Metabolomics of colorectal cancer: past and current analytical platforms

Metabolomics is coming of age as an important area of investigation which may help reveal answers to questions left unanswered or only partially understood from proteomic or genomic approaches. Increased knowledge of the relationship of genes and proteins to smaller biomolecules (metabolites) will advance our ability to diagnose, treat, and perhaps prevent cancer and other diseases that have eluded scientists for generations. Colorectal tumors are the second leading cause of cancer mortality in the USA, and the incidence is rising. Many patients present late, after the onset of symptoms, when the tumor has spread from the primary site. Once metastases have occurred, the prognosis is significantly worse. Understanding alterations in metabolic profiles that occur with tumor onset and progression could lead to better diagnostic tests as well as uncover new approaches to treat or even prevent colorectal cancer (CRC). In this review, we explore the various analytical technologies that have been applied in CRC metabolomics research and summarize all metabolites measured in CRC and integrate them into metabolic pathways. Early studies with nuclear magnetic resonance and gas-chromatographic mass spectrometry suggest that tumor cells are characterized by aerobic glycolysis, increased purine metabolism for DNA synthesis, and protein synthesis. Liquid chromatography, capillary electrophoresis, and ion mobility, each coupled with mass spectrometry, promise to advance the field and provide new insight into metabolic pathways used by cancer cells. Studies with improved technology are needed to identify better biomarkers and targets for treatment or prevention of CRC.

Use of solid-phase microextraction coupled to gas chromatography–mass spectrometry for determination of urinary volatile organic compounds in autistic children compared with healthy controls

Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders which have a severe life-long effect on behavior and social functioning, and which are associated with metabolic abnormalities. Their diagnosis is on the basis of behavioral and developmental signs usually detected before three years of age, and there is no reliable biological marker. The objective of this study was to establish the volatile urinary metabolomic profiles of 24 autistic children and 21 healthy children (control group) to investigate volatile organic compounds (VOCs) as potential biomarkers for ASDs. Solid-phase microextraction (SPME) using DVB/CAR/PDMS sorbent coupled with gas chromatography–mass spectrometry was used to obtain the metabolomic information patterns. Urine samples were analyzed under both acid and alkaline pH, to profile a range of urinary components with different physicochemical properties. Multivariate statistics techniques were applied to bioanalytical data to visualize clusters of cases and to detect the VOCs able to differentiate autistic patients from healthy children. In particular, orthogonal projections to latent structures discriminant analysis (OPLS-DA) achieved very good separation between autistic and control groups under both acidic and alkaline pH, identifying discriminating metabolites. Among these, 3-methyl-cyclopentanone, 3-methyl-butanal, 2-methyl-butanal, and hexane under acid conditions, and 2-methyl-pyrazine, 2,3-dimethyl-pyrazine, and isoxazolo under alkaline pH had statistically higher levels in urine samples from autistic children than from the control group. Further investigation with a higher number of patients should be performed to outline the metabolic origins of these variables, define a possible association with ASDs, and verify the usefulness of these variables for early-stage diagnosis.

Identification of volatiles from oxidised phosphatidylcholine molecular species using headspace solid-phase microextraction (HS-SPME) and gas chromatography–mass spectrometry (GC-MS)

Headspace solid-phase microextraction (HS-SPME) followed by gas chromatography–mass spectrometry analysis (GC-MS) was used to investigate the volatile compounds from oxidised phosphatidylcholine molecular species. 1-stearoyl-2-oleoyl-sn-glycero-3-phosphocholine (SOPC) and 1-stearoyl-2-linoleoyl-sn-glycero-3-phosphocholine (SLPC) were chosen as models. The influence of several parameters on the efficiency of volatile oxidised compounds (VOCs) microextraction, such as type of fibre, extraction duration and temperature were studied. The best results were obtained with a polydimethylsiloxane/divinylbenzene (PDMS/DVB) fibre used at 50 °C during 25 min. The effect of oxidation temperature on the yield of VOCs from SOPC and SLPC was investigated. Oxidative kinetics of SOPC and SLPC were investigated by measuring both the production of VOCs and the degradation of starting materials. More than 30 VOCs were detected by means of the reference mass spectra of the National Institute of Standards and Technology mass spectral library, and most of them were further confirmed by comparing their mass spectra and retention time with those obtained from authentic reference compounds under the same analytical conditions. Moreover, the origins of VOCs from oxidised PLs were studied by comparing those obtained from their corresponding triacylglycerides under the same experimental conditions. The main VOCs identified from oxidised SOPC were (E)-2-decenal, nonanal and octanal and from oxidised SLPC were (E)-2-heptenal, (E)-2-octenal and (E, E)-2,4-decadienal. The proposed method was applied to a real food sample, soy lecithin.

Metabonomic classification and detection of small molecule biomarkers of malignant pleural effusions

To date, most research has been focused on the benign molecules in pleural effusions, and diagnosis of malignant ones still remains challenging. In the present study, targeting the small molecules as potential biomarkers to predict the malignancy of the effusions, the metabolic profiles of 81 clinical pleural effusions (41 malignant effusions from lung cancer and 40 benign ones) were investigated through a NMR-based metabonomic approach. In ¹H NMR analysis, a total of ten small molecules in the effusions were simultaneously determined. Significantly higher mean values of valine, lactate, and alanine and markedly lower signal intensities of acetoacetate, trimethylamine-N-oxide, and α- and β-glucose were observed in malignant pleural effusions compared with those in benign ones. DFA modeling of NMR spectra subjected to a validation allowed the malignant effusions to be discriminated from benign ones in both training and validation groups. Currently, the conventional clinical analyses on chemical constituents in effusions could not provide a reliable prediction of malignancy of the effusions; the present results revealed that the small molecules might serve as useful biomarkers for diagnosis of the effusions, and the present NMR-based metabonomic approach provided a valuable potential to rapidly and sensitively predict the malignancy of the pleural effusions.

Classification of olive leaves and pulps according to their cultivar by using protein profiles established by capillary gel electrophoresis

A method to classify olive leaves and pulps according to their cultivar using protein profiles obtained by capillary gel electrophoresis (CGE) has been developed. For this purpose, proteins were extracted using an enzyme-assisted method, which provided higher protein recoveries than other previously described methods. Ten and nine common peaks, for leaf and pulp samples, respectively, were identified in the 12 cultivars studied in this work. In addition, and using linear discriminant analysis of the CGE data, olive leaf and pulp samples belonging to 12 cultivars from different Spanish regions were correctly classified with an excellent resolution among all the categories, which demonstrated that protein profiles were characteristic of each cultivar.

High-throughput,multiparameter analysis of single cells

Heterogeneity of cell populations in various biological systems has been widely recognized, and the highly heterogeneous nature of cancer cells has been emerging with clinical relevance. Single-cell analysis using a combination of high-throughput and multiparameter approaches is capable of reflecting cell-to-cell variability, and at the same time of unraveling the complexity and interdependence of cellular processes in the individual cells of a heterogeneous population. In this review, analytical methods and microfluidic tools commonly used for high-throughput, multiparameter single-cell analysis of DNA, RNA, and proteins are discussed. Applications and limitations of currently available technologies for cancer research and diagnostics are reviewed in the light of the ultimate goal to establish clinically applicable assays.

Electrochemical immunosensor for prostate-specific antigen using a glassy carbon electrode modified with a nanocomposite containing gold nanoparticles supported with starch-functionalized multi-walled carbon nanotubes

We report on a simple, rapid, and efficient method for the extraction of volatile organic compounds (VOCs; including methanol, tetrahydrofuran, 2-hexanone and benzene) from air and solid samples. The system is based on the use of a laboratory-made syringe as the extractor. The needle of the syringe is placed in a chamber cooled by liquid nitrogen. The tip of the needle is placed in the headspace of a vial containing the sample. The headspace components then are circulated with a pump to pass the needle, and this results in freeze-trapping of the VOCs on the inner surface of the needle. The circulation of the headspace components is continued for 15 min, and the syringe is then removed and placed in a GC injector. The effects of volume of the sample vial, headspace flow rate, temperature and time of extraction and desorption were optimized. The overall time for sampling and analysis is <30 min. The method displays an extraction efficiency of >80%) and a good sample transfer efficiency into the GC column due to the absence of a sorbent inside the needle. No carry-over was observed after 30?s desorption at 260?°C. An external standard method was used for quantitative analysis. The relative standard deviation values are below 10% and the limits of detection range from 1.3 to 4.6?ng?g^?1.

Change of fucosylated IgG2 Fc-glycoforms in pancreatitis and pancreatic adenocarcinoma: a promising disease-classification model

The fixed constant (Fc) region of IgG is subject to changes in glycosylation state in response to diseases. On the basis of sera from 75 healthy controls, 75 pancreatitis (PT) patients, and 75 pancreatic adenocarcinoma (PAC) patients, we analyzed six fucosylated glycoforms of IgG₂ (G0F, G1F, G2F, G0FN, G1FN, and G2FN), by matrix-assisted laser desorption/ionization–Fourier-transform ion cyclotron resonance mass spectrometry (MALDI–FTICR MS), to evaluate their use as biomarkers for pancreatic diseases. Compared with healthy controls, significant increases in agalactosylated glycoforms and decreases in galactosylated glycoforms were observed for PT and PAC patients. Logistic regression analysis suggested that truncation of the sugar chain was prone to occur in PT and, especially, PAC patients. After participants were stratified by sex and age, receiver operating characteristic curve analysis revealed good overall sensitivity and specificity for discrimination of PAC and PT patients from healthy controls. A combination of G0F and galactosylation also had acceptable power for differentiating PAC patients from PT patients.

Single-walled carbon nanohorns immobilized on a microporous hollow polypropylene fiber as a sorbent for the extraction of volatile organic compounds from water samples

We have evaluated the behavior of single-walled carbon nanohorns as a sorbent for headspace and direct immersion (micro)solid phase extraction using volatile organic compounds (VOCs) as model analytes. The conical carbon nanohorns were first oxidized in order to increase their solubility in water and organic solvents. A microporous hollow polypropylene fiber served as a mechanical support that provides a high surface area for nanoparticle retention. The extraction unit was directly placed in the liquid sample or the headspace of an aqueous standard or a water sample to extract and preconcentrate the VOCs. The variables affecting extraction have been optimized. The VOCs were then identified and quantified by GC/MS. We conclude that direct immersion of the fiber is the most adequate method for the extraction of VOCs from both liquid samples and headspace. Detection limits range from 3.5 to 4.3 ng L^?1 (excepted for toluene with 25 ng L^?1), and the precision (expressed as relative standard deviation) is between 3.9 and 9.6 %. The method was applied to the determination of toluene, ethylbenzene, various xylene isomers and styrene in bottled, river and tap waters, and the respective average recoveries of spiked samples are 95.6, 98.2 and 86.0 %.

Mass spectrometric analysis reveals O-methylation of pyruvate kinase from pancreatic cancer cells

Pyruvate kinase (PK) is an important glycolytic enzyme that catalyzes the dephosphorylation of phosphoenolpyruvate to pyruvate. Human PK isozyme M2 (PKM2), a splice variant of M1, is overexpressed in many cancer cells, and PKM2 has been investigated as a potential tumor marker for diagnostic assays and as a target for cancer therapy. To facilitate identification and characterization of PK, we studied the enzyme from pancreatic cancer cells and normal pancreatic duct cells by electrophoresis and mass spectrometry, and identified multiple O-methylated residues from PK. These findings advance our knowledge of the biochemical properties of PK and will be important in understanding its biological function in cells.

Immunoassay for SKOV-3 human ovarian carcinoma cells using a graphene oxide-modified electrode

We describe a highly sensitive electrochemical immunoassay for the tumor maker HER2 on the surface of SKOV-3 human ovarian cancer cells. Following the binding of the cancer cells, ssDNA-labeled anti-HER2 antibody (ssDNA-Ab; the detection antibody) was added to conjugate unbound antigen on the target cells. Following hybridization of ssDNA with its complementary DNA, daunorubicin was injected in order to intercalate into the duplex. This enables electron transfer between daunorubicin and electrode to take place. The GO film strongly amplifies the redox signal of daunorubicin. This new assay has a detection limit of 5.2 cells per mL and in our opinion holds great promise for clinical screening of cancer biomarkers and point-of-care diagnostics.

Study of urinary steroid hormone disorders: difference between hepatocellular carcinoma in early stage and cirrhosis

Hepatocellular carcinoma (HCC) is the fifth most common cancer in the world. Discovery of novel biomarkers for early HCC from other liver diseases such as cirrhosis is of great clinical benefit. In this study, a novel steroid hormone metabolomic method based on liquid chromatography–mass spectrometry combined with logistic regression analysis was applied to study the steroid hormone disorders and to screen potential urinary steroid hormone biomarkers of early HCC. Thirty-six urinary steroid hormones were detected and quantified in healthy controls, cirrhotic patients, and early HCC patients. Heat map analysis and multivariate statistical analysis suggested severe disorders of steroid hormone network and holistically decreased urinary steroid hormone pattern in cirrhotic and early HCC patients. Logistic regression analysis reveals that a panel of two urinary steroid hormones (epitestosterone and allotetrahydrocortisol) displayed excellent diagnostic capability for distinguishing early HCC from cirrhosis with area under the curve (AUC)?=?0.938 of receiver operating characteristic (ROC) analysis. These results help to overcome the disadvantage of lower sensitivity and specificity of alpha-fetoprotein for distinguishing early HCC from cirrhosis. Our work shows that steroid hormone metabolomics is a promising biomarker tool for biomarker study of early HCC.

Oral cancer diagnostics based on infrared spectral markers and wax physisorption kinetics

Infrared microspectroscopy is an emerging approach for disease analysis owing to its capability for in situ chemical characterization of pathological processes. Synchrotron-based infrared microspectroscopy (SR-IMS) provides ultra-high spatial resolution for profiling biochemical events associated with disease progression. Spectral alterations were observed in cultured oral cells derived from healthy, precancerous, primary, and metastatic cancers. An innovative wax-physisorption-based kinetic FTIR imaging method for the detection of oral precancer and cancer was demonstrated successfully. The approach is based on determining the residual amount of paraffin wax (C₂₅H₅₂) or beeswax (C₄₆H₉₂O₂) on a sample surface after xylene washing. This amount is used as a signpost of the degree of physisorption that altered during malignant transformation. The results of linear discriminant analysis (LDA) of oral cell lines indicated that the methylene (CH₂) and methyl group (CH₃) stretching vibrations in the range of 3,000–2,800 cm^?1 have the highest accuracy rate (89.6 %) to discriminate the healthy keratinocytes (NHOK) from cancer cells. The results of wax-physisorption-based FTIR imaging showed a stronger physisorption with beeswax in oral precancerous and cancer cells as compared with that of NHOK, which showed a strong capability with paraffin wax. The infrared kinetic study of oral cavity tissue showed a consistency in the wax physisorption of the cell lines. On the basis of our findings, these results show the potential use of wax-physisorption-based kinetic FTIR imaging for the early screening of oral cancer lesions and the chemical changes during oral carcinogenesis.

Spectroscopic analysis of bacterial biological warfare simulants and the effects of environmental conditioning on a bacterial spectrum

The ability to distinguish bacteria from mixed samples is of great interest, especially in the medical and defence arenas. This paper reports a step towards the aim of differentiating pathogenic endospores in situ, to aid any required response for hazard management using infrared spectroscopy combined with multivariate analysis. We describe a proof-of-principle study aimed at discriminating biological warfare simulants from common environmental bacteria. We also report an evaluation of multiple pre-processing techniques and subsequent differences in cross-validation of two pattern recognition models (Support Vector Machines and Principal Component–Linear Discriminant Analysis) for a six-class classification (bacterial classification). These classifications were possible with an average sensitivity of 88.0 and 86.9?%, and an average specificity of 97.6 and 97.5?% for the SVM and the PC-LDA models, respectively. Most spectroscopic models are built upon spectra from bacteria that have been specifically prepared for analysis by a particular method; this paper will comment upon the differences in the bacterial spectrum that occur between specific preparations when the bacteria have spent 30?days in the simulated weather conditions of a hot dry climate.

Rapid Screening for Potential Epitopes Reactive with a Polycolonal Antibody by Solution-Phase H/D Exchange Monitored by FT-ICR Mass Spectrometry

The potential epitopes of a recombinant food allergen protein, cashew Ana o 2, reactive to polyclonal antibodies, were mapped by solution-phase amide backbone H/D exchange (HDX) coupled with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Ana o 2 polyclonal antibodies were purified in the serum from a goat immunized with cashew nut extract. Antibodies were incubated with recombinant Ana o 2 (rAna o 2) to form antigen:polyclonal antibody (Ag:pAb) complexes. Complexed and uncomplexed (free) rAna o 2 were then subjected to HDX-MS analysis. Four regions protected from H/D exchange upon pAb binding are identified as potential epitopes and mapped onto a homologous model.

Spectral signatures for the classification of microbial species using Raman spectra

In general, classification-based methods based on confocal Raman microscopy are focused on targeted studies under which the spectral libraries are collected under controlled instrument parameters, which facilitate analyses via standard multivariate data analysis methods and cross-validation. We develop and compare approaches to transform spectra collected at different spectral ranges and varying levels of resolution into a single lower-dimension spectral signature library. This will result in a more robust analysis method able to accommodate spectra accumulated at different times and conditions. We demonstrate these approaches on a relevant test case; the identification of microbial species from a natural environment. The training data were based on samples prepared for three unique species collected at two time points and the test data consisted of blinded unknowns prepared and analyzed at a later date with different instrument parameters. The results indicate that using reduced dimension representations of the spectral signatures improves classification accuracy over basic alignment protocols. In particular, utilizing the microbial species partial least squares discriminant analysis classifier on the blinded samples based on alignment achieved ~78 % accuracy, while both binning and peak selection approaches yielded 100 % accuracy.

Plasma-Assisted Reaction Chemical Ionization for Elemental Mass Spectrometry of Organohalogens

We present plasma-assisted reaction chemical ionization (PARCI) for elemental analysis of halogens in organic compounds. Organohalogens are broken down to simple halogen-containing molecules (e.g., HBr) in a helium microwave-induced plasma followed by negative mode chemical ionization (CI) in the afterglow region. The reagent ions for CI originate from penning ionization of gases (e.g., N₂) introduced into the afterglow region. The performance of PARCI-mass spectrometry (MS) is evaluated using flow injection analyses of organobromines, demonstrating 5–8 times better sensitivities compared with inductively coupled plasma MS. We show that compound-dependent sensitivities in PARCI-MS mainly arise from sample introduction biases.

Phase-resolved real-time breath analysis during exercise by means of smart processing of PTR-MS data

Separation of inspiratory, mixed expired and alveolar air is indispensable for reliable analysis of VOC breath biomarkers. Time resolution of direct mass spectrometers often is not sufficient to reliably resolve the phases of a breathing cycle. To realise fast on-line breath monitoring by means of direct MS utilising low-fragmentation soft ionisation, a data processing algorithm was developed to identify inspiratory and alveolar phases from MS data without any additional equipment. To test the algorithm selected breath biomarkers (acetone, isoprene, acetaldehyde and hexanal) were determined by means of quadrupole proton transfer reaction mass spectrometry (PTR-MS) in seven healthy volunteers during exercise on a stationary bicycle. The results were compared to an off-line reference method consisting of controlled alveolar breath sampling in Tedlar® bags, preconcentration by solid-phase micro extraction (SPME), separation and identification by GC-MS. Based on the data processing method, quantitative attribution of biomarkers to inspiratory, alveolar and mixed expiratory phases was possible at any time during the experiment, even under respiratory rates up to 60/min. Alveolar concentrations of the breath markers, measured by PTR-MS ranged from 130 to 2,600 ppb (acetone), 10 to 540 ppb (isoprene), 2 to 31 ppb (acetaldehyde), whereas the concentrations of hexanal were always below the limit of detection (LOD) of 3 ppb. There was good correlation between on-line PTR-MS and SPME-GC-MS measurements during phases with stable physiological parameters but results diverged during rapid changes of heart rate and minute ventilation. This clearly demonstrates the benefits of breath-resolved MS for fast on-line monitoring of exhaled VOCs.

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Figure Experimental setup showing bicycle ergometer and analytical pathways: Right side PTR-MS: identification of respiratory phases by means of the new algorithm. Left side: confirmation of PTR-MS data for exhaled isoprene by means of GC-MS analysis

     

Soft Ionization of Saturated Hydrocarbons, Alcohols and Nonpolar Compounds by Negative-Ion Direct Analysis in Real-Time Mass Spectrometry

Large polarizable n-alkanes (approximately C18 and larger), alcohols, and other nonpolar compounds can be detected as negative ions when sample solutions are injected directly into the sampling orifice of the atmospheric pressure interface of the time-of-flight mass spectrometer with the direct analysis in real time (DART) ion source operating in negative-ion mode. The mass spectra are dominated by peaks corresponding to [M + O₂]￣^?. No fragmentation is observed, making this a very soft ionization technique for samples that are otherwise difficult to analyze by DART. Detection limits for cholesterol were determined to be in the low nanogram range.

Capillary liquid chromatographic fingerprint used for discrimination of Zingiber montanum from related species

Fingerprint analysis using capillary liquid chromatography (CLC) has been developed for discrimination of Zingiber montanum (ZM) from related species, for example Z. americans (ZA) and Z. zerumbet (ZZ). By comparing the fingerprint chromatograms of ZM, ZA, and ZZ we could identify ZM samples and discriminate them from ZA and ZZ by using their marker peaks. We also combined CLC fingerprint with multivariate analysis, including principal-component analysis (PCA) and canonical variate analysis (CVA); all three species were discriminated successfully. This result indicates that CLC fingerprint analysis in combination with PCA and CVA can be used for discrimination of ZM samples from samples of related species.