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Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: Application to the detection of breast cancer
Abstract
Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.
... Also, the symptoms of pain and fatigue had association with several targeted metabolites. An improved metabolic profile of human serum samples was obtained using complementary thecniques, namely MS and NMR and this approach may be useful to achieve more accurate disease detection and gain more insights regarding disease mechanisms and biology [67]. ...
... Centering is performed when the data analysis is focused on the differences between variables, where all measurements (e.g., concentrations, areas) are converted to values around zero based on variation measures. Mean [46,67,68,79] is the measure normally used in centering. Scaling is used to adjust the variables measurements based on a scaling factor, converting the measurements of all variables into values relative to the scaling factor. ...
... The main scaling approaches based on dispersive measures are autoscaling (standard deviation) [46,51,59] and pareto scaling (square root of the standard deviation) [43,53,55]. On the other hand, the most of size measure approaches uses scaling factors based on the mean [80], median [51,57,59,66,75,78,83] or total intensity value [53,58,67,68,71,73,74,81]. Transformations are mathematical approaches used to decrease the heteroscedasticity of dataset, which the variability between variables is dramatically reduce. ...
Cancer is a major health issue worldwide for many years and has been increasing significantly. Among the different types of cancer, breast cancer (BC) remains the leading cause of cancer-related deaths in women being a disease caused by a combination of genetic and environmental factors. Nowadays, the available diagnostic tools have aided in the early detection of BC leading to the improvement of survival rates. However, better detection tools for diagnosis and disease monitoring are still required. In this sense, metabolomic NMR, LC-MS and GC-MS-based approaches have gained attention in this field constituting powerful tools for the identification of potential biomarkers in a variety of clinical fields. In this review we will present the current analytical platforms and their applications to identify metabolites with potential for BC biomarkers based on the main advantages and advances in metabolomics research. Additionally, chemometric methods used in metabolomics will be highlighted.
... Nowadays, the available diagnostic tools have supported in BC detection leading to the improved of survival rates. In this regard, metabolomics (metabolomic profiling) studies have emerged as a powerful approach to study the metabolic changes in several diseases including cancer (Chen et al., 2015;Dona et al., 2016;Gu et al., 2011;Trifonova et al., 2013;Zhang et al., 2013). Moreover, metabolomics plays an important role in disease profiling being a promising approach for the pursuit of new biomarkers in biological matrices, such as cell extracts, tissues or biological fluids (Johnson et al., 2016). ...
... If used in combination, these techniques enable the identification of a more comprehensive panel of metabolites involved in metabolic alterations and help unraveling the possible correlations and the underlying mechanisms induced by a disease (Chen et al., 2015;Marshall & Powers, 2017;Robertson et al., 2011). A variety of studies have been conducted by NMR and direct analysis in real time (DART)-MS using this approach to find serum biomarkers for BC (Gu et al., 2011). Another study was developed by Chen et al. (2015) that used the dual platform of NMR and MS methods to establish the urinary metabolomic profile of bipolar disorder (BD) subjects with a diagnosis purpose. ...
IntroductionGlobally, breast cancer (BC) is leading at the top of women's diseases and, as a multifactorial disease, there is the need for the development of new approaches to aid clinicians on monitoring BC treatments. In this sense, metabolomic studies have become an essential tool allowing the establishment of interdependency among metabolites in biological samples.Objective
The combination of nuclear magnetic resonance (NMR) and gas chromatography–quadrupole mass spectrometry (GC–qMS) based metabolomic analyses of urine and breast tissue samples from BC patients and cancer-free individuals was used.Methods
Multivariate statistical tools were used in order to obtain a panel of metabolites that could discriminate malignant from healthy status assisting in the diagnostic field. Urine samples (n = 30), cancer tissues (n = 30) were collected from BC patients, cancer-free tissues were resected outside the tumor margin from the same donors (n = 30) while cancer-free urine samples (n = 40) where obtained from healthy subjects and analysed by NMR and GC–qMS methodologies.ResultsThe orthogonal partial least square discriminant analysis model showed a clear separation between BC patients and cancer-free subjects for both classes of samples. Specifically, for urine samples, the goodness of fit (R2Y) and predictive ability (Q2) was 0.946 and 0.910, respectively, whereas for tissue was 0.888 and 0.813, revealing a good predictable accuracy. The discrimination efficiency and accuracy of tissue and urine metabolites was ascertained by receiver operating characteristic curve analysis that allowed the identification of metabolites with high sensitivity and specificity. The metabolomic pathway analysis identified several dysregulated pathways in BC, including those related with lactate, valine, aspartate and glutamine metabolism. Additionally, correlations between urine and tissue metabolites were investigated and five metabolites (e.g. acetone, 3-hexanone, 4-heptanone, 2-methyl-5-(methylthio)-furan and acetate) were found to be significant using a dual platform approach.Conclusion
Overall, this study suggests that an improved metabolic profile combining NMR and GC–qMS may be useful to achieve more insights regarding the mechanisms underlying cancer.Graphic abstract
... In a critical review of metabolomics data analysis, univariate analysis, unsupervised analysis, supervised analysis, and receiver operating characteristic (ROC) curve analysis were identified as essential criteria for ensuring comprehensive data analysis [4]. Many studies have suggested that supervised analysis using partial least squares-discriminant analysis (PLS-DA)/orthogonal partial least squares-discriminant analysis (OPLS-DA) is the most frequently employed method, followed by random forests (RFs) and support vector machines (SVMs) [5,6]. Although reduced dimensions were found to account for the resulting variables, variable-picking models typically provide only a measure of the importance of the variables and do variable selection unnaturally. ...
Metabolomics and foodomics shed light on the molecular processes within living organisms and the complex food composition by leveraging sophisticated analytical techniques to systematically analyze the vast array of molecular features. The traditional feature-picking method often results in arbitrary selections of the model, feature ranking, and cut-off, which may lead to suboptimal results. Thus, a Multiple and Optimal Screening Subset (MOSS) approach was developed in this study to achieve a balance between a minimal number of predictors and high predictive accuracy during statistical model setup. The MOSS approach compares five commonly used models in the context of food matrix analysis, specifically bourbons. These models include Student’s t-test, receiver operating characteristic curve, partial least squares-discriminant analysis (PLS-DA), random forests, and support vector machines. The approach employs cross-validation to identify promising subset feature candidates that contribute to food characteristic classification. It then determines the optimal subset size by comparing it to the corresponding top-ranked features. Finally, it selects the optimal feature subset by traversing all possible feature candidate combinations. By utilizing MOSS approach to analyze 1406 mass spectral features from a collection of 122 bourbon samples, we were able to generate a subset of features for bourbon age prediction with 88% accuracy. Additionally, MOSS increased the area under the curve performance of sweetness prediction to 0.898 with only four predictors compared with the top-ranked four features at 0.681 based on the PLS-DA model. Overall, we demonstrated that MOSS provides an efficient and effective approach for selecting optimal features compared with other frequently utilized methods.
... Orthogonal partial least squares-discriminant analysis (OPLS-DA) (Gu et al. 2011) was performed for multivariate statistical analysis to facilitate group separation and identification of biomarkers contributing most to group differences. The validity of the OPLS-DA model was evaluated through permutation test to determine the level of significance of group separation in a model, with p-value < 0.05 as significant. ...
Trichlorfon, one of the most widely used organophosphate insecticides, is commonly employed in aquaculture and agriculture to combat parasitic infestations. However, its inherent instability leads to rapid decomposition into dichlorvos (DDVP), increasing its toxicity by eightfold. Therefore, the environmental effects of trichlorfon in real-world scenarios involve the combined effects of trichlorfon and its degradation product, DDVP. In this study, we systematically investigated the degradation of trichlorfon in tap water over time using HPLC and LC–MS/MS analysis. Subsequently, an experiment was conducted to assess the acute toxicity of trichlorfon and DDVP on goldfish (Carassius auratus), employing a ¹H NMR-based metabolic approach in conjunction with serum biochemistry, histopathological inspection, and correlation network analysis. Exposure to trichlorfon and its degradation product DDVP leads to increased lipid peroxidation, reduced antioxidant activity, and severe hepatotoxicity and nephrotoxicity in goldfish. Based on the observed pathological changes and metabolite alterations, short-term exposure to trichlorfon significantly affected the liver and kidney functions of goldfish, while exerting minimal influence on the brain, potentially due to the presence of the blood–brain barrier. The changes in the metabolic profile indicated that trichlorfon and DDVP influenced several pathways, including oxidative stress, protein synthesis, energy metabolism, and nucleic acid metabolism. This study demonstrated the applicability and potential of ¹H NMR-based metabonomics in pesticide environmental risk assessment, providing a feasible method for the comprehensive study of pesticide toxicity in water environments.
Graphical abstract
... For example, it was reported that the DART-MS method has been used for the analysis of dried blood spots for the detection of phenylketonuria in newborns and quantification of metabolites in human plasma [13]. DART-MS technology was also used in various biochemical analyses of the natural moisturizing factor in the stratum corneum, breast cancer, ovarian cancer, carp muscle and chicken meat metabolomics [14][15][16][17]. ...
Alanine transaminase (ALT) is an important amino acid-metabolizing enzyme in silkworm Bombyx mori L., and is mainly involved in transferring glutamate to alanine (serving as an essential precursor in silk protein synthesis) through transamination. Therefore, it is generally believed that silk protein synthesis in the silk gland and the cocoon quantity increase with the increase in ALT activity to a certain extent. Here, a novel analytical method was developed to determine the ALT activity in several key tissues of Bombyx mori L. including the posterior silk gland, midgut, fat body, middle silk gland, trachea and hemolymph, by combining the direct-analysis-in-real-time (DART) ion source with a triple-quadrupole mass spectrometer. In addition, a traditional ALT activity assay, the Reitman–Frankel method, was also used to measure ALT activity for comparison. The ALT activity results obtained via the DART-MS method are in good agreement with those obtained via the Reitman–Frankel method. However, the present DART-MS method provides a more convenient, rapid and environmentally friendly quantitative method for ALT measurement. Especially, this method can also monitor ALT activity in different tissues of Bombyx mori L. in real time.
... DART is used with small molecular compounds with minor cross-contamination and provides a simple and high-throughput analysis. 89,115 MS analysers. Improving resolution and sensitivity is the main goal of metabolomics studies. ...
Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.
... The common approaches used for metabolomic profiling include use of liquid chromatography-mass spectrometry (LC-MS) (Chen et al. 2009), gas chromatography-mass spectrometry (GC-MS) (Woo et al., 2009), and nuclear magnetic resonance (NMR) spectroscopy (Gu et al., 2011). Among all these techniques, NMR spectroscopy is a highly reproducible, non-targeted and non-destructive method (DeFeo et al., 2011). ...
Introduction:
Globally, one of the major causes of cancer related deaths in women is breast cancer. Although metabolic pattern is altered in cancer patients, robust metabolic biomarkers with a potential to improve the screening and disease monitoring are lacking. A complete metabolome profiling of breast cancer patients may lead to the identification of diagnostic/prognostic markers and potential targets.
Objectives:
The aim of this study was to analyze the metabolic profile in the serum from 43 breast cancer patients and 13 healthy individuals.
Materials & methods:
We used 1H NMR spectroscopy for the identification and quantification of metabolites. q-RT-PCR was used to examine the relative expression of lncRNAs.
Results:
Metabolites such as amino acids, lipids, membrane metabolites, lipoproteins, and energy metabolites were observed in the serum from both patients and healthy individuals. Using unsupervised PCA, supervised PLS-DA, supervised OPLS-DA, and random forest classification, we observed that more than 25 metabolites were altered in the breast cancer patients. Metabolites with AUC value > 0.9 were selected for further analysis that revealed significant elevation of lactate, LPR and glycerol, while the level of glucose, succinate, and isobutyrate was reduced in breast cancer patients in comparison to healthy control. The level of these metabolites (except LPR) was altered in advanced-stage breast cancer patients in comparison to early-stage breast cancer patients. The altered metabolites were also associated with over 25 signaling pathways related to metabolism. Further, lncRNAs such as H19, MEG3 and GAS5 were dysregulated in the breast tumor tissue in comparison to normal adjacent tissue.
Conclusion:
The study provides insights into metabolic alteration in breast cancer patients. It also provides an avenue to examine the association of lncRNAs with metabolic patterns in patients.
... Additionally, fusion of 1 H-NMR and chromatographic techniques (gas and liquid chromatography) data coupled with mass spectrometry was applied to provide more an accurate knowledge about the classification of golden rums [26]. The combined use of multi-technique data in chemometric analysis produced the best results compared to the individual techniques in classifying and distinguishing samples [27]. ...
Tacle is a citrus variety which recently gained further interest due to its antioxidant and biological properties. This study suggests using Nuclear Magnetic Resonance (NMR) imaging to characterize Tacle juice’s metabolic composition as it is intimately linked to its quality. First, polar and apolar solvent systems were used to identify a significant fraction of the Tacle metabolome. Furthermore, the antioxidant capacity and the total content of flavonoids, polyphenols and β-carotene in the juice were investigated with UV—Visible spectroscopy. Tacle juice was clarified and fractionated by ultrafiltration (UF) and nanofiltration (NF) membranes in order to recover and purify its bioactive principles. Finally, the second part of this work sheds light on the spectrophotometric assays and 1H-NMR spectra of fractions coming from membrane operations coupled with a multivariate data analysis technique, PCA, to explore the impact of UF and NF processes on the metabolic profile of the juice.
... A supervised orthogonal partial least-squares discriminant analysis (OPLS-DA) (Gu et al., 2011) were performed for multivariate statistical analysis, which can identify different groups and screen for effective discriminants. The validity of the OPLS-DA model was evaluated through permutation test to determine the level of signi cance of group separation, a model with p-values< 0.05 was regarded as signi cant. ...
Trichlorfon, one of the most widely used organophosphate insecticide, is used in aquaculture and agriculture against parasitic infestations, but it is extremely unstable and easily decomposed into dichlorvos (DDVP), increasing its toxicity by 8 times. The degradation pattern of trichlorfon in water was systematically studied by LC-MS/MS. The experiment was conducted to investigate the acute toxicity of trichlorfon and DDVP on goldfish using a ¹ H NMR based metabolic approach combined with serum biochemistry, histopathological inspection and correlation network analysis. The changes of metabolic profile indicated that trichlorfon and DDVP influenced several pathways including oxidative stress, protein synthesis, energy metabolism and nucleic acid metabolism. Plasma was collected and then the hematological indicators of MDA, SOD, ALT, AST BUN and CRE were measured. The histopathological alternations were observed by H&E staining, which showed the tubular epithelial cell swelling, cytoplasmic loosening in the kidney. This study verified the applicability and potential of metabonomics based on ¹ H NMR in pesticide environmental risk assessment, and provided a feasible method for the study of overall toxicity of pesticides in water environment.
... MS and NMR are the most widely used analytical techniques in metabolomics [45]. MS provides a mix of rapid, sensitive and selective qualitative and quantitative analyses with good skill to identify metabolites [46]. ...
Metabolomics represent the set of small organic molecules generally called metabolites, which are located within cells, tissues or organisms. This new “omic” technology, together with other similar technologies (genomics, transcriptomics and proteomics) is becoming a widely used tool in cancer research, aiming at the understanding of global biology systems in their physiologic or altered conditions. Cancer is among the most alarming human diseases and it causes a considerable number of deaths each year. Cancer research is one of the most important fields in life sciences. In fact, several scientific advances have been made in recent years, aiming to illuminate the metabolism of cancer cells, which is different from that of healthy cells, as suggested by Otto Warburg in the 1950s. Studies on sponges and algae revealed that these organisms are the main sources of the marine bioactive compounds involved in drug discovery for cancer treatment and prevention. In this review, we analyzed these two promising groups of marine organisms to focus on new metabolomics approaches for the study of metabolic changes in cancer cell lines treated with chemical extracts from sponges and algae, and for the classification of the chemical structures of bioactive compounds that may potentially prove useful for specific biotechnological applications.
... After the emergence of the pioneered technique, desorption electrospray ionization (DESI) [101], more than dozens of direct-ionization techniques have emerged which can meet the requirements of being real-time, in situ, online, non-destructive, and in accordance with the new concept of no pollution and low energy consumption. Typical ambient ion source includes direct analysis in real time (DART) [102,103], surface desorption atmospheric pressure chemical ionization (DAPCI) [104,105], extractive electrospray ionization (EESI) [106][107][108], dielectric barrier discharge ionization (DBDI) [109], flowing atmospheric pressure afterglow (FAPA) [110], etc., all of which have obtained a series of achievements in the analysis of complex matrix samples and greatly expanded the range of analytical objects of mass spectrometry, from various fields including metabolomics [111][112][113], proteomics [114,115], forensic medicine [116,117], and quality monitoring [118]. ...
Polycyclic aromatic hydrocarbons (PAHs) are a class of the most common and widespread contaminants. The accumulation of PAHs has made a certain impact on the environment and is seriously threatening human health. Numerous general analytical methods suitable for PAHs were developed. With the development of economy, the environmental problems of PAHs in modern society are more extensive and prominent, and attract more attention from environmental scientists and analysts. Deeper understanding of the properties of PAHs depends on the advent of detection methods, which can also be more conducive to promoting the protection of the environment. Till now, more sensitive, more high-speed and more high-throughput analytical tools are being invented and have played important roles in the research of PAHs. In this short review article, we focused mainly on the contemporary analytical methods about PAHs. We started with a brief review on the hazards, migration, distribution and traditional analysis methods of PAHs in recent years, including liquid chromatography, gas chromatography, surface enhanced Raman spectroscopy and so on. We also presented the applications of the modern ambient mass spectrometry, especially microwave plasma torch mass spectrometry, in the detection of PAHs, as well as the far out novel results in our lab by using microwave plasma torch (MPT) mass spectrometry; for example, some new insights about Birch reduction, regular hydrogen addition and the robustness of molecular structure. These studies have demonstrated the versatility of MPT MS as a platform in the research of PAHs.
... In a first example which aimed at determining metabolic differences between serum samples from breast cancer patients and healthy controls, a mid-level data fusion approach was used to enhance the discriminative performance of unsupervised analyses and limit the misclassification of the supervised analyses performed on the individuals NMR and the direct analysis in real time (DART-MS) models (Gu et al., 2011). In that end, another supervised analysis was performed by setting up the Y variable to the first component of the unsupervised NMR model, which performed better than the MS model, and the X matric was set as the DART-MS dataset, containing more variables. ...
Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.
... 8−12 These approaches have been successfully used to highlight high priority regions of the spectra, using spectral variability as a proxy to gauge potential novelty or detection of biomarkers. 13,14 However, 1 H NMR-based metabolomics methods cannot easily associate signals that derive from the same molecule, limiting identification options for unknown constituents. ...
The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.
... In the field of metabolomics, direct analysis in real time coupled to high resolution mass spectrometry (DART-HRMS) is considered an innovative, ambient mass spectrometric approach, successfully applied in clinical screening, microbiology, food safety, and toxicology (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). DART-HRMS requires minimal sample preparation and it has already demonstrated its accuracy, intra-sample repeatability and rapidity, with the aim of reducing the burden of chemical laboratories. ...
Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of paratuberculosis [Johne's disease (JD)], a chronic disease that causes substantial economic losses in the dairy cattle industry. The long incubation period means clinical signs are visible in animals only after years, and some cases remain undetected because of the subclinical manifestation of the disease. Considering the complexity of JD pathogenesis, animals can be classified as infected, infectious, or affected. The major limitation of currently available diagnostic tests is their failure in detecting infected non-infectious animals. The present study aimed to identify metabolic markers associated with infected and infectious stages of JD. Direct analysis in real time coupled with high resolution mass spectrometry (DART-HRMS) was, hence, applied in a prospective study where cohorts of heifers and cows were followed up annually for 2–4 years. The animals' infectious status was assigned based on a positive result of both serum ELISA and fecal PCR, or culture. The same animals were retrospectively assigned to the status of infected at the previous sampling for which all JD tests were negative. Stored sera from 10 infected animals and 17 infectious animals were compared with sera from 20 negative animals from the same herds. Two extraction protocols and two (-/+) ionization modes were tested. The three most informative datasets out of the four were merged by a mid-level data fusion approach and submitted to partial least squares discriminant analysis (PLS-DA). Compared to the MAP negative subjects, metabolomic analysis revealed the m/z signals of isobutyrate, dimethylethanolamine, palmitic acid, and rhamnitol were more intense in infected animals. Both infected and infectious animals showed higher relative intensities of tryptamine and creatine/creatinine as well as lower relative abundances of urea, glutamic acid and/or pyroglutamic acid. These metabolic differences could indicate altered fat metabolism and reduced energy intake in both infected and infectious cattle. In conclusion, DART-HRMS coupled to a mid-level data fusion approach allowed the molecular features that identified preclinical stages of JD to be teased out.
... NMR metabolomic analysis of urine and blood samples served as a gateway for early diagnosis of BC, while analysis of breast tissue biopsy samples can be a useful tool as a form of secondary confirmation [45]. In a study by Slupsky et al. [46], NMR urinary metabolic profiling revealed that numerous metabolites decreased in concentration among BC patients when compared with the healthy group. ...
Cancer is a category of diseases characterized by uncontrolled cell growth and high potential to disseminate to other parts of the body. Cancer diagnosis is challenging due to the high structure similarity between normal and cancerous cells and the aggressive diagnostic procedures. Early diagnosis of cancer is crucial to increase the remission probability and avoid complications. A number of techniques have been involved in cancer diagnosis including biopsy, laboratory tests, computerized tomography (CT) scan, Ultrasonography, X-ray imaging, and nuclear magnetic resonance (NMR) spectroscopy. NMR has been applied both in vivo (known as magnetic resonance imaging) and in vitro to aid in cancer diagnosis. This chapter discusses the application of in vitro NMR in diagnosis and prognosis of different types of cancer with emphasis on the metabolic alterations at early stages of malignancy. The signature metabolites of brain, breast, epithelial ovarian, prostate, lung, colorectal, bladder, and oral cancers have been presented. A perspective overview of the role of NMR spectroscopy in cancer diagnosis has also been presented. This chapter shed the light on the important role of NMR spectroscopy in cancer diagnosis and treatment follow up. The applications introduced are not meant to provide a complete list of existing studies, but to present a wide overview of the current progress in this field. The chapter will cover the following topics.
... Since principal components analysis-based clustering of metabolomics data is often difficult, 16 PsA patients was not evident (figure 2A). When assessing the quality of the PLS-DA models, their accuracy was ≤65%, and both the R 2 and Q 2 values were very low ( 1 H-spectra: R 2 =0.13, ...
Objectives
The differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases.
Methods
Sera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model.
Results
Univariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified.
Conclusions
PsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.
... The study of specific metabolites to identify cancer fingerprints or signatures can aid in cancer detection and prognosis as well as the assessment of the pharmacodynamic effects of therapy [11]. The most common approaches in metabolomics involve gas chromatography-mass spectrometry (GC-MS) [8], liquid chromatography-mass spectrometry (LC-MS) [19] or nuclear magnetic resonance spectroscopy (NMR) [3,16,18,20]. ...
Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC patients from healthy controls (variable importance in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification of some metabolites with the highest sensitivities and specificities to discriminate BC patients from healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic pathway analysis indicated several metabolism pathway disruptions, including amino acid and carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained results support the high throughput potential of NMR-based urinary metabolomics patterns in discriminating BC patients from CTL. Further investigations could unravel novel mechanistic insights into disease pathophysiology, monitor disease recurrence, and predict patient response towards therapy.
... Metabolomics has become a promising field for applications in personalized healthcare. 2 As of now, breast cancer metabolomics is a hot topic and has been explored by many groups around the world. [3][4][5] However, very few studies have employed accurate quantification methods to establish prediction models. [6][7][8][9][10][11] This is in fact due to a major obstacle within metabolomic studies in general, as, unlike drug studies, metabolites are endogenous and thus a true blank cannot be manufactured. ...
Rationale:
Breast cancer is one of the most common cancers among women and its associated mortality has been on the rise. Metabolomics is a potential strategy for breast cancer detection. The post column infused internal standard (PCI-IS) assisted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method has been demonstrated as an effective strategy for quantitative metabolomics. In this study, we evaluated the performance of targeted metabolomics with the PCI-IS quantification method to identify women with breast cancer.
Methods:
We used metabolite profiling to identify 17 dysregulated metabolites in breast cancer patients. Two LC-MS/MS methods in combination with the PCI-IS strategy were developed to quantify these metabolites in plasma samples. Detection models were built through the analysis of plasma samples from 176 subjects consisting of healthy volunteers and breast cancer patients.
Results:
Three isotope standards were selected as the PCI-IS for the metabolites. The accuracy was within 82.8-114.16 %, except for citric acid and lactic acid at high concentration levels. The repeatability and intermediate precision were all lower than 15% relative standard deviation. We have identified several metabolites that indicate the presence of breast cancer. The area under the receiver operating characteristics (AUROC) curve, sensitivity and specificity of the linear combinations of metabolite concentrations and age with the highest AUROC were 0.940 (0.889-0.992), 88.4% and 94.2% for pre-menopausal woman, respectively, and 0.828 (0.734-0.922), 73.5% and 85.1% for post-menopausal women, respectively.
Conclusions:
The targeted metabolomics with PCI-IS quantification method successfully established prediction models for breast cancer detection. Further study is essential to validate these proposed markers.
... This classification technique finds the components or latent variables which discriminate as much as possible between two or more different groups of samples (X block), according to their maximum covariance with target classes (concentrations of metabolites) defined in the Y data block [24]. By relating a data matrix containing independent variables from samples (concentration values) to a matrix containing dependent variables (classes) for these samples, OPLS-DA can remove variations from the independent variables that are not correlated to the dependent variables and enables reducing the model complexity with preserved prediction ability [25]. ...
ST-segment elevation myocardial infarction (STEMI) is the most severe form of myocardial infarction (MI) and the main contributor to morbidity and mortality caused by MI worldwide. Frequently, STEMI is caused by complete and persistent occlusion of a coronary artery by a blood clot, which promotes heart damage. STEMI impairment triggers changes in gene transcription, protein expression, and metabolite concentrations, which grants a biosignature to the heart dysfunction. There is a major interest in identifying novel biomarkers that could improve the diagnosis of STEMI. In this study, the phenotypic characterization of STEMI patients ( n=15 ) and healthy individuals ( n=19 ) was performed, using a target metabolomics approach. Plasma samples were analyzed by UPLC-MS/MS (ultra-high-performance liquid chromatography-tandem mass spectrometry) and FIA-MS (MS-based flow injection analysis). The goal was to identify novel plasma biomarkers and metabolic signatures underlying STEMI. Concentrations of phosphatidylcholines, lysophosphatidylcholines, sphingomyelins, and biogenic amines were altered in STEMI patients in relation to healthy subjects. Also, after multivariate analysis, it was possible to identify alterations in the glycerophospholipids, alpha-linolenic acid, and sphingolipid metabolisms in STEMI patients.
... The heat map also indicates substantial differences in the patterns of variables (markers) of different cancers (each column represents a patient, and each row represents a protein). Higher and lower protein levels are indicated in red and green, respectively; the ID of 69 proteins in the heat map (right y-axis) variables, from top to bottom, are: 7, 1, 68,8,47,36,55,37,60,48,43,50,28,51,38,3,42,58,63,46,53,31,54,17,14,44,24,21,39,40,52,5,27,11,69,65,56,57,32,16,15,13,10,26,22,62,49,6,2,41,12,45,67,59,29,4,19,64,20,33,66,61,30,23,18,35,34,25, and 9 (the protein names are provided in Table 1). are mapped onto nearby or the same neurons, which means that the selected variables provide valuable information for discriminating the samples in the feature space. ...
The earlier any catastrophic disease (e.g., cancer) is diagnosed, the more likely it can be treated, providing improved patient prognosis, extended survival and better quality of life. In early 2014, we revealed that various types of disease can substantially affect the composition/profile of protein corona (i.e., a layer of biomolecules that forms at the surface of nanoparticles upon their interactions with biological fluids). Here, by combining the concepts of disease-specific protein corona and sensor array technology we developed a platform with disease detection capacity using blood plasma. Our sensor array consists of three cross-reactive liposomes, with distinct lipid composition and surface charge. Rather than detecting a specific biomarker, the sensor array provides pattern recognition of the corona protein composition adsorbed on the liposomes. As a feasibility study, sensor array validation was performed using plasma samples obtained from patients diagnosed with five different cancer types (i.e. lung cancer, glioblastoma, meningioma, myeloma, and pancreatic cancer) and a control group of healthy donors. Although no single corona composition is specific for any one cancer type, overlapping but distinct patterns of the corona composition constitutes a unique “fingerprint” for each type of cancer (with a high classification accuracy, i.e. 99.4%). To finally probe the capacity of this sensor array for early detection of cancers, we used cohort plasma obtained from healthy people who were subsequently diagnosed several years after plasma collection with lung, brain, and pancreatic cancers. Our results suggest that the disease-specific protein corona sensor array will not only be instrumental in the screening, detection, and identification of diseases, but may also help identify novel protein pattern markers whose role in disease development and/or disease biology has not been appreciated so far.
... In addition, machine learning methods are becoming more and more of interest and they are often used to analyze the data from mass spectrometry, chromatography and spectroscopy, etc. (Yang et al. 2002;Mahadevan et al. 2008;Gu et al. 2011;Finch et al. 2017). To explore and understand the capacity of machine learning methods for classifying wood species, based on DART-FTICR-MS data, four methods, including SMO, random forest, multilayer perceptron and KNN were used to establish a classification model (Table 4). ...
Pterocarpus santalinus , listed in CITES Appendix II, is an endangered timber species as a result of illegal harvesting due to its high value and commercial demand. The growing demand for P. santalinus and timbers with the morphologically similar Pterocarpus tinctorius has resulted in confusion as well as identification problems. Therefore, it is of vital importance to explore reliable ways to accurately discriminate between P. santalinus and P. tinctorius . In this study, the method of direct analysis in real time and fourier transform ion cyclotron resonance mass spectrometry (DART-FTICR-MS), combined with multivariate statistical analysis, was used to extract chemical information from xylarium wood specimens and to explore the feasibility of distinguishing these two species. Significant differences were observed in their DART-FTICR-MS spectra. Orthogonal partial least square-discriminant analysis (OPLS-DA) showed the highest prediction, with an accuracy of 100%. These findings demonstrate the feasibility of authenticating wood types using DART-FTICR-MS coupled with multivariate statistical analysis.
... Here, 28/30 quantified metabolites were significantly lower in urine samples from BCa patients, with formate, succinate, and uracil among the most important for separation. A study on a small cohort (n = 57) further demonstrated the possibility to separate BCa from healthy controls using serum samples, 61 with higher lactate and a tendency of lower glucose and taurine in BCa patients. No further studies have validated these findings using NMR. ...
Prostate cancer is the second most common malignancy, and the fifth leading cause of cancer-related death among men, worldwide. A major unsolved clinical challenge in prostate cancer is the ability to accurately distinguish indolent cancer types from the aggressive ones. Reprogramming of metabolism is now a widely accepted hallmark of cancer development, where cancer cells must be able to convert nutrients to biomass while maintaining energy production. Metabolomics is the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues, or organisms. Nuclear magnetic resonance (NMR) spectroscopy is commonly applied in metabolomics studies of cancer. This chapter provides protocols for NMR-based metabolomics of cell cultures, biofluids (serum and urine), and intact tissue, with concurrent advice for optimal biobanking and sample preparation procedures.Key wordsBiobankingBiofluids analysisCell extracts analysisMetabolite quantificationMetabolomicsNMR pulse sequencesNMR spectroscopyProstate cancerSample preparationTargeted metabolic pathway
analysisTissue analysis
... The processed NMR data were analyzed by a supervised orthogonal signal correction partial least-squares discriminant analysis (OSC-PLSDA). 25 OSC-PLSDA is a supervised pattern recognition technique that could filter out effects unrelated to an interested response to the metabolic differences between the classes. To assess the quality of the established OSC-PLSDA model, repeated 2-fold cross-validation was carried out. ...
Owing to the promising applications of C-dots in biomedical engineering, concerns about their safety have drawn increasing attention recently. In this study, mice were intraperitoneally injected at different C-dots concentrations (0, 6.0, 12.0 and 24.0 mg/kg) once every 2 days for 30 days, 1H NMR-based metabolic approach supplemented with biochemical analysis and histopathology was used for the first time to explore the toxicity of C-dots in vivo. Histopathological inspection revealed that C-dots did not induce any obvious impairments in tissues. Biochemical assays showed no significant alterations of most measured biochemical parameters in tissues and serum, except for a slight reduction of albumin level in serum as well as AChE activity in liver and kidney. Orthogonal signal correction–partial least squares–discriminant analysis (OSC–PLS–DA) of NMR profiles supplemented with correlation network analysis and SUS-plots disclosed that C-dots not only triggered the immune system but also disturbed the function of cell membranes as well as the normal liver clearance, indicating that 1H NMR based metabolomics approach provided deep insights into the toxicity of C-dots in vivo, gained an advantage over traditional toxicological means, and should be helpful for the understanding of its toxic mechanism.
... Here, 28/30 quantified metabolites were significantly lower in urine samples from BCa patients, with formate, succinate, and uracil among the most important for separation. A study on a small cohort (n = 57) further demonstrated the possibility to separate BCa from healthy controls using serum samples, 61 with higher lactate and a tendency of lower glucose and taurine in BCa patients. No further studies have validated these findings using NMR. ...
Metabolomics is the branch of “omics” technologies that involves high‐throughput identification and quantification of small‐molecule metabolites in the metabolome. NMR‐based spectroscopy of biofluids represents a potential method for non‐invasive characterization of cancer. While the metabolism of cancer cells is altered compared with normal non‐proliferating cells, the metabolome of several biofluids (e.g. blood and urine) reflects the metabolism of the entire organism. This review provides an update on the current status of NMR metabolomics analysis of biofluids with respect to: (i) cancer risk assessment; (ii) cancer detection; (iii) disease characterization and prognosis; and (iv) treatment monitoring. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, and suggest that NMR metabolomics can be used to provide information with prognostic or predictive value. However, translation of these findings to clinical practice is currently hindered by a lack of validation, difficulties in biological interpretation, and non‐standardized analytical procedures.
This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10–15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.
Rare earth elements (REEs) have been widely applied in modern industry and material science due to their special chemical properties and luminescence properties, but their environment pollution problem has also attracted attention. How to trace and control impact of REEs on environment and develop highly sensitive methods for REEs analysis have practical significance. Microwave plasma torch (MPT) is a kind of simple, low power consumption (∼200W) and easily operated plasma generators. When it was coupled with varied mass spectrometers as ion source of mass spectrometry (MS), i.e. MPT-MS can use for the analysis of metal elements in aqueous with the remarkable advantage of only minimal or even no simple pretreatment, and can meet comprehensive requirements of environmental control and water quality monitoring. MPT-MS have also an ability to carry out online real-time monitoring and analysis of water environment. Compared with the traditional ICP-MS, it can obtain more effective information and has higher sensitivity (the detection limit is at sub-ppb level). This paper reviews common methods and research progress of REEs analysis in recent years. Practical applications and advantages of MPT in the analysis of REEs are briefly summarized. Specifically, this paper introduces some work recently done in our group on analysis of REEs by the MPT, including analysis of metal elements distribution in water samples in local Poyang Lake, and general rules of formation and behavior of complex metal ions in the MPT plasma are also proposed afterwards. Through the practically dealing with the Poyang Lake case by the MPT, hopefully this would interest more academia into this area so as to speed up development of the MPT itself.
Zinc oxide (ZnO) nanoparticles (NPs) have been widely used in industry, cosmetics, drugs, bioimaging, and drug delivery. ZnO NPs have been found to interact and interfere with cellular physiology via macrophages, thereby resulting in macrophage polarization. The functional reprogramming of the cells is synchronized through cellular metabolic adaptations. The current study, therefore, aims to establish crosstalk between ZnO-NP-induced metabolic alterations and macrophage polarization in PMA-activated THP-1 cell lines. We observed moderate to heightened cytotoxic response in terms of cell viability and proliferation. The results also revealed increased Th1-type cytokine and chemokine expression. In order to characterize the changes in metabolite concentration in treatment groups, we employed multivariate data analysis (principal component analysis and partial least-squares discriminant analysis) of 1H NMR spectra. The results revealed biologically relevant patterns and alterations in many metabolic pathways. These alterations and patterns were found to be in line across the immune-cytotoxic axis. Furthermore, the results also implicate the role of carbon metabolism toward the classical activation of macrophage polarization. The omics approach could identify the markers involved in NP-induced toxicity, thus elaborating our vision of cytotoxicity that is currently limited to end-point and cytokine assays. Also, it could be emphasized that metabolic reconfiguration upon NP stimulation could direct macrophage polarization toward classical activation.
Background:
The aim of this study was to gain an increased understanding of the aetiology of breast cancer, by investigating possible associations between serum lipoprotein subfractions and metabolites and the long-term risk of developing the disease.
Methods:
From a cohort of 65,200 participants within the Trøndelag Health Study (HUNT study), we identified all women who developed breast cancer within a 22-year follow-up period. Using nuclear magnetic resonance (NMR) spectroscopy, 28 metabolites and 89 lipoprotein subfractions were quantified from prediagnostic serum samples of future breast cancer patients and matching controls (n = 1199 case-control pairs).
Results:
Among premenopausal women (554 cases) 14 lipoprotein subfractions were associated with long-term breast cancer risk. In specific, different subfractions of VLDL particles (in particular VLDL-2, VLDL-3 and VLDL-4) were inversely associated with breast cancer. In addition, inverse associations were detected for total serum triglyceride levels and HDL-4 triglycerides. No significant association was found in postmenopausal women.
Conclusions:
We identified several associations between lipoprotein subfractions and long-term risk of breast cancer in premenopausal women. Inverse associations between several VLDL subfractions and breast cancer risk were found, revealing an altered metabolism in the endogenous lipid pathway many years prior to a breast cancer diagnosis.
Efficient subtype classification utilizing multiple biological information sources is clinically practical for precision treatment of inborn errors of metabolism (IEMs). This study utilized urinary GC-MS and dried blood spot MS/MS to enable complementary mass spectrometric characterization of pathological information for methylmalonic aciduria (MMA) subtypes. A novel mid-level data fusion strategy was subsequently described to effectively detect their subtle metabolic perturbations. In the proposed strategy, multivariate advantage was extracted via considering variable relationships in individual data blocks, separately. Further, it was extended to exploiting partial correlation across multiple data blocks, which induced the models between one data block and the first principal component (PC1) of another one for more valuable information recovery. The continuous values of PC1 replaced the traditional binary class labels, enabling suitable representation of disease heterogeneity numerically and subsequent accurate disease detection. Based on the unique role in exploratory data analysis, partial least squares discriminant analysis (PLS-DA) was coupled with bootstrap to form ensemble feature selection framework. It allowed the novel mid-level data fusion strategy to screen stable significant features. Investigated by two common MMA subtypes (mut and cblC) using urinary and blood metabolic profiles, the results showed that the novel mid-level data fusion strategy outperformed the traditional data fusion approaches both in predictive accuracy and biological interpretation. Besides, correlation network of the identified stable informative metabolites was visualized by chord plots, revealing different between-block biological interaction pattern for mut and cblC. Especially, in mut subtype, strong interactions could be occurred between the urinary metabolite of 3-OH-propionic-2 and two blood metabolite ratios (C3/C2 and C3/C0), separately. The findings deepened the biological insights into the disease pathology of mut and cblC. The integration of multiple analytical sources combined with the proposed novel mid-level data fusion strategy thus opened the possibilities to achieve desirable subtype classification for MMA. It suitably guided the early and timely medical intervention for IEMs.
Introduction: Breast cancer is the most common cancer in women and is the second most common cause of cancer related mortality. Metabolomics, the identification of small metabolites, is a technique for determining the amount of these metabolites. Objectives: This study aimed to identify markers for the early diagnosis of brain metastasis by metabolomic methods in breast cancer patients. Methods: A total of 88 breast cancer patients with distant metastases were included in the study. The patients were divided into two groups according to their metastasis status: patients with brain metastases and distant metastases without any brain metastases. Liquid chromatography quadrupole time-of-flight mass spectrometry (LC-qTOF-MS) and gas chromatography-mass spectrometry (GC-MS) analysis methods were used for metabolomic analyses. Results: 33 of them, 88 patients had brain metastasis, and 55 patients had distant metastases without brain metastasis. A total of 72 and 35 metabolites were identified by the GC-MS and LC-qTOF-MS analysis, respectively. 47 of them were found to be significantly different in patients with brain metastasis. The pathway analysis, performed with significantly altered metabolites, showed that aminoacyl tRNA biosynthesis, valine, leucine and isoleucine biosynthesis, alanine, aspartate, and glutamate metabolism, arginine biosynthesis, glycine, serine, and threonine metabolism pathways significantly altered in patients with brain metastasis. Predictive accuracies for have identifying the brain metastasis were performed with receiver operating characteristic (ROC) analysis, and the model with fifteen metabolites has 96.9% accuracy. Conclusions: While these results should be supported by prospective studies, these data are promising for early detection of brain metastasis with markers in liquid biopsy samples.
Cancer apparently seems to be incurable but a deeper introspection reveals other story. Cancer survival rate can be increased significantly with early detection and proper therapeutic intervention. It can be vividly justified by the fact that breast cancer survival rates in high income countries have reached over 80% while it is nearly 50% or even below in poorer countries. The reason for such contrasting picture can be understood as effective therapeutic facilities are available in above 90% of the developed countries as compared to treatment availability in below 30% of poorer countries. Proper diagnostic facilities are also lacking as only 26% of poorer countries can offer public sector pathology services according to 2017 WHO data. Conventional diagnostic approaches usually rely up on the clinical manifestation of aberration symptoms for disease diagnosis which is associated with significant delay in onset of therapeutic intervention. This is fatal, particularly for cancer as it urges earliest detection, preferably before metastasis for effective treatment outcome. State-of-the-art high-throughput proteomics and metabolomics techniques can offer solution as they identify the disease associated molecular signatures much earlier than the traditional methods. Further, high resolution, single-cell or even organelle level penetration, extreme sensitivity, considerable reliability, and automation render them as potential platforms for identification of novel therapeutic targets as well which can facilitate development of extremely precise target-specific drugs to overcome the systemic side-effects of traditional cancer chemotherapeutics. Separation based chromatographic and electrophoretic methods such as liquid or gas chromatography (LC/GC) or capillary electrophoresis (CE) coupled with mass spectrometry (MS), fluorescence-based methods, Raman-based methods, nuclear magnetic resonance (NMR), direct mass spectrometry imaging (MSI) are the mainstay of currently available proteomics and metabolomics analytical platforms. Although the spatiotemporal analyte dynamicity; generation, handling, and meaningful interpretation of the large data in biological context; dearth of universal standardized analytical protocols and specialized databases are posing limitations, continuous efforts from several stakeholders throughout the world are progressively alleviating the hurdles for transition of these high-end techniques from research arena to the field of routine clinical cancer diagnosis and therapy. Relentless progress in sample handling methods, instrumentation, computational software, and data analyses programs ensure intense prospect of the techniques in oncology arena.
For quite of time, supercritical fluid chromatography (SFC) is expected to make a big revolution in separation technologies. The technique is well known for developing fast and ultrafast high efficiency separation, improving the analytical method greenness. The evolution of SFC technologies (i.e., instrumentations, stationary phases) have shifted dramatically trends in SFC application from the chiral and pharmaceutical analysis toward new diverse application fields including drugs and bioactive compounds, metabolomics, environment, and food science analysis. Moreover, the applications have been greatly expanded toward the analysis of polar and very polar metabolites, resulting in certain successes. This review focuses on the most of SFC related studies during 2020 – 2021, covering the new development of SFC technologies, recent trends in application and method developments, and more importantly discusses the upcoming perspective regarding the art of supercritical fluid separation.
Metabolomics is an interdisciplinary area that integrates knowledge of instrumentation, data science, and biochemistry. Metabolomics studies the changes in a large number of metabolites after various treatments using analytical platforms. However, the interpretation approaches have not been completely investigated. Principal component analysis (PCA) is an unsupervised method that describes high throughput metabolite data, which is different from supervised approaches such as partial least-squares discriminant analysis (PLS-DA) which frequently has overfitting problems. The interpretation of PCA loadings, especially for studies with multiple study groups, is not well developed for metabolomics. In this study, a new method was reported that integrates PCA loading values with the commonly used statistical t-test analysis to significantly improve the convenience and efficiency of interpretation. The method was demonstrated using practical studies of NMR metabolomics on the extracts from sea anemone that were treated with six atrazine concentrations. The results indicated that the approach is suitable for multiple groups of metabolomics for early-stage discoveries, such as low concentrations and potentially longitudinal studies. In summary, this methodology may be critical in studies such as environmental metabolomics with various stimuli factors where the data interpretation was previously incompletely developed.
Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).
Ambient ionization has emerged as one of the hottest and fastest growing topics in mass spectrometry enabling sample analysis with minimal sample preparation. Introducing the subject and explaining the basic concepts and terminology, this book will provide a comprehensive, unique treatise devoted to the subject. Written by acknowledged experts, there are full descriptions on how new ionization techniques work, with an overview of their strengths, weaknesses and applications. This title will bring the reader right up to date, with both applications and theory, and will be suitable as a tutorial text for those starting in the field from a variety of disciplines.
Ambient ionization has emerged as one of the hottest and fastest growing topics in mass spectrometry enabling sample analysis with minimal sample preparation. Introducing the subject and explaining the basic concepts and terminology, this book will provide a comprehensive, unique treatise devoted to the subject. Written by acknowledged experts, there are full descriptions on how new ionization techniques work, with an overview of their strengths, weaknesses and applications. This title will bring the reader right up to date, with both applications and theory, and will be suitable as a tutorial text for those starting in the field from a variety of disciplines.
Acute lymphoblastic leukemia (ALL) is one of the common malignant tumors. Compared with childhood ALL, the treatment effect of adult B-cell ALL is less effective and remains a big challenge. In order to explore the pathogenesis of adult B-cell ALL and find new diagnostic biomarkers to develop sensitive diagnostic tools, we investigated the plasma metabolites of adult B-cell ALL by using ¹H NMR (nuclear magnetic resonance) metabolomics. Relative to healthy controls, adult B-cell ALL patients showed abnormal metabolism, including glycolysis, gluconeogenesis, amino acid metabolism, fatty acid metabolism and choline phospholipid metabolism. What's more important, we also found that the optimal combination of choline, tyrosine and unsaturated lipids has the potential to diagnose and prognose adult B-cell ALL in the clinic.
The best preventive method for breast cancer, which accounts for 18% of the most common cancer-related deaths among women, is early diagnosis. Metabolomic analysis plays an important role in both early diagnosis and tracking of the disease progress. In this study, targeted metabolomic analyses were performed on twenty-six selected metabolites, which were proposed in the literature for use in the early diagnosis of breast cancer, by examining the plasma samples of 105 healthy volunteers, 172 early stage and 92 metastatic breast cancer patients. To this purpose, an LC-ESI-MS/MS method was developed, validated, and applied to the analysis of the above plasma samples. Measurements were performed on a Merck SeQuant ZIC-HILIC column (50 x 4.6 mm, 5 µm) with the mobile phase run in the gradient mode with 0.1% formic acid in water and 0.1% formic acid in acetonitrile at a flow rate of 0.3 mL min-1 and 40 °C. The correlation coefficient values between 0.991-0.999 in the study range where each metabolite is linear shows the linearity of the method. The LOD and LLOQ values of the metabolites were between 5.9x10-5-1.0x10-2 μg mL-1 and 1.8x10-4-5.3x10-2 μg mL-1, respectively. The validation studies revealed that the method was linear, sensitive, precise, accurate, and selective. Our results showed that the metabolite identification based exclusively on the m/z values obtained from untargeted metabolomic studies must conform with standards before their clinical usage. Since only nine (citrulline, malic acid, glyceric acid, 3-hydroxybutyric acid, glutamine, proline, choline, fumaric acid, lactic acid) out of the twenty-six metabolites changed significantly among breast cancer patients, untargeted metabolomic analyses should be confirmed by targeted analyses before suggesting metabolites as biomarkers to avoid possible mismatches.
In this paper, a new approach aimed at the Fault Diagnosis with Full-scope Simulator based on the State Information Imaging (FDFSSII) in NPP is proposed. The FDFSSII approach first constructs a series of gray-image which presents the operating transient (included normal and fault condition) according to the real time monitoring data. Furthermore, the Machine Learning (ML) technology is employed to achieve image feature extraction and classification by analyzing and learning from massive amounts of historical and synthetic gray-image data – the image feature is extracted by the Kernel Principal Component Analysis (KPCA) and classified by the designed classifiers in different learning methods. Finally, diagnosis effect is evaluated by the F1 score. The simulation result shows that the FDFSSII approach has achieved good effect for the fault diagnosis in NPP. Meanwhile, it simplifies the process of nuclear reactor with the large monitoring data and provides useful support information to the operators.
Breast cancer (BC) is a heterogeneous malignancy that is responsible for a great portion of female cancer cases and cancer-related deaths in the United States. In comparison to other major BC subtypes, triple negative breast cancer (TNBC) presents with a relatively low survival rate and a high rate of metastasis. This has led to a strong, though largely unmet, need for more sensitive and specific methods of early stage TNBC (ES-TNBC) detection to combat its high-grade pathology and relatively low survival rate. The current study employs a liquid chromatography-tandem mass spectrometry assay capable of targeted, highly specific and sensitive detection of lipids to propose two diagnostic biomarker panels for TNBC/ES-TNBC. Using this approach, 110 lipids were reliably detected in 166 human plasma samples, 45 controls and 121 BC (96 non-TNBC and 25 TNBC) subjects. Univariate and multivariate analyses allowed the construction and application of a 19-lipid biomarker panel capable of distinguishing TNBC (and ES-TNBC) from controls, as well as, a 5-lipid biomarker panel capable of differentiating TNBC from non-TNBC and ES-TNBC from ES-non-TNBC. Receiver operating characteristic curves with notable classification performances were generated from the biomarker panels according to their orthogonal partial least squares-discrimination analysis models. TNBC was distinguished from controls with an area under the receiving operating characteristic curve (AUROC) = 0.93, sensitivity = 0.96, specificity = 0.76, and ES-TNBC from controls with an AUROC = 0.96, sensitivity = 0.95, and specificity = 0.89. TNBC was differentiated from non-TNBC with an AUROC = 0.88, sensitivity = 0.88, specificity = 0.79, and ES-TNBC from ES-non-TNBC with an AUROC = 0.95, sensitivity = 0.95, and specificity = 0.87. A pathway enrichment analysis between TNBC and controls also revealed significant disturbances in choline metabolism, sphingolipid signaling, and glycerophospholipid metabolism. To the best of our knowledge, this is the first study to propose a diagnostic lipid biomarker panel for TNBC detection. All raw mass spectrometry data have been deposited to MassIVE (dataset identifier: MSV000085324).
Backgroud
The impact of cancer in the society has created the necessity of new and faster theoretical models for the early diagnosis of cancer.
Methods
In the work, A mass spectrometry (MS) data analysis method based on star-like graph of protein and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the MS data set. Firstly, the MS data is reduced and transformed into the corresponding protein sequence. And then, the topological indexes of the star-like graph are calculated to describe each MS data of cancer sample. Finally, the SVM model is suggested to classify the MS data.
Results
Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models. The average prediction accuracy, sensitivity, and specificity of the model were 96.45%, 96.88%, and 95.67%, respectively, for [0,1] normalization data. and the model were 94.43%, 96.25%, and 91.11%, respectively, for [-1,1] normalization data.
Conclusion
The model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.
Locally advanced breast cancer patients have a worse prognosis compared to patients with localized tumors and require neoadjuvant treatment before surgery. The aim of this study was to characterize the systemic metabolic effect of neoadjuvant chemotherapy in patients with large primary breast cancers, and to relate these changes to treatment response and long-term survival. This study included 132 patients with large primary breast tumors randomized to receive neoadjuvant chemotherapy with or without the addition of the antiangiogenic drug bevacizumab. Tumor biopsies and serum were collected before and during treatment; serum additionally six weeks after surgery. Samples were analyzed by nuclear magnetic resonance spectroscopy (NMR). Correlation analysis showed low correlations between metabolites measured in cancer tissue and serum. Multilevel partial least squares discriminant analysis (PLS-DA) showed clear changes in serum metabolite levels during treatment (p-values ≤ 0.001), including unfavorable changes in lipid levels. PLS-DA revealed metabolic differences between tissue samples from survivors and non-survivors collected 12 weeks into treatment with an accuracy of 72% (p-value = 0.005), however this was not evident in serum samples. Our results demonstrate a potential clinical application for serum-metabolomics for patient-monitoring during and after treatment, and indicate potential for tissue NMR spectroscopy for predicting patient survival.
The advancement of mass spectrometry-based analytical platform largely facilitates small-molecule metabolomics studies, which allows simultaneously analysis of a large number of metabolites from bio-samples and give a general picture of metabolic changes related to diseases or environmental alteration. Due to the large diversity of cellular metabolites, globally and precisely examining metabolic profile remains the most challenging part in metabolomic experiment. Mass spectrometry coupled with liquid chromatography enhances sensitivity and resolving power of metabolites identification and quantification, as well as versatility of analyzing a wide array of metabolites. In this chapter, we discussed the technical aspects of each step in the workflow of metabolomics studies we aimed to give technical guidelines for metabolomics investigation design and approach.
Metabolite profiling in complex biological matrices such as serum requires high-throughput technologies capable of accurate and reproducible quantitative analysis and detection of slight differences in metabolite concentrations. Gas chromatography-mass spectrometry (GC-MS) is widely used for characterizing the metabolome. This chapter summarizes the necessary preparatory steps required to profile the metabolome using GC-MS. While this chapter focuses on evaluating polar metabolites in serum samples, the methods can be adapted to quantify nonpolar metabolites in other biological matrices.
Global analysis of 1H-NMR spectra of serum is an appealing approach for the rapid detection of cancer. To evaluate the usefulness of this method
in distinguishing between mammary tumor-bearing mice and healthy controls, we conducted 1H-NMR metabonomic analyses on serum samples obtained from the following: 10 mice inoculated with a highly-metastatic mammary
carcinoma cell line, 10 mice inoculated with a “normally” metastatic mammary carcinoma cell line, and 10 healthy controls.
Following standard spectral processing and subsequent data reduction, we applied unsupervised Principal Component Analysis
(PCA) to determine if unique metabolic fingerprints for different categories of metastatic breast cancer in serum exist. The
PCA method correctly separated sera of tumor-bearing mice from that of normal healthy controls, as shown using the scores
plot which indicated that sera classes from tumor-bearing mice did not share multivariate space with that from healthy controls.
In addition, this technique was capable of distinguishing between classes of varying metastatic ability in this system. Metabolites
apparently responsible for separation between diseased and healthy mice include lactate, taurine, choline, and sugar moieties.
Results of this study suggest that 1H-NMR spectra of mouse serum analyzed using PCA statistical methods indicate separation of tumor-bearing mice from healthy
normal controls, justifying further study of the use of 1H-NMR metabonomics for cancer detection using serum.
In this study, we hypothesized that the altered insulin and glucose levels in male pancreatic cancer patients reported in a recent JAMA article would result in an altered lipid profile in the blood of pancreatic cancer patients when compared to controls (Stolzenberg-Solomon etal., 2005). Proton nuclear magnetic resonance (NMR) spectra of human lipophilic plasma extracts were used in order to build partial least squares discriminant function (PLS-DF) models that classified samples as belonging to the pancreatic control group or to the pancreatic cancer group. The sensitivity, specificity, and overall accuracy of the PLS-DF models based on 4 bins were 96%, 88%, and 92%, respectively. The sensitivity, specificity, and overall accuracy of the PLS-DF models based on 5 bins were 98%, 94%, and 96%, respectively. The sensitivity, specificity and overall accuracy of both the 4-bin and 5-bin PLS-DF models dropped only 1–2% during leave-25%-out cross-validation testing. Mass spectrometric profiling of phospholipids in plasma found three phosphatidylinositols that were significantly lower in pancreatic cancer patients than in healthy controls. The cancer models are based upon changes in lipid profiles that may provide a more sensitive and accurate diagnosis of pancreatic cancer than current methods that are based upon a single biomarker.
Classifying groups of individuals based on their metabolic profile is one of the main topics in metabolomics research. Due
to the low number of individuals compared to the large number of variables, this is not an easy task. PLSDA is one of the
data analysis methods used for the classification. Unfortunately this method eagerly overfits the data and rigorous validation
is necessary. The validation however is far from straightforward. Is this paper we will discuss a strategy based on cross
model validation and permutation testing to validate the classification models. It is also shown that too optimistic results
are obtained when the validation is not done properly. Furthermore, we advocate against the use of PLSDA score plots for inference
of class differences.
High-resolution 750 MHz 1H NMR spectra of control human blood plasma have been measured and assigned by the concerted use of a range of spin-echo, two-dimensional J-resolved, and homonuclear and heteronuclear (1H-13C) correlation methods. The increased spectral dispersion and sensitivity at 750 MHz enable the assignment of numerous 1H and 13C resonances from many molecular species that cannot be detected at lower frequencies. This work presents the most comprehensive assignment of the 1H NMR spectra of blood plasma yet achieved and includes the assignment of signals from 43 low M(r) metabolites, including many with complex or strongly coupled spin systems. New assignments are also provided from the 1H and 13C NMR signals from several important macromolecular species in whole blood plasma, i.e., very-low-density, low-density, and high-density lipoproteins, albumin, and alpha 1-acid glycoprotein. The temperature dependence of the one-dimensional and spin-echo 750 MHz 1H NMR spectra of plasma was investigated over the range 292-310 K. The 1H NMR signals from the fatty acyl side chains of the lipoproteins increased substantially with temperature (hence also molecular mobility), with a disproportionate increase from lipids in low-density lipoprotein. Two-dimensional 1H-13C heteronuclear multiple quantum coherence spectroscopy at 292 and 310 K allowed both the direct detection of cholesterol and choline species bound in high-density lipoprotein and the assignment of their signals and confirmed the assignment of most of the lipoprotein resonances.
The later that a molecule or molecular class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery programme. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery-and-development process.
To apply genomic knowledge effectively in drug discovery, mechanistic connectivities between genetic variation and disease processes need to be established via systems biology approaches. Humans have hundreds of functionally specialized cell types that interact differentially with environmental factors to influence disease development and to modulate the effects of drugs. Metabonomics can provide a means of modelling these interactions, but the relationships between 'endogenous' metabolic processes (coded in the genome and intrinsic to cellular function) and 'xenobiotic' (foreign compound) metabolism are poorly understood, especially with respect to environmental factors. We present an overview of 'global' mammalian metabolic conversions that should be accounted for in human systems biology models and propose a new probabilistic approach to help understand gene-disease relationships and vexed issues of idiosyncratic drug toxicity.
In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for sample classification. However, the well-known variability of some chemical shifts in 1H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.
Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz 1H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.
A recent innovation in mass spectrometry is the ability to record mass spectra on ordinary samples, in their native environment,
without sample preparation or preseparation by creating ions outside the instrument. In desorption electrospray ionization
(DESI), the principal method described here, electrically charged droplets are directed at the ambient object of interest;
they release ions from the surface, which are then vacuumed through the air into a conventional mass spectrometer. Extremely
rapid analysis is coupled with high sensitivity and high chemical specificity. These characteristics are advantageously applied
to high-throughput metabolomics, explosives detection, natural products discovery, and biological tissue imaging, among other
applications. Future possible uses of DESI for in vivo clinical analysis and its adaptation to portable mass spectrometers
are described.
Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry coupled with rapid chemometric analysis were used to identify chemical differences in metabolite extracts isolated from yeast cells either metabolizing glucose (repressed (R) cells) via fermentation or metabolizing ethanol by respiration (derepressed (DR) cells). Principal component analysis (PCA) followed by parallel factor analysis (PARAFAC) in concert with the LECO ChromaTOF software located and identified the differences in composition between the two types of cell extracts and provided a reliable ratio of the metabolite concentrations. In this report, we demonstrate the analytical method developed to provide relatively rapid analysis of three selective mass channels (m/z 73, 205, 387), although in principle all collected mass channels could be analyzed. Twenty-six metabolites that differentiate repressed cells from derepressed cells were identified. The DR/R ratio of metabolite concentrations ranged from 0.02 for glucose to 67 for trehalose. The average biological variation of the sample extracts was 31%. This analysis demonstrates the utility and benefit of using PCA combined with PARAFAC and ChromaTOF software on extremely complex samples to derive useful information from complex three-dimensional chromatographic data objectively and relatively rapidly.
Direct analysis in real time (DART) is implemented on a time-of-flight (TOF) mass spectrometer, and used for the generation of fatty acid methyl esters (FAMEs) ions from whole bacterial cells.
Three different pattern recognition methods before and after orthogonal signal correction (OSC) were employed to perform the metabonomic analysis of 1H NMR spectra recorded on healthy human sera, in order to explore the potential of applying 1H NMR-based metabonomics to clinical research. At first, 78 healthy human sera were collected after a routine fasting for 8 h, and the corresponding 1D 1H NMR spectra were recorded on a Varian Unity INOVA-600 spectrometer, and then three pattern recognition analyses, PCA (principal component analysis), PLS-DA (partial least squares-discriminant analysis), and SIMCA (soft independent modeling of class analogy), were performed, respectively. In spite of no specific sample-collecting restriction on foods, life styles, and physiological cycles, the PLS-DA method after OSC is able to distinguish the NMR metabonomic profiles of male sera from those of female sera, more perfectly than both the PCA and SIMCA. Furthermore, the major NMR integral regions relevant to gender classification from PLS-DA after OSC were identical with those from PLS-DA without OSC filter in the literature. In the figure of displaying the variation of PLS-DA model before OSC and after removing different OSC latent variables (LVs), the eigenvalues of the first and second OSC-removed LVs were much greater than others. After removing two LVs by OSC, the remaining sum of square (RSS) in the X block was 20.82%, that is, 79.18% information unrelated to Y was removed from the PLS-DA model. Meanwhile, the LV number of PLS-DA model attained to one; while the LV number was two for the model with the first LV being removed by OSC, and three for the model without OSC. R2X, R2Y, and Q2 (cum) are usually used to evaluate the quality of PLS-DA model. R2X and R2Y are the fraction of the sum of square of the entire X's and Y's explained by the current LV of PLS-DA, and represent the variance of X and Y variables, respectively; while Q2 is cross validated R2. Q2 (cum) reflects the cumulative cross-validated percent of the total variation of the X's and Y's that can be predicted by the current LV of PLS-DA model. In our study, after OSC filtering the first two LVs, R2X reached the minimum, suggesting that the least systematic variance should be present in the current PLS-DA model. Meanwhile, both R2Y and Q2 (cum) were always higher than 80%, indicative of the good quality of the PLS-DA model. Obviously, OSC is capable of eliminating the influence of dietary and environmental factors, and decreasing the heterogeneity of samples, which is fairly useful and important for clinical investigations. Additionally, the appropriate number of OSC-removed LVs should be determined on the basis of RSS in the X block, eigenvalue of OSC-removed latent variables, LV number and the qualitative indicators of the PLS-DA model.
A new pattern discovery method based on the best individual feature selection and BP neural network was proposed to select characteristic metabolites in urine which were most correlative with breast cancer. Four nucleosides (orotidine, 1-methyladenosine, S-adenosylmethionine, and N2-methylguanosine), which were identified by using HPLC/MS/MS, were selected out and composed a characteristic pattern for diagnosis of breast cancer. Subsequently, BP neural network was investigated as potential tools to diagnose breast cancer by using those four nucleosides as the input features. The results of Leave-One-Out and independent cross validation show that the prediction rate of the model built with BP neural network is higher than 90%. As a consequence, those four selected nucleosides could be considered as a characteristic pattern for the diagnosis of breast cancer.
Principal component analysis has often been dealt with in textbooks as a special case of factor analysis, and this tendency has been continued by many computer packages which treat PCA as one option in a program for factor analysis—see Appendix A2. This view is misguided since PCA and factor analysis, as usually defined, are really quite distinct techniques. The confusion may have arisen, in part, because of Hotelling’s (1933) original paper, in which principal components were introduced in the context of providing a small number of ‘more fundamental’ variables which determine the values of the p original variables. This is very much in the spirit of the factor model introduced in Section 7.1, although Girschick (1936) indicates that there were soon criticisms of Hotelling’s method of PCs, as being inappropriate for factor analysis. Further confusion results from the fact that practitioners of ‘factor analysis’ do not always have the same definition of the technique (see Jackson, 1981). The definition adopted in this chapter is, however, fairly standard.
Multiblock and hierarchical PCA and PLS methods have been proposed in the recent literature in order to improve the interpretability of multivariate models. They have been used in cases where the number of variables is large and additional information is available for blocking the variables into conceptually meaningful blocks. In this paper we compare these methods from a theoretical or algorithmic viewpoint using a common notation and illustrate their differences with several case studies. Undesirable properties of some of these methods, such as convergence problems or loss of data information due to deflation procedures, are pointed out and corrected where possible. It is shown that the objective function of the hierarchical PCA and hierarchical PLS methods is not clear and the corresponding algorithms may converge to different solutions depending on the initial guess of the super score. It is also shown that the results of consensus PCA (CPCA) and multiblock PLS (MBPLS) can be calculated from the standard PCA and PLS methods when the same variable scalings are applied for these methods. The standard PCA and PLS methods require less computation and give better estimation of the scores in the case of missing data. It is therefore recommended that in cases where the variables can be separated into meaningful blocks, the standard PCA and PLS methods be used to build the models and then the weights and loadings of the individual blocks and super block and the percentage variation explained in each block be calculated from the results. © 1998 John Wiley & Sons, Ltd.
Metabolomics is a growing area in the field of systems biology. Metabolomics has already a long history and also the connection of metabolomics with chemometrics goes back some time. This review discusses the symbiosis of metabolomics and chemometrics with emphasis on the medical domain, puts the combination of the two in historical perspective and tries to give ideas for future research. Copyright © 2006 John Wiley & Sons, Ltd.
This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed. © 2006 Wiley Periodicals, Inc., Mass Spec Rev 26:51–78, 2007
Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter‐correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross‐validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis (CA) in order to handle qualitative variables and as multiple factor analysis (MFA) in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen‐decomposition of positive semi‐definite matrices and upon the singular value decomposition (SVD) of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc.
This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis
Statistical and Graphical Methods of Data Analysis > Dimension Reduction
A generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described. O-PLS removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity). In mathematical terms this is equivalent to removing systematic variation in X that is orthogonal to Y. In an earlier paper, Wold et al. (Chemometrics Intell. Lab. Syst. 1998; 44: 175–185) described orthogonal signal correction (OSC). In this paper a method with the same objective but with different means is described. The proposed O-PLS method analyzes the variation explained in each PLS component. The non-correlated systematic variation in X is removed, making interpretation of the resulting PLS model easier and with the additional benefit that the non-correlated variation itself can be analyzed further. As an example, near-infrared (NIR) reflectance spectra of wood chips were analyzed. Applying O-PLS resulted in reduced model complexity with preserved prediction ability, effective removal of non-correlated variation in X and, not least, improved interpretational ability of both correlated and non-correlated variation in the NIR spectra. Copyright © 2002 John Wiley & Sons, Ltd.
The application of in vitro NMR spectroscopy and HNMR metabonomics to breast cancer characterization and detection is discussed. It is a powerful tool for analyzing both aqueous and lipophilic extracts from breast cancer tissue, allowing for the detection and identification of several metabolites that serve as biomarkers for various stages of disease. It is found that valuable data can be obtained by collecting NMR spectroscopic data from sample sources such as plasma, cyst fluid, and urine. Applications of similar techniques to breast cancer analysis provide clinicians with vital information concerning breast cancer incidence at a much earlier level of expression, which would lead to the initiation of earlier treatment and chances for survival.
Near-infrared (NIR) spectra are often pre-processed in order to remove systematic noise such as base-line variation and multiplicative scatter effects. This is done by differentiating the spectra to first or second derivatives, by multiplicative signal correction (MSC), or by similar mathematical filtering methods. This pre-processing may, however, also remove information from the spectra regarding Y (the measured response variable in multivariate calibration applications). We here show how a variant of PLS can be used to achieve a signal correction that is as close to orthogonal as possible to a given Y-vector or Y-matrix. Thus, one ensures that the signal correction removes as little information as possible regarding Y. In the case when the number of X-variables (K) exceeds the number of observations (N), strict orthogonality is obtained. The approach is called orthogonal signal correction (OSC) and is here applied to four different data sets of multivariate calibration. The results are compared with those of traditional signal correction as well as with those of no pre-processing, and OSC is shown to give substantial improvements. Prediction sets of new data, not used in the model development, are used for the comparisons.
Extensive optimisation of a mathematical model's fit to a relatively small set of empirical data, may lead to over-optimistic validation results. If the assessment of the final, optimised model is based on the same validation method and the same input data that were used as basis for the extensive model optimisation, accumulated spurious correlations may appear as real predictive ability in the final model validation. An example of this is the use of extensive variable selection in multiple regression, based on a cross-model validation scheme.To illustrate the over-optimism problem in optimisation based on conventional one-layered validation, an artificial data set, with only random numbers was submitted to regression modelling. The model was optimised by stepwise variable selection. A very good apparent predictive ability for y from X was found in the final model by leave-one-out cross-validation (84%), after the number of X-variables had been reduced stepwise from 500 to 29. Finally, the performance of the cross-model validation is tested on one large QSAR data set. Several calibration sets were chosen randomly and a regression model optimised by variable selection. The prediction accuracy of these models was compared to the cross-validation and cross-model validation results. In these tests cross-model validation gives the better measure of model predictive ability.
NMR spectra of extracted blood spots were used to investigate the possibility for the development of a new method for mass screening concerning the diagnosis of inborn errors of metabolism (IEM). Blood spots were collected on filter papers from normal, phenylketonuric (PKU) and maple syrup urine disease (MSUD) subjects and their Carr-Purcell-Meiboom-Gill (CPMG) NMR spectra were acquired. The spectra were reduced to a number of spectral descriptors and principal component analysis (PCA) was performed. The scores plot showed that PKU and MSUD samples were well discriminated from the main cluster of points.
Many "omics" techniques have been developed for one goal: biomarker discovery and early diagnosis of human cancers. A comprehensive review of mass spectrometry-based "omics" approaches performed on various biological samples for molecular diagnosis of human cancers is presented in this article. Furthermore, the existing and potential problems/solutions (both de facto experimental and bioinformatic challenges), and future prospects have been extensively discussed. Although the use of present omic methods as diagnostic tools are still in their infant stage and consequently not ready for immediate clinical use, it can be envisaged that the "omics"-based cancer diagnostics will gradually enter into the clinic in next 10 years as an important supplement to current clinical diagnostics.
Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms `transcriptome' and `proteome', the set of metabolites synthesized by a biological system constitute its `metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
Two key bottlenecks in pharmaceutical bioanalysis are sample cleanup and chromatographic separations. Although multiple approaches have been developed in the past decade to either shorten or multiplex these steps, they remain the rate limiting steps as ADME (Absorption, Distribution, Metabolism, and Excretion) property screening is being routinely incorporated into the drug discovery process. In this work, a novel system incorporating an automated Direct Analysis in Real Time (DART) ionization source coupled with a triple-quadrupole mass spectrometer has been developed and evaluated for quantitative bioanalysis. This system has the capability of directly analyzing samples from their biological matrixes and therefore potentially eliminating the need for sample cleanup and chromatographic separations. A LEAP Technologies autosampler was customized to perform the automated sample introduction into the DART beam with high precision, which significantly improved the reproducibility of the method. Additional pumping was applied to the atmospheric pressure inlet on the mass spectrometer to compensate for the increased vacuum load because of the use of high-flow helium by the DART. This resulted in an improvement of detection sensitivity by a factor of 10 to 100 times. Matrix effects for a diversified class of compounds were evaluated directly from untreated raw plasma and were found to range from approximately 0.05 to 0.7. Precision and accuracy were also tested for multiple test compounds over a dynamic range of four orders of magnitude. The system has been used to analyze biological samples from both in vivo pharmacokinetic studies and in vitro microsomal/S9 stability studies, and the results generated were similar to those obtained with conventional LC/MS/MS methods. Overall, this new automated DART-triple quadrupole mass spectrometer system has demonstrated significant potential for high-throughput bioanalysis.
Positive ions in the direct analysis in real time (DART) ion source are commonly formed by proton transfer. However, the DART source is similar to atmospheric pressure photoionization (APPI) in that it can produce molecular ions as well as protonated molecules, although the two sources differ in the initial ion formation process. This report discusses some of the factors that influence molecular ion formation in DART and shows how the DART source can be used to analyze "difficult" or nonpolar compounds such as alkanes and cholesterol. Trace reagent ions including NO(+) and O(2)(+)* formed from atmospheric gases are shown to play important roles in DART ionization. The use of the DART source as a gas chromatography/mass spectrometry (GC/MS) interface is demonstrated to show the difference between mass spectra obtained using conditions that favor proton transfer and those that favor molecular ion formation.
Current clinical strategy for staging and prognostication of colorectal cancer (CRC) relies mainly upon the TNM or Duke system. This clinicopathological stage is a crude prognostic guide because it reflects in part the delay in diagnosis in the case of an advanced cancer and gives little insight into the biological characteristics of the tumor. We hypothesized that global metabolic profiling (metabonomics/metabolomics) of colon mucosae would define metabolic signatures that not only discriminate malignant from normal mucosae, but also could distinguish the anatomical and clinicopathological characteristics of CRC. We applied both high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) and gas chromatography mass spectrometry (GC/MS) to analyze metabolites in biopsied colorectal tumors and their matched normal mucosae obtained from 31 CRC patients. Orthogonal partial least-squares discriminant analysis (OPLS-DA) models generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from malignant samples (Q(2) > 0.50, Receiver Operator Characteristic (ROC) AUC >0.95, using 7-fold cross validation). A total of 31 marker metabolites were identified using the two analytical platforms. The majority of these metabolites were associated with expected metabolic perturbations in CRC including elevated tissue hypoxia, glycolysis, nucleotide biosynthesis, lipid metabolism, inflammation and steroid metabolism. OPLS-DA models showed that the metabolite profiles obtained via HR-MAS NMR could further differentiate colon from rectal cancers (Q(2)> 0.60, ROC AUC = 1.00, using 7-fold cross validation). These data suggest that metabolic profiling of CRC mucosae could provide new phenotypic biomarkers for CRC management.
The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.
1H NMR spectroscopy has been used to assess long-term toxicological effects of a rare earth. Male Wistar rats were administrated orally with La(NO3)3 at doses of 0.1, 0.2, 2.0, 10, and 20 mg/kg body wt, resp., for 3-6 months. Urine was collected at 1, 2, and 3 months and serum samples were taken after 6 months. Numerous low-M(r) metabolites in rats serum and rats urine, including creatinine, citrate, glucose, ketone bodies, trimethylamine N-oxide (TMAO), and various amino acids, were identified on 400- and 500-MHz 1H NMR spectra. La3+-induced renal and liver damage is characterized by an increase in the amounts of the excreted ketone bodies, amino acids, lactate, ethanol, succinate, TMAO, dimethylamine, and taurine and a decrease in citrate, glucose, urea, and allantoin. Information on the molecular basis of the long-term toxicity of La(NO>3)3 was derived from the abnormal patterns of metabolite excretions. An assay of some biochemical indexes and analysis of some enzymes in plasma supported NMR results.
Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
Metabolic phenotyping, or metabotyping, is increasingly being used as a probe in functional genomics studies. However, such profiling is subject to intrinsic physiological variation found in all animal populations. Using a nuclear magnetic resonance-based metabonomic approach, we show that diurnal variations in metabolism can obscure the interpretation of strain-related metabolic differences in two phenotypically normal mouse strains (C57BL10J and Alpk:ApfCD). To overcome this problem, diurnal-related metabolic variation was removed from these spectral data by application of orthogonal signal correction (OSC), a data filtering method. Interpretation of the removed orthogonal variation indicated that diurnal-related variation had been removed and that the AM samples contained higher levels of creatine, hippurate, trimethylamine, succinate, citrate and 2-oxo-glutarate and lower levels of taurine, trimethylamine-N-oxide, spermine and 3-hydroxy-iso-valerate relative to the PM samples. We propose OSC will have great potential removing confounding variation obscuring subtle changes in metabolism in functional genomic studies and will be of benefit to optimising interpretation of proteomic and genomic datasets.
Using one-dimensional (1D) and two-dimensional (2D) proton nuclear magnetic resonance (NMR) methods, the perchloric acid extract of involved (n = 11) and noninvolved (n = 12) axillary lymph nodes (ALN) of breast cancer patients was investigated. Resonances from 40 metabolites such as lactate (Lac), glucose, several amino acids (alanine, lysine, glutamic acid, glutamine, etc.), nucleotides (adenosine triphosphate, guanosine triphosphate, uridine triphosphate, uridine monophosphate, etc.), membrane metabolites [glycerophosphocholine (GPC), phosphocoline (PC), phosphoethanolamine (PE), choline] were unambiguously assigned in both the involved and noninvolved ALN. The concentration of PC/GPC (p = 0.002) was significantly higher in the involved compared to noninvolved nodes. In addition, the concentration of glycolytic product Lac (p = 0.0001) was also found to be significantly higher in involved nodes. Increased concentration of membrane metabolites PC/GPC may be attributed to increased membrane synthesis in malignant cells and, therefore, suggests the presence of metastatic cells in lymph nodes. The higher concentration of Lac is indicative of the presence of malignant cells that derive energy via anaerobic glycolytic pathway. Present results demonstrate the potentials of in vitro proton NMR in detecting malignant cells in ALN and such studies may have an important bearing in determining the prognosis of breast cancer patients.
In the post-genomic era, increasing efforts have been made to describe the relationship between the genome and the phenotype in cells and organisms. It has become clear that even a complete understanding of the state of the genes, messages, and proteins in a living system does not reveal its phenotype. Therefore, researchers have started to study the metabolome (or the metabolic complement of functional genomics). Within this context, mass spectrometry (MS) has increasingly occupied a central position in the methodologies developed for determination of the metabolic state. This review is mainly focused on the status of MS in the metabolome field, trying to direct the reader to the main approaches for analysis of metabolites, reviewing basic methodologies in sample preparation, and the most recent MS techniques introduced. Apart from the description of the different methods, this review will try to state a general comparison between the several different techniques that involve MS and metabolite analysis, and will highlight their limitations and preferred applicability.
A new ion source has been developed for rapid, noncontact analysis of materials at ambient pressure and at ground potential. The new source, termed DART (for "Direct Analysis in Real Time"), is based on the reactions of electronic or vibronic excited-state species with reagent molecules and polar or nonpolar analytes. DART has been installed on a high-resolution time-of-flight mass spectrometer (TOFMS) that provides improved selectivity and accurate elemental composition assignment through exact mass measurements. Although DART has been applied to the analysis of gases, liquids, and solids, a unique application is the direct detection of chemicals on surfaces without requiring sample preparation, such as wiping or solvent extraction. DART has demonstrated success in sampling hundreds of chemicals, including chemical agents and their signatures, pharmaceutics, metabolites, peptides and oligosaccharides, synthetic organics, organometallics, drugs of abuse, explosives, and toxic industrial chemicals. These species were detected on various surfaces, such as concrete, asphalt, human skin, currency, airline boarding passes, business cards, fruits, vegetables, spices, beverages, body fluids, horticultural leaves, cocktail glasses, and clothing. DART employs no radioactive components and is more versatile than devices using radioisotope-based ionization. Because its response is instantaneous, DART provides real-time information, a critical requirement for screening or high throughput.
Partial least squares discriminant analysis (PLS-DA) provides a sound statistical basis for the selection of a limited number of gene transcripts most effective in discriminating different lung tumoral histotypes. The potentialities of the PLS-DA approach are pointed out by its ability to identify genes which, according to current knowledge, are considered molecular markers for colon cancer diagnostics and classification. Indeed application of PLS-DA to in vivo data allowed identification of a set of genes able to discriminate primary lung tumours from colon metastases.
Desorption electrospray ionization mass spectrometry (DESI-MS) and nuclear magnetic resonance (NMR) spectroscopy are used to provide data on urine examined without sample preparation to allow differentiation between diseased (lung cancer) and healthy mice. Principal component analysis (PCA) is used to shortlist compounds with potential for biomarker screening which are responsible for significant differences between control urine samples and samples from diseased animals. Similar PCA score plots have been achieved by DESI-MS and NMR, using a subset of common detected metabolites. The common compounds detected by DESI and NMR have the same changes in sign of their concentrations thereby indicating the usefulness of corroborative analytical methods. The effects of different solvents and surfaces on the DESI mass spectra are also evaluated and optimized. Over 80 different metabolites were successfully identified by DESI-MS and tandem mass spectrometry experiments, with no prior sample preparation.
Urine metabolic profiles of patients with inborn errors of metabolism were examined with nuclear magnetic resonance (NMR) and desorption electrospray ionization mass spectrometry (DESI-MS) methods. Spectra obtained from the study of urine samples from individual patients with argininosuccinic aciduria (ASA), classic homocystinuria (HCY), classic methylmalonic acidemia (MMA), maple syrup urine disease (MSUD), phenylketonuria (PKU) and type II tyrosinemia (TYRO) were compared with six control patient urine samples using principal component analysis (PCA). Target molecule spectra were identified from the loading plots of PCA output and compared with known metabolic profiles from the literature and metabolite databases. Results obtained from the two techniques were then correlated to obtain a common list of molecules associated with the different diseases and metabolic pathways. The combined approach discussed here may prove useful in the rapid screening of biological fluids from sick patients and may help to improve the understanding of these rare diseases.
The high resolution nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) is compared and combined with multivariate statistics that further drives the field of metabolomics. The complementary analytical features of NMR and MS increases the opportunities for both the methods to create more comprehensive metabolic profiling. The high sensitivity of MS makes it an important method for measuring metabolites in complex biosamples. The MS methods allows for reliable metabolite identification. Current challenges for MS-based metabolomics include the development of more robust methods for chromatographic separation, data reduction methods, and reduction of matrix effects including ion suppression. Compared to MS, The NMR spectroscopy yields relatively low-sensitivity measurements. NMR based metabolic profiling is considered to be performed successfully, as the method is highly quantitative and reproducible.
The collection of global metabolic data and their interpretation (both spectral and biochemical) using modern spectroscopic techniques and appropriate statistical approaches, are known as 'metabolic profiling', 'metabonomics' or 'metabolomics'. This review addresses 1H-nuclear magnetic resonance (NMR)-based metabolomic principles and their application in biomedical science, with special emphasis on their potential in translational research in transplantation, oncology, and drug toxicity or discovery. Various steps in metabolomics analysis are described in order to illustrate the types of biological samples, their respective handling and preparation for 1H-NMR analysis; provide a rationale for using pattern-recognition techniques (spectral database concept) versus quantitative 1H-NMR-based metabolomics (metabolite database concept); and identify necessary technological and logistical future developments that will allow 1H-NMR-based metabolomics to become an established tool in biomedical research and patient care.
Dietary composition has been shown to influence metabolism and to impact on the prevalence and risk for certain diseases, but hitherto, there have been no systematic studies on the effects of dietary modulation of human metabolic phenotype (metabotype). Here, we have applied 1H NMR spectroscopy in combination with multivariate statistical analysis to characterize the effects of three diets: "vegetarian", "low meat", and "high meat" on the metabotype signature of human participants. Twelve healthy male participants (age range of 25-74 years) consumed each of these diets, in a randomized order, for continuous 15-day-periods with an intervening washout period between each diet of 7 days duration. Each participant provided three consecutive 24-hour urine collections on days 13, 14, and 15 of each dietary period, and 1H NMR spectra were acquired on all samples. Pattern recognition analysis allowed differentiation of the characteristic metabolic signatures of the diets with creatine, carnitine, acetylcarnitine, and trimethylamine-N-oxide (TMAO) being elevated in the high-meat consumption period. Application of orthogonal projection to latent structure discriminant analysis (O-PLS-DA) allowed the low-meat diet and vegetarian diet signatures to be characterized, and p-hydroxyphenylacetate (a microbial mammalian cometabolite) was higher in the vegetarian than meat diet samples, signaling an alteration of the bacterial composition or metabolism in response to diet. This work shows the potential for the routine use of metabonomics in nutritional and epidemiological studies, in characterizing and predicting the metabolic effects and the influence of diet on human metabotypes.
A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.
The effect of diet on metabolites found in rat urine samples has been investigated using nuclear magnetic resonance (NMR) and a new ambient ionization mass spectrometry experiment, extractive electrospray ionization mass spectrometry (EESI-MS). Urine samples from rats with three different dietary regimens were readily distinguished using multivariate statistical analysis on metabolites detected by NMR and MS. To observe the effect of diet on metabolic pathways, metabolites related to specific pathways were also investigated using multivariate statistical analysis. Discrimination is increased by making observations on restricted compound sets. Changes in diet at 24-h intervals led to predictable changes in the spectral data. Principal component analysis was used to separate the rats into groups according to their different dietary regimens using the full NMR, EESI-MS data or restricted sets of peaks in the mass spectra corresponding only to metabolites found in the urea cycle and metabolism of amino groups pathway. By contrast, multivariate analysis of variance from the score plots showed that metabolites of purine metabolism obscure the classification relative to the full metabolite set. These results suggest that it may be possible to reduce the number of statistical variables used by monitoring the biochemical variability of particular pathways. It should also be possible by this procedure to reduce the effect of diet in the biofluid samples for such purposes as disease detection.
It is of increasing interest and practical importance to develop convenient methods based on mass spectrometry for high-throughput analyses of biological samples. This is usually difficult because of the complex matrix and ion suppression effects. Generation of ions at ambient conditions is a promising solution to these problems because the sample is easily accessible and the ion suppression effect is reduced significantly. A new method for rapid on-line detection of metabolic markers in complex biological samples is described here. It combines atmospheric pressure desorption sampling by a gentle stream of air or nitrogen with extractive electrospray ionization (EESI) and mass spectrometric analysis. The resulting mass spectral fingerprints are shown to be able to detect spoilage of meat even in the frozen (-20 degrees C) state and the contamination of spinach by E. coli, and to identify metabolites and contaminants on human skin within seconds, in an on-line and high-throughput fashion. Typical molecular markers are identified using MS/MS data and by comparison with reference compounds. Differences between closely related samples are easily visualized by using principal component analysis (PCA) of the mass spectra data. The detection limit achieved is 10 fg/cm2 (S/N = 3) for histamine on the surface of frozen meat. The technique reported here shows potential for more advanced applications in multiple disciplines, including food regulation, homeland security, in vivo metabolomics, and clinical diagnosis.
- Ma Constantinou
- E Papakonstantinou
- D Benaki
- M Spraul
- K Shupis
- Ma Koupparis
- E Mikros
Constantinou MA, Papakonstantinou E, Benaki D, Spraul M, Shupis K, Koupparis MA, Mikros E.
Anal Chim Acta 2004;511:303.
- S Wold
- H Antti
- F Lindgren
- J Ohman
Wold S, Antti H, Lindgren F, Ohman J. J Chemometr Intell Lab Syst 1998;44:175.
- Tl Whitehead
- B Monzavi-Karbassi
- T Kieber-Emmons
Whitehead TL, Monzavi-Karbassi B, Kieber-Emmons T. Metabolomics 2005;1:269.
- Cy Pierce
- Jr Barr
- Rb Cody
- Rf Massung
- Ar Woolfitt
- H Moura
- Ha Thompson
- Fm Fernandez
Pierce CY, Barr JR, Cody RB, Massung RF, Woolfitt AR, Moura H, Thompson HA, Fernandez
FM. Chem Commun 2007;8:807.