Analytical technologies for metabonomics and metabolomics, and multi-omic information recovery

New opportunities from the cancer metabolome

BACKGROUND

Metabolomics, the study of all metabolites produced in the body, which often includes flora and drug metabolites, is the omics approach that can be considered most closely related to a patient's phenotype. Metabolomics has a great and largely untapped potential in the field of oncology, and the analysis of the cancer metabolome to identify biofluid markers and novel druggable targets can now be undertaken in many research laboratories.

CONTENT

The cancer metabolome has been used to identify and begin to evaluate potential biomarkers and therapeutic targets in a variety of malignancies, including breast, prostate, and kidney cancer. We discuss the several standard techniques for metabolite separation and identification, with their potential problems and drawbacks. Validation of biomarkers and targets may entail intensive use of labor and technology and generally requires a large number of study participants as well as laboratory validation studies. The field of pharmacometabolomics, in which specific therapies are chosen on the basis of a patient's metabolomic profile, has shown some promise in the translation of metabolomics into the arena of personalized medicine.

SUMMARY

The relatively new approach using metabolomics has just begun to enter the mainstream of cancer diagnostics and therapeutics. As this field advances, metabolomics will take its well-deserved place next to genomics, transcriptomics, and proteomics in both clinical and basic research in oncology.

Systematic evaluation of extraction methods for multiplatform-based metabotyping: application to the Fasciola hepatica metabolome

Combining data from multiple analytical platforms is essential for comprehensive study of the molecular phenotype (metabotype) of a given biological sample. The metabolite profiles generated are intrinsically dependent on the analytical platforms, each requiring optimization of instrumental parameters, separation conditions, and sample extraction to deliver maximal biological information. An in-depth evaluation of extraction protocols for characterizing the metabolome of the hepatobiliary fluke Fasciola hepatica, using ultra performance liquid chromatography and capillary electrophoresis coupled with mass spectroscopy is presented. The spectrometric methods were characterized by performance, and metrics of merit were established, including precision, mass accuracy, selectivity, sensitivity, and platform stability. Although a core group of molecules was common to all methods, each platform contributed a unique set, whereby 142 metabolites out of 14,724 features were identified. A mixture design revealed that the chloroform:methanol:water proportion of 15:59:26 was globally the best composition for metabolite extraction across UPLC-MS and CE-MS platforms accommodating different columns and ionization modes. Despite the general assumption of the necessity of platform-adapted protocols for achieving effective metabotype characterization, we show that an appropriately designed single extraction procedure is able to fit the requirements of all technologies. This may constitute a paradigm shift in developing efficient protocols for high-throughput metabolite profiling with more-general analytical applicability.

1H Nuclear Magnetic Resonance (NMR) Metabolomic Study of Chronic Organophosphate Exposure in Rats

1H NMR spectroscopy and chemometric analysis were used to characterize rat urine obtained after chronic exposure to either tributyl phosphate (TBP) or triphenyl phosphate (TPP). In this study, the daily dose exposure was 1.5 mg/kg body weight for TBP, or 2.0 mg/kg body weight for TPP, administered over a 15-week period. Orthogonal signal correction (OSC) -filtered partial least square discriminant analysis (OSC-PLSDA) was used to predict and classify exposure to these organophosphates. During the development of the model, the classification error was evaluated as a function of the number of latent variables. NMR spectral regions and corresponding metabolites important for determination of exposure type were identified using variable importance in projection (VIP) coefficients obtained from the OSC-PLSDA analysis. As expected, the model for classification of chronic (1.5-2.0 mg/kg body weight daily) TBP or TPP exposure was not as strong as the previously reported model developed for identifying acute (15-20 mg/kg body weight) exposure. The set of majorly impacted metabolites identified for chronic TBP or TPP exposure was slightly different than those metabolites previously identified for acute exposure. These metabolites were then mapped to different metabolite pathways and ranked, allowing the metabolic response to chronic organophosphate exposure to be addressed.

Review of current and "omics" methods for assessing the toxicity (genotoxicity, teratogenicity and nephrotoxicity) of herbal medicines and mushrooms

ETHNOPHARMACOLOGICAL RELEVANCE

The increasing use of traditional herbal medicines around the world requires more scientific evidence for their putative harmlessness. To this end, a plethora of methods exist, more or less satisfying. In this post-genome era, recent reviews are however scarce, not only on the use of new "omics" methods (transcriptomics, proteomics, metabonomics) for genotoxicity, teratogenicity, and nephrotoxicity assessment, but also on conventional ones.

METHODS

The present work aims (i) to review conventional methods used to assess genotoxicity, teratogenicity and nephrotoxicity of medicinal plants and mushrooms; (ii) to report recent progress in the use of "omics" technologies in this field; (iii) to underline advantages and limitations of promising methods; and lastly (iv) to suggest ways whereby the genotoxicity, teratogenicity, and nephrotoxicity assessment of traditional herbal medicines could be more predictive.

RESULTS

Literature and safety reports show that structural alerts, in silico and classical in vitro and in vivo predictive methods are often used. The current trend to develop "omics" technologies to assess genotoxicity, teratogenicity and nephrotoxicity is promising but most often relies on methods that are still not standardized and validated.

CONCLUSION

Hence, it is critical that toxicologists in industry, regulatory agencies and academic institutions develop a consensus, based on rigorous methods, about the reliability and interpretation of endpoints. It will also be important to regulate the integration of conventional methods for toxicity assessments with new "omics" technologies.

A proton nuclear magnetic resonance-based metabonomics study of metabolic profiling in immunoglobulin a nephropathy

OBJECTIVES

Immunoglobulin A nephropathy is the most common cause of chronic renal failure among primary glomerulonephritis patients. The ability to diagnose immunoglobulin A nephropathy remains poor. However, renal biopsy is an inconvenient, invasive, and painful examination, and no reliable biomarkers have been developed for use in routine patient evaluations. The aims of the present study were to identify immunoglobulin A nephropathy patients, to identify useful biomarkers of immunoglobulin A nephropathy and to establish a human immunoglobulin A nephropathy metabolic profile.

METHODS

Serum samples were collected from immunoglobulin A nephropathy patients who were not using immunosuppressants. A pilot study was undertaken to determine disease-specific metabolite biomarker profiles in three groups: healthy controls (N = 23), low-risk patients in whom immunoglobulin A nephropathy was confirmed as grades I-II by renal biopsy (N = 23), and high-risk patients with nephropathies of grades IV-V (N = 12). Serum samples were analyzed using proton nuclear magnetic resonance spectroscopy and by applying multivariate pattern recognition analysis for disease classification.

RESULTS

Compared with the healthy controls, both the low-risk and high-risk patients had higher levels of phenylalanine, myo-Inositol, lactate, L6 lipids ( = CH-CH2-CH = O), L5 lipids (-CH2-C = O), and L3 lipids (-CH2-CH2-C = O) as well as lower levels of β -glucose, α-glucose, valine, tyrosine, phosphocholine, lysine, isoleucine, glycerolphosphocholine, glycine, glutamine, glutamate, alanine, acetate, 3-hydroxybutyrate, and 1-methylhistidine.

CONCLUSIONS

These metabolites investigated in this study may serve as potential biomarkers of immunoglobulin A nephropathy. Point scoring of pattern recognition analysis was able to distinguish immunoglobulin A nephropathy patients from healthy controls. However, there were no obvious differences between the low-risk and high-risk groups in our research. These results offer new, sensitive and specific, noninvasive approaches that may be of great benefit to immunoglobulin A nephropathy patients by enabling earlier diagnosis.

Understanding and classifying metabolite space and metabolite-likeness

While the entirety of ‘Chemical Space’ is huge (and assumed to contain between 10⁶³ and 10²⁰⁰ ‘small molecules’), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous metabolites, defined as ‘naturally occurring’ products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human metabolites in two ways. Firstly, in order to understand metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of metabolites and non-metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing metabolites from non-metabolites, by assigning a ‘metabolite-likeness’ score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of metabolite-likeness, the one being more ‘synthetic’ and the other being more ‘metabolite-like’. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying metabolites, as well as to understanding metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble metabolites, and in our work particularly for assessing the metabolite-likeness of candidate molecules during metabolite identification in the metabolomics field.

Metabolomics in the study of kidney diseases

Metabolomics--the nontargeted measurement of all metabolites produced by the body--is beginning to show promise in both biomarker discovery and, in the form of pharmacometabolomics, in aiding the choice of therapy for patients with specific diseases. In its two basic forms (pattern recognition and metabolite identification), this developing field has been used to discover potential biomarkers in several renal diseases, including acute kidney injury (attributable to a variety of causes), autosomal dominant polycystic kidney disease and kidney cancer. NMR and gas chromatography or liquid chromatography, together with mass spectrometry, are generally used to separate and identify metabolites. Many hurdles need to be overcome in this field, such as achieving consistency in collection of biofluid samples, controlling for batch effects during the analysis and applying the most appropriate statistical analysis to extract the maximum amount of biological information from the data obtained. Pathway and network analyses have both been applied to metabolomic analysis, which vastly extends its clinical relevance and effects. In addition, pharmacometabolomics analyses, in which a metabolomic signature can be associated with a given therapeutic effect, are beginning to appear in the literature, which will lead to personalized therapies. Thus, metabolomics holds promise for early diagnosis, increased choice of therapy and the identification of new metabolic pathways that could potentially be targeted in kidney disease.

Cross-platform analysis of longitudinal data in metabolomics

Metabolic profiling is considered to be a very promising tool for diagnostic purposes, for assessing nutritional status and response to drugs. However, it is also evident that human metabolic profiles have a complex nature, influenced by many external factors. This, together with the understanding of the difficulty to assign people to distinct groups and a general move in clinical science towards personalized medicine, raises the interest to explore individual and variable metabolic features for each individual separately in longitudinal study design. In the current paper we have analyzed a set of metabolic profiles of a selection of six urine samples per person from a set of healthy individuals by (1)H NMR and reversed-phase UPLC-MS. We have demonstrated that the method for recovery of individual metabolic phenotypes can give complementary information to another established method for analysis of longitudinal data--multilevel component analysis. We also show that individual metabolic signatures can be found not only in (1)H NMR data, as has been demonstrated before, but also even more strongly in LC-MS data.

Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome

(1)H NMR spectroscopy of aqueous fecal extracts has been used to investigate differences in metabolic activity of gut microbiota in patients with ulcerative colitis (UC) (n = 13), irritable bowel syndrome (IBS) (n = 10), and healthy controls (C) (n = 22). Up to four samples per individual were collected over 2 years giving a total of 124 samples. Multivariate discriminant analysis, based on NMR data from all three groups, was able to predict UC and C group membership with good sensitivity and specificity; classification of IBS samples was less successful and could not be used for diagnosis. Trends were detected toward increased taurine and cadaverine levels in UC with increased bile acid and decreased branched chain fatty acids in IBS relative to controls; changes in short chain fatty acids and amino acids were not significant. Previous PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analysis of the same fecal material had shown alterations of the gut microbiota when comparing UC and IBS groups with controls. Hierarchical cluster analysis showed that DGGE profiles from the same individual were stable over time, but NMR spectra were more variable; canonical correlation analysis of NMR and DGGE data partly separated the three groups and revealed a correlation between the gut microbiota profile and metabolite composition.

Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive "global" metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography-MS (LC-MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS-based metabolomics with a particular focus on LC-MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC-MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures.

Diabetes resolution and hyperinsulinaemia after metabolic Roux-en-Y gastric bypass

The global prevalence of type 2 diabetes mellitus and impaired glucose metabolism continues to rise in conjunction with the pandemic of obesity. The metabolic Roux-en-Y gastric bypass operation offers the successful resolution of diabetes in addition to sustained weight loss and excellent long-term outcomes in morbidly obese individuals. The procedure consists of the physiological BRAVE effects: (i) Bile flow alteration; (ii) Reduction of gastric size; (iii) Anatomical gut rearrangement and altered flow of nutrients; (iv) Vagal manipulation and (v) Enteric gut hormone modulation. This operation provides anti-diabetic effects through decreasing insulin resistance and increasing the efficiency of insulin secretion. These metabolic outcomes are achieved through weight-independent and weight-dependent mechanisms. These include the foregut, midgut and hindgut mechanisms, decreased inflammation, fat, adipokine and bile metabolism, metabolic modulation, shifts in gut microbial composition and intestinal gluconeogenesis. In a small minority of patients, gastric bypass results in hyperinsulinaemic hypoglycaemia that may lead to nesidioblastosis (pancreatic beta-cell hypertrophy with islet hyperplasia). Elucidating the precise metabolic mechanisms of diabetes resolution and hyperinsulinaemia after surgery can lead to improved operations and disease-specific procedures including 'diabetes surgery'. It can also improve our understanding of diabetes pathogenesis that may provide novel strategies for the management of metabolic syndrome and impaired glucose metabolism.

Using metabolomic analysis to understand inflammatory bowel diseases

Crohn's disease (CD) and ulcerative colitis (UC) are inflammatory bowel diseases (IBD) attributed to a dysregulated immune response towards intestinal microbiota. Although various susceptibility genes have been identified for CD and UC, the exact disease etiology is unclear and complicated by the influence of environmental factors. Metabolomic analysis enables high sample throughput measurements of multiple metabolites in biological samples. The use of metabolomic analysis in medical sciences has revealed metabolite perturbations associated with diseases. This article provides a summary of the current understanding of IBD, and describes potential applications and previous metabolomic analysis in IBD research to understand IBD pathogenesis and improve IBD therapy.

The role of metabolites and metabolomics in clinically applicable biomarkers of disease

Metabolomics allows the simultaneous and relative quantification of thousands of different metabolites within a given sample using sensitive and specific methodologies such as gas or liquid chromatography coupled to mass spectrometry, typically in discovery phases of studies. Biomarkers are biological characteristics that are objectively measured and evaluated as indicators of normal biological processes, pathological processes or pharmacologic responses to a therapeutic intervention. Biomarkers are widely used in clinical practice for the diagnosis, assessment of severity and response to therapy in a number of clinical disease states. In human studies, metabolomics has been applied to define biomarkers related to prognosis or diagnosis of a disease or drug toxicity/efficacy and in doing so hopes to provide greater pathophysiological understanding of disease or therapeutic toxicity/efficacy. This review discusses the application of metabolomics in the discovery and subsequent application of biomarkers in the diagnosis and management of inborn errors of metabolism, cardiovascular disease and cancer. We critically appraise how novel biomarkers discovered through metabolomic analysis may be utilized in future clinical practice by addressing the following three fundamental questions: (1) Can the clinician measure them? (2) Do they add new information? (3) Do they help the clinician to manage patients? Although a number of novel biomarkers have been discovered through metabolomic studies of human diseases in the last decade, none have currently made the transition to routine use in clinical practice. Metabolites identified from these early studies will need to form the basis of larger, prospective, externally validated studies in clinical cohorts for their future use as biomarkers. At this stage, the absolute quantification of these biomarkers will need to be assessed epidemiologically, as will the ultimate deployment in the clinic via routine biochemistry, dip stick or similar rapid at- or near-patient care technologies.

Metabolic profiling of accelerated aging ERCC1 d/- mice

Aging is a fundamental biological process for which the mechanism is still largely unknown due to its complex and multifactorial nature. Animal models allow us to simplify this complexity and to study individual factors separately. As there are many causative links between DNA repair deficiency and aging, we studied the ERCC1(d/-) mouse, which has a modified ERCC1 gene, involved in the Nucleotide Excision Repair, and as a result has a premature aging phenotype. Profiling of these mice on different levels can give an insight into the mechanisms underlying the aging phenotype. In the current study, we have performed metabolic profiling of serum and urine of these mice in comparison to wild type and in relation to aging by (1)H NMR spectroscopy. Analysis of metabolic trajectories of animals from 8 to 20 weeks suggested that wild type and ERCC1(d/-) mutants have similar age-related patterns of changes; however, the difference between genotypes becomes more prominent with age. The main differences between these two genetically diverse groups of mice were found to be associated with altered lipid and energy metabolism, transition to ketosis, and attenuated functions of the liver and kidney.

Toward Arabidopsis thaliana hydrophilic metabolome: assessment of extraction methods and quantitative 1H NMR

Our goal was to establish the hydrophilic metabolome of heterotrophic Arabidopsis thaliana cells grown in suspension, a cellular model of plant sink tissues. Water-soluble metabolites were extracted using four protocols: perchloric acid, boiling ethanol, methanol and methanol/chloroform (M/Chl). They were detected and quantified using (1)H nuclear magnetic resonance (NMR) spectroscopy at 400 MHz. Extraction yields and reproducibility of the extraction methods were investigated. The effects of cell harvest protocol, cell grinding and lyophilization and storage conditions on the measured metabolic profiles were also studied. These quantitative studies demonstrated for the first time that the four extraction protocols commonly used do lead to quite similar molecular compositions as analyzed by (1)H NMR. The M/Chl method proved effective and reliable to prepare series of physiologically significant extracts from plant cells for (1)H NMR analysis. Reproducibility of the detected metabolome was assessed over long periods of time by analyzing a large number of separate extracts prepared from independent cultures. Larger variations in the NMR metabolite profiles could be correlated to changes in physiological parameters of the culture medium. Quantitative resolved (1)H NMR of cell extracts proved to be robust and reliable for routine metabolite profiling of plant cell cultures.

Metabolic fate of polyphenols in the human superorganism

Dietary polyphenols are components of many foods such as tea, fruit, and vegetables and are associated with several beneficial health effects although, so far, largely based on epidemiological studies. The intact forms of complex dietary polyphenols have limited bioavailability, with low circulating levels in plasma. A major part of the polyphenols persists in the colon, where the resident microbiota produce metabolites that can undergo further metabolism upon entering systemic circulation. Unraveling the complex metabolic fate of polyphenols in this human superorganism requires joint deployment of in vitro and humanized mouse models and human intervention trials. Within these systems, the variation in diversity and functionality of the colonic microbiota can increasingly be captured by rapidly developing microbiomics and metabolomics technologies. Furthermore, metabolomics is coming to grips with the large biological variation superimposed on relatively subtle effects of dietary interventions. In particular when metabolomics is deployed in conjunction with a longitudinal study design, quantitative nutrikinetic signatures can be obtained. These signatures can be used to define nutritional phenotypes with different kinetic characteristics for the bioconversion capacity for polyphenols. Bottom-up as well as top-down approaches need to be pursued to link gut microbial diversity to functionality in nutritional phenotypes and, ultimately, to bioactivity of polyphenols. This approach will pave the way for personalization of nutrition based on gut microbial functionality of individuals or populations.

Analysis of metabolic variation and galanthamine content in Narcissus bulbs by 1H NMR

INTRODUCTION

Galanthamine is a benzazepine alkaloid used as a drug to relieve symptoms of Alzheimer's disease. For pharmaceutical use this natural product has been extracted from the plant Leucojum aestivum (Amaryllidaceae) or produced synthetically. Limited supply of the natural source and high cost of synthetic production has led to a search for alternative sources of galanthamine. The bulbs of Narcissus pseudonarcissus (Amaryllidaceae) have been identified as a potential source of raw material for galanthamine extraction. Since inconsistent chemical composition can be an issue with medicinal plant material, it is of interest to know whether large variations occur between Narcissus bulbs grown in different geographical locations.

OBJECTIVE

To evaluate whether large differences exist in the overall metabolic profiles of Narcissus bulbs grown in the two most important cultivation regions.

METHODOLOGY

(1)H NMR and principal component analysis were used for an unbiased comparison of the bulb samples.

RESULTS

Overall metabolite profiles were quite similar, but galanthamine levels could slightly discriminate samples by geographical region. (1)H NMR was used for quantitation of galanthamine, and was found to be comparable to quantitation by HPLC. Compared with conventional chromatographic methods, sample preparation for (1)H NMR analysis is simple and rapid, and only a small amount of plant material is required.

CONCLUSIONS

Since useful qualitative and quantitative information about the metabolic state of Narcissus bulbs can be obtained by (1)H NMR, this method is useful for agricultural applications, and for quality control of raw material used in the pharmaceutical industry.

NMR metabolomics and drug discovery

NMR is an integral component of the drug discovery process with applications in lead discovery, validation, and optimization. NMR is routinely used for fragment-based ligand affinity screens, high-resolution protein structure determination, and rapid protein-ligand co-structure modeling. Because of this inherent versatility, NMR is currently making significant contributions in the burgeoning area of metabolomics, where NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

Development of extractive electrospray ionization ion trap mass spectrometry for in vivo breath analysis

In metabolomics studies and clinical diagnosis, interest is increasing in the rapid analysis of exhaled breath. In vivo breath analysis offers a unique, unobtrusive, non-invasive method of investigating human metabolism. To analyze breath in vivo, we constructed a novel platform of extractive electrospray ionization (EESI) ion trap mass spectrometry (ITMS) using a home-made EESI source coupled to a linear trap quadrupole mass spectrometer. A reference compound (authentic n-octyl amine) was used to evaluate effects of systematically varying selected characteristics of the EESI source on signal intensity. Under the optimized working conditions, metabolic changes of human bodies were in vivo followed by performing rapid breath analysis using the multi-stage EESI-ITMS tandem mass spectrometry platform. For nicotine, a limit of determination was found to be 0.05 fg mL(-1) (S/N = 3, RSD = 5.0 %, n = 10) for nicotine in aerosol standard samples; the dynamic response range was from 0.0155 pg mL(-1) to 155 pg mL(-1). The concentration of nicotine in the exhaled breath of a regular smoker was in vivo determined to be 5.8 pg mL(-1), without any sample pre-treatment. Our results show that EESI-ITMS is a powerful analytical platform to provide high sensitivity, high specificity and high throughput for semi-quantitative analysis of complex samples in life science, particularly for in vivo metabolomics studies.

Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics

The chemical identification of mass spectrometric signals in metabolomic applications is important to provide conversion of analytical data to biological knowledge about metabolic pathways. The complexity of electrospray mass spectrometric data acquired from a range of samples (serum, urine, yeast intracellular extracts, yeast metabolic footprints, placental tissue metabolic footprints) has been investigated and has defined the frequency of different ion types routinely detected. Although some ion types were expected (protonated and deprotonated peaks, isotope peaks, multiply charged peaks) others were not expected (sodium formate adduct ions). In parallel, the Manchester Metabolomics Database (MMD) has been constructed with data from genome scale metabolic reconstructions, HMDB, KEGG, Lipid Maps, BioCyc and DrugBank to provide knowledge on 42,687 endogenous and exogenous metabolite species. The combination of accurate mass data for a large collection of metabolites, theoretical isotope abundance data and knowledge of the different ion types detected provided a greater number of electrospray mass spectrometric signals which were putatively identified and with greater confidence in the samples studied. To provide definitive identification metabolite-specific mass spectral libraries for UPLC-MS and GC-MS have been constructed for 1,065 commercially available authentic standards. The MMD data are available at http://dbkgroup.org/MMD/.

1H NMR metabolite fingerprinting and metabolomic analysis of perchloric acid extracts from plant tissues

Metabolite fingerprinting provides a powerful method for discriminating between biological samples on the basis of differences in metabolism caused by such factors as growth conditions, developmental stage or genotype. This protocol describes a technique for acquiring metabolite fingerprints from samples of plant origin. The preferred method involves freezing the tissue rapidly to stop metabolism, extracting soluble metabolites using perchloric acid (HClO4) and then obtaining a fingerprint of the metabolic composition of the sample using 1D 1H NMR spectroscopy. The spectral fingerprints of multiple samples may be analyzed using either unsupervised or supervised multivariate statistical methods, and these approaches are illustrated with data obtained from the developing seeds of two genotypes of sunflower (Helianthus annuus). Preparation of plant extracts for analysis takes 2-3 d, but multiple samples can be processed in parallel and subsequent acquisition of NMR spectra takes approximately 30 min per sample, allowing 24-48 samples to be analyzed in a week.