products, as well as human metabolomic studies. The surveyed literature covers the two years since our comprehensive review on the subject [1••].

Reporting the Purity of Molecules

Major applications of qNMR in pharmaceutical studies are linked to the purity determination of library compounds [3], with most studies targeting the evaluation of impurity levels along with structural information [4], degradation pathways, residual solvent, isomeric composition, and molar ratios [2••, 5•-6] (Figure 2A).

The guidance on the “Safety Testing of Drug Metabolites” issued by the U.S. Food and Drug Administration has highlighted the importance of identifying and characterizing drug metabolites as early as possible in the discovery and development processes. Traditionally, quantitation of drug metabolites eliminated in biological samples has been obtained through radioactivity counting. Strategies using qNMR have enabled a better definition of drug exposure, through the identification and quantitation of drug metabolites in pre-clinical animal studies [7].

The determination of the structure and concentration of new synthetic, non-commercially available drug metabolites by qHNMR also allows their subsequent use as reference standards in more sensitive LC-MS based assays [8]. Many drugs have been designed to include fluorine in order to enhance either their pharmacological effects or their pharmacokinetics properties. 19F has favorable intrinsic NMR properties, including the 100% natural isotopic abundance, and high sensitivity in NMR, measurements with very low background interference. Mutlib et al. have proposed a method to obtain the mass balance (recovery of drug related material) of fluorinated compounds by quantitative 19F NMR (qFNMR), without the use of radiolabelling [9•]. This method can be defined as an alternate and cost-saving way to obtain reliable mass balance results of all fluorinated compounds. While LC-MS-MS can be performed when the absorbed compounds are eliminated in an unchanged form, qFNMR was found to provide new insights when the administered compounds were extensively metabolized [9•].

Another emerging area of qHNMR applications is the characterization of complex biological drugs such as heparins, vaccines, and antibodies [1-2••, 10•]. Due to its high capacity to characterize carbohydrates, 1H-NMR has been applied in the identification and quantitation of antigens in vaccines, such as capsular polysaccharides. The monitoring of the antigen purity could, therefore, be performed by qHNMR during several stages of a vaccine's production [11].

Moreover, qHNMR has been demonstrated to be a competitive metrological technique for the purity certification of organic compounds, with a reliable estimation of chemical purity defined by an accuracy and precision of ±1%, and an uncertainty of measurement less than 0.1% [12•, 13•, 14]. In fact, qHNMR is powerful enough to be used as a reference method for the validation of other analytical chromatographic techniques [12•, 14]. The recognition of qHNMR as a reference method is exemplified by its application for qualitative and quantitative analysis of several compounds such as non-fractionated heparin sodium [15] in pharmacopeial methods [6, 12•].

While purity assessment by qHNMR is routinely used in the pharmaceutical industry, it is still not the case elsewhere, notably in food and related industries or in natural product research. Only very few articles have reported the use of qHNMR as a way to determine the purity of isolated plant metabolites, along with the identity of their impurities [16-18].

Qualitative and Quantitative Analysis of Food and Brewery Products

There is a growing interest in the use of qHNMR in food analysis due to its numerous advantages over routinely applied chromatographic analytical methods (Figure 1). The simplicity and rapidity of qNMR implementation have been demonstrated to advance food science and technology when studying metabolic and fermentation processes, composition of foods, or controlling manufacturing processes [19••]. Recent studies have highlighted the use of qHNMR for the in situ monitoring of fermentation processes in wine [20], milk [21], and tea [19••]; the absolute quantitation of preservatives benzoic and sorbic acids in processed food [22, 23]; the qualitative and quantitative evaluation of lipid composition in sea bass [24], and in processed pork meat sausages [25]. Two classes of primary metabolites were found to be the targets of qHNMR study in food products: on one hand, the water soluble organic acids, amino acids, and sugars, and on the other hand the fatty acids, which are altogether important for nutritional and organoleptic aspects [26]. All studies have demonstrated that qHNMR is accurate, reproducible, and specific enough, either for the quantitation of preservatives in complex matrices such as margarine, syrup, avocado paste, sausages [22, 23•], or for the determination of fatty acid chain compositions in food products [24, 25].

qHNMR-based Metabolomics and Chemometrics

Metabolite profiling and fingerprinting (Figure 3A) are two approaches aimed at the analysis of the metabolome (i.e., the entire set of metabolites) in a given biological system. Metabolomics has become one of the major applications of qHNMR. The importance of qNMR-based metabolomics is illustrated in comprehensive recent reviews [10••, 27•], and in other reviews addressing specific applications such as nutritional metabolomics [28••], the study of bacterial biofilms [29•], and the investigations of plant or herbal remedies [30•, 31••].

As qHNMR usually covers only the subset of the metabolome that exceeds a certain concentration threshold (1-5 μM), it is frequently combined with more sensitive analytical techniques (e.g., LC-MSn). Datasets that integrate both techniques have been demonstrated to give a broader coverage of the metabolome [28, 32••].

Metabolomics involve large amount of samples and the resulting qHNMR spectra are generally complex, with many overlapping signals. Accordingly, significant data processing through multivariate data analysis combined with statistics is typically required. Chemometric methods are necessary to extract and describe spectral differences and to evaluate global changes in large sets of NMR spectra for group classification (i.e., pattern recognition). The most widely used pattern recognition methods are supervised partial least squares-discriminant analysis (PLS-DA) and unsupervised principal component analysis (PCA) [2, 10, 27-29].

Metabolic flux measurement involves time-resolved metabolite fingerprinting acquired by qNMR and provides complementary information to “static” metabolomics through the characterization of the flow of metabolites in biochemical pathways [10•]. Moreover, with the development of dynamic nuclear polarization (DNP), i.e., the transfer of electron spin polarization to nuclear spin polarization, quantitative metabolic flux experiments have become more sensitive by as much as six orders of magnitude, although still requiring specialized equipment (e.g., HyperSense from Oxford Instruments, or DNP-NMR, developed by Bruker)[33••].

Metabolomics, Quality Control, and Drug Discovery from Natural Products

In natural products research, qHNMR has become one of the most suitable techniques for carrying out comprehensive qualitative and quantitative analysis [34-36•]. This includes an increased use of qHNMR-based plant metabolomic methods for the QC of herbal remedies [30], chemotaxonomic studies [36•], analysis of genetically modified plants [37•-38], and the (q)NMR-driven discovery of new metabolites [39-40]. Details of plant metabolomic [31, 32••] and chemometric methods [41] applied to the study of herbal products have been recently reviewed.

Metabolomics has been regarded as the bridge between genotype and phenotype. Therefore, qNMR-based metabolomics has been used as a phenotyping or functional genotyping tool for the investigation of transgenic organisms to discover metabolites associated with a specific phenotype or genotype [42•]. Evaluation of 1H NMR metabolic fingerprints (1D and 2D) appears to be a powerful approach, revealing the entire metabolome of an altered phenotype resulting from mutations [37•-38] (Figure 3B). The concept of qHNMR-based metabolomic mining has recently been introduced, and applies qHNMR as a tool to expedite dereplication and partial identification of new marker compounds at each step of the natural product discovery (extraction, fractionation, and final isolation) [18, 40].

In the field of botanical dietary supplements (BDSs), it becomes increasingly recognized that herbal remedies, or botanicals, exert their health effects as a whole rather than by virtue of a few constituents [30•, 31••]. Hence, analytical protocols for the characterization and authentication of extracts must cover complex mixtures rather than single chemicals. In response, qHNMR spectroscopy, offering a holistic view of the extract composition, enables a comprehensive characterization of botanicals, allowing the detection and quantitation of multiple active compounds in one experiment [34-35] (Figure 2B).

The principal challenge in 1D qHNMR spectroscopy of plant extracts is the extensive resonance overlap, which frequently obstructs the identification and quantitation of metabolites. To overcome this shortcoming, several studies resort to the use of a fractionation/enrichment step, reducing the sample complexity prior to qHNMR analysis. Although these approaches lead to sample alteration, they enable the observation of otherwise undetected metabolites, usually present in trace amounts [34-35]. A more general approach to optimize 1D-qHNMR specificity is the use of 1H NMR fingerprints obtained through iterative full spin analysis (HiFSA) in combination with multi-signal integrations for the quantitation of several compounds [43-44•].

Human Metabolomics: from diagnosis to the follow-up of patients

Biofluids such as urine and plasma are easy-to-obtain samples, do not require extraction from tissue and, in most cases, can be analyzed without additional concentration steps. These advantages make biofluids most accessible for metabolomic analysis. A description of a standardized sample preparation protocol for different biofluids and mammalian tissues has been provided by Beckonert et al.[45], and has been summarized in recent reviews [10, 28, 46••]. The importance of qHNMR-based human metabolomics (including metabonomics) has increased considerably in the last few years because it provides a means of differentiating between disease and healthy states, or between drug-treated and untreated groups. Furthermore, qNMR analysis has been successfully applied in the identification of disease-related biomarkers [10, 46••-47].

Nutritional metabolomics has been applied to the study of the human metabolome as a function of nutritional status or challenge. Therefore, nutritional metabolomics aim at investigating biomarkers of food intake and lifestyle diseases. In contrast to other analytical methods, NMR has a unique property for measuring lipoprotein and chylomicron triglycerides content in blood samples due to its sensitivity to physical phenomena such as diffusion and rotational motion. The types of molecules in these studies are of high importance for diagnosis or monitoring of diseases related to nutritional disorders and dyslipidemia [28••, 48].

Intact tissues can be directly analyzed, without elaborate sample preparation, using high-resolution magic angle spinning (HR-MAS) NMR [49]. Ex vivo HR-MAS NMR provides solution-like spectra, allowing for spatial resolution of metabolites and pharmaceuticals, hence offering a complementary approach to histology. As such, HR-MAS NMR has been demonstrated to be a powerful technique for the identification and quantitation of different cancer biomarkers, as well as for the classification and prognosis of various tumors, and the evaluation of treatment efficacy [46••, 50].

Conclusions

The capability to perform concurrent identification and quantitation of impurities and other organic compounds makes qHNMR a unique analytical tool. Additionally, its quantitative capabilities can compete with established metrological techniques for purity determination. Quantitative 1H and 19F NMR dominate qNMR in pharmaceutical and forensic applications, where it is recognized as the method of choice for the identification and purity evaluation of biological and drug metabolites. Although qFNMR is a promising technique for the determination of metabolized fluorine drugs, it may take additional studies before it is accepted as an alternative method to expensive radioactivity-based assays in early pharmacokinetics [9•].

Compared to classical analytical methods, qHNMR offers several advantages (Figure 1): it provides an inherently quantitative, unbiased overview of the sample composition; it allows for rapid method implementation and simultaneous quantification of several metabolites, with the convenient choice of a wide variety of calibrants. While qHNMR is commonly used in pharmaceutical research and is relatively recognized in natural product research, reports of its application in other fields are only beginning. In this regard, the question remains whether the cost/availability of NMR instruments and the need for NMR expertise prevent the use of this powerful analytical technique.

While having matured considerably in the last decade, qHNMR continues to face several challenges, in particular its relatively low sensitivity, and the lack of selectivity/specificity caused by the inherent resonance overlap (Figure 1). The first can be overcome by the use of orthogonal LC-MS methods and the use of DNP technology [33••]. The use of computational tools for the analysis of complex resonance patterns and the development of quantitative 2D NMR experiments hold the promise of overcoming the second shortcoming [1••][12•].

NMR-based metabolomics is regarded as a potent tool for studying metabolism and biochemistry of organisms. In fact, the application of NMR techniques to identify new markers and differentiate closely related biochemical groups has grown significantly. Despite the wide varieties of organisms studied, mammalian and plant systems have been the most frequently examined by qHNMR. Given the existence of public MS/NMR spectral databases and the possibility to determine multiple biological markers in clinical samples, the importance of NMR-based human metabolomics has increased dramatically. Similarly, the influence of qNMR-based plant metabolomics will benefit markedly by further development of spectral databases for plant secondary metabolites.

In summary, the use of a nearly universal and easily calibrated quantitative detector makes qNMR a versatile technique with a broad range of applications for purity determination, metabolomic studies, multi-marker quantitation and quality control of samples involving or derived from complex natural matrices (Figure 4).

Figure 4. Sample varieties and panel of qNMR applications.

Figure 4

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The current applications of qNMR can be divided in two main groups: (1) absolute quantitation and purity determination of organic compounds (drugs, primary metabolites, natural products); and (2) metabolomics and quantitation of multiple analytes in complex natural matrices (e.g., food, botanicals, biofluids). Essentially all types of metabolites (e.g., sugars, fatty acids, organic acids, steroids) can be detected by NMR, explaining why a wide range of samples can be investigated. Therefore, qNMR applications cover the certification of purity, the identification and quantitation of drug metabolites, the quality control of food products and herbal remedies, the identification of biomarkers in complex natural matrices (e.g., herbal mixtures, biofluids), and finally clinical diagnosis.

Highlights.

  • qNMR is a powerful analytical tool for structure and purity determination

  • qNMR is suitable for the quality control of complex natural samples

  • qNMR and MS-hyphenated chromatography are complementary orthogonal techniques

  • qNMR has a strong track record for metabolomic studies of biological systems

  • NMR-based metabolomics is a diagnostic tool for human diseases

Acknowledgments

The authors are grateful to their colleagues, Drs. Tanja Gödecke, Birgit U. Jaki, and David C. Lankin, for helpful discussions of qNMR topics. Financial support by NIH through grants P50AT000155 by NCCAM and ODS as well as RC2AT005899 by NCCAM are also kindly acknowledged.

Footnotes

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