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Metabolite identification represents a major bottleneck in contemporary metabolomics research and a step where critical errors may occur and pass unnoticed. This is especially the case for studies employing liquid chromatography-mass spectrometry technology, where there is increased concern on the validity of the proposed identities. In the present perspective article, we describe the issue and categorize the errors into two types: identities that show poor biological plausibility and identities that do not comply with chromatographic data and thus to physicochemical properties (usually hydrophobicity/hydrophilicity) of the proposed molecule. We discuss the problem, present characteristic examples, and propose measures to improve the situation.

We report that the cellular uptake of stable isotope-labeled compounds by bacteria can be probed at the single-cell level using infrared spectroscopy, and this monitors the chemical vibrations affected by the incorporation of "heavy" atoms by cells and thus can be used to understand microbial systems. This presents a significant advancement as most studies have focused on evaluating communities of cells due to the poor spatial resolution achieved by classical infrared microspectrometers, and to date, there is no study evaluating the incorporation of labeled compounds by bacteria at single-cell levels using infrared spectroscopy. The development of new technologies and instrumentations that provide information on the metabolic activity of a single bacterium is critical as this will allow for a better understanding of the interactions between microorganisms as well as the function of individual members and their interactions in different microbial communities. Thus, the present study demonstrates the ability of a novel far-field infrared imaging technique, optical photothermal infrared (O-PTIR) spectroscopy, as a tool to monitor the uptake of 13C-glucose and 15N-ammonium chloride by Escherichia coli bacteria at single-cell levels using spectral signatures recorded via single-point and imaging modes. An additional novelty is that imaging was achieved using six vibrational bands in the amide I and II regions, which were analyzed with chemometrics by employing partial least squares-discriminant analysis to predict 13C/12C and 15N/14N simultaneously.

Surface-enhanced Raman scattering (SERS) is a powerful and sensitive technique for the detection of fingerprint sig-nals of molecules and for the investigation of a series of surface chemical reactions. Many studies introduced quantita-tive applications of SERS in various fields and several SERS methods have been implemented for each specific applica- tion, ranging in performance characteristics, analytes used, instruments, and analytical matrices. In general, very few methods have been validated according to international guidelines. As a consequence, the application of SERS in high-ly- regulated environments is still considered risky and the perception of a poorly reproducible and insufficiently robust analytical technique has persistently retarded its routine implementation. Collaborative trials are a type of interlabora-tory study (ILS) frequently performed to ascertain the quality of a single analytical method. The idea of an ILS of quan- tification with SERS arose within the framework of Working Group 1 (WG1) of the COST Action BM1401 Ra-man4Clinics32 in an effort to overcome the problematic perception of quantitative SERS methods. Here we report the first interlaboratory SERS study ever conducted, involving 15 laboratories and 41 researchers. In this study we tried to define a methodology to assess the reproducibility and trueness of a quantitative SERS method, and to compare differ- ent methods. In our opinion, this is a first important step toward a “standardization” process of SERS protocols, not proposed by a single laboratory but by a larger community.

Human norovirus (NoV) remains the most common cause of viral gastroenteritis and the leading cause of viral foodborne outbreaks globally. NoV is highly pathogenic with an estimated median viral infective dose (ID50) ranging from 18 to 1015 genome copies. For NoV detection, the only reliable and sensitive method available for detection and quantification is reverse transcription quantitative polymerase chain reaction (RTqPCR). NoV detection in food is particularly challenging, requiring matrix specific concentration of the virus and removal of inhibitory compounds to detection assays. Hence, the RTqPCR method poses some challenges for rapid in-field or point-of-care diagnostic applications. We propose a new colorimetric NanoZyme aptasensor strategy for rapid (10 min) and ultrasensitive (calculated Limit of Detection (LoD) of 3 viruses per assay equivalent to 30 viruses/mL of sample and experimentally demonstrated LoD of 20 viruses per assay equivalent to 200 viruses/mL) detection of the infective murine norovirus (MNV), a readily cultivable surrogate for NoV. Our approach combines the enzyme-mimic catalytic activity of gold nanoparticles with high target specificity of an MNV aptamer to create sensor probes that produce a blue color in the presence of this norovirus, such that the color intensity provides the virus concentrations. Overall, our strategy offers the most sensitive detection of norovirus or a norovirus surrogate achieved to date using a biosensor approach, enabling for the first time, the detection of MNV virion corresponding to the lower end of the ID50 for NoV. We further demonstrate the robustness of the norovirus NanoZyme aptasensor by testing its performance in the presence of other nontarget microorganisms, human serum and shellfish homogenate, supporting the potential of detecting norovirus in complex matrices. This new assay format can, therefore, be of significant importance as it allows ultrasensitive norovirus detection rapidly within minutes, while also offering the simplicity of use and need for nonspecialized laboratory infrastructure.

A metabolomics profiling approach was conducted to identify diagnostic biomarkers of PD from sebum, a non-invasively available biofluid. In this study, we used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Partial least squares-discriminant analysis (PLS-DA) models based on this sebum metabolome had correct classification rates of 70.4% and 69.7% to distinguish between drug naïve PD and medicated PD from control, respectively. Variable importance in projection (VIP) scores indicate compounds with significance belonged to sphingolipid, triacylglycerol and fatty acid/ester lipid classes. Pathway enrichment analysis showed alterations in lipid metabolism and mitochondrial dysfunction viz. the carnitine shuttle, sphingolipid metabolism and arachidonic acid metabolism. This study unveiled novel diagnostic sebum-based biomarkers for PD, and provides insight towards our current understanding of the pathogenesis of PD.

A metabolomics profiling approach was conducted to identify diagnostic biomarkers of PD from sebum, a non-invasively available biofluid. In this study, we used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Partial least squares-discriminant analysis (PLS-DA) models based on this sebum metabolome had correct classification rates of 70.4% and 69.7% to distinguish between drug naïve PD and medicated PD from control, respectively. Variable importance in projection (VIP) scores indicate compounds with significance belonged to sphingolipid, triacylglycerol and fatty acid/ester lipid classes. Pathway enrichment analysis showed alterations in lipid metabolism and mitochondrial dysfunction viz. the carnitine shuttle, sphingolipid metabolism and arachidonic acid metabolism. This study unveiled novel diagnostic sebum-based biomarkers for PD, and provides insight towards our current understanding of the pathogenesis of PD.

A colorimetric assay for the detection of DNA fragments associated with the oil palm pathogen Ganoderma boninense is reported, which is based on the aggregation of DNA-nanoparticle conjugated in the presence of complementary DNA from the pathogen. Here, various designs of DNA-nanoparticle conjugates were evaluated, and it was found that the best design gave a visually observable colour change with as little as 2 pmol of double-stranded DNA analyte even in the presence of a large excess of a mixture of non-complementary DNA. The assay was also able to differentiate analyte sequences with three or more single nucleotide mismatches. Overall, this label-free system is rapid, sensitive, selective, simple in design and easy to carry out. It does not require specialist equipment or specialist training for the interpretation of the results and therefore has the potential to be deployed of agricultural diagnostics in the field.

There is no doubt that the contribution of microbially mediated bioprocesses toward maintenance of life on earth is vital. However, understanding these microbes in situ is currently a bottleneck, as most methods require culturing these microorganisms to suitable biomass levels so that their phenotype can be measured. The development of new culture-independent strategies such as stable isotope probing (SIP) coupled with molecular biology has been a breakthrough toward linking gene to function, while circumventing in vitro culturing. In this study, for the first time we have combined Raman spectroscopy and Fourier transform infrared (FT-IR) spectroscopy, as metabolic fingerprinting approaches, with SIP to demonstrate the quantitative labeling and differentiation of Escherichia coli cells. E. coli cells were grown in minimal medium with fixed final concentrations of carbon and nitrogen supply, but with different ratios and combinations of (13)C/(12)C glucose and (15)N/(14)N ammonium chloride, as the sole carbon and nitrogen sources, respectively. The cells were collected at stationary phase and examined by Raman and FT-IR spectroscopies. The multivariate analysis investigation of FT-IR and Raman data illustrated unique clustering patterns resulting from specific spectral shifts upon the incorporation of different isotopes, which were directly correlated with the ratio of the isotopically labeled content of the medium. Multivariate analysis results of single-cell Raman spectra followed the same trend, exhibiting a separation between E. coli cells labeled with different isotopes and multiple isotope levels of C and N.

Raman spectroscopy has been of interest as a detection method for liquid chromatographic separations for a significant period of time, due to the structural information it can provide, allowing the identification and distinction of coeluting analytes. Combined with the rapidly advancing field of enhanced Raman techniques, such as surface-enhanced Raman scattering (SERS), the previous low sensitivity of Raman measurements has also been alleviated. At-line LC-SERS analyses, where SERS measurements are taken of fractions collected during or after HPLC separation have been shown to be sensitive and applicable to a wide variety of analytes; however, quantitative, real-time, online LC-SERS analysis at comparable sensitivity to existing methods, applicable to high-throughput experiments, has not been previously demonstrated. Here we show that by introducing silver colloid, followed by an aggregating agent into the postcolumn flow of an HPLC system, we can quantitatively and reproducibly analyze mixtures of purine bases, with limits of detection in the region of 100-500 pmol. The analysis is performed without the use of a flow cell, thereby eliminating previously detrimental memory effects.

Portable rapid detection of pathogenic bacteria such as Bacillus is highly desirable for safety in food manufacture and under the current heightened risk of biological terrorism. Surface-enhanced Raman scattering (SERS) is becoming the preferred analytical technique for bacterial detection, due to its speed of analysis and high sensitivity. However in seeking methods offering the lowest limits of detection, the current research has tended toward highly confocal, microscopy-based analysis, which requires somewhat bulky instrumentation and precisely synthesized SERS substrates. By contrast, in this study we have improved SERS for bacterial analyses using silver colloidal substrates, which are easily and cheaply synthesized in bulk, and which we shall demonstrate permit analysis using portable instrumentation. All analyses were conducted in triplicate to assess the reproducibility of this approach, which was excellent. We demonstrate that SERS is able to detect and quantify rapidly the dipicolinate (DPA) biomarker for Bacillus spores at 5 ppb (29.9 nM) levels which are significantly lower than those previously reported for SERS and well below the infective dose of 10(4)B. anthracis cells for inhalation anthrax. Finally we show the potential of multivariate data analysis to improve detection levels in complex DPA extracts from viable spores.

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography-mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson's R = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (R = 0.94).

Colloidal-based surface-enhanced Raman scattering (SERS) is a complex technique, where interaction between multiple parameters, such as colloid type, its concentration, and aggregating agent, is poorly understood. As a result SERS has so far achieved limited reproducibility. Therefore the aim of this study was to improve enhancement and reproducibility in SERS, and to achieve this, we have developed a multiobjective evolutionary algorithm (MOEA) based on Pareto optimality. In this MOEA approach, we tested a combination of five different colloids with six different aggregating agents, and a wide range of concentrations for both were explored; in addition we included in the optimization process three laser excitation wavelengths. For this optimization of experimental conditions for SERS, we chose the β-adrenergic blocker drug propranolol as the target analyte. The objective functions chosen suitable for this multiobjective problem were the ratio between the full width at half-maximum and the half-maximum intensity for enhancement and correlation coefficient for reproducibility. To analyze a full search of all the experimental conditions, 7785 experiments would have to be performed empirically; however, we demonstrated the search for acceptable experimental conditions of SERS can be achieved using only 4% of these possible experiments. The MOEA identified several experimental conditions for each objective which allowed a limit of detection of 2.36 ng/mL (7.97 nM) propranolol, and this is significantly lower (>25 times) than previous SERS studies aimed at detecting this β-blocker.

In clinical analyses, the most appropriate biofluid should be analyzed for optimal assay performance. For biological fluids, the most readily accessible is blood, and metabolomic analyses can be performed either on plasma or serum. To determine the optimal agent for analysis, metabolic profiles of matched human serum and plasma were assessed by gas chromatography/time-of-flight mass spectrometry and ultrahigh-performance liquid chromatography mass spectrometry (in positive and negative electrospray ionization modes). Comparison of the two metabolomes, in terms of reproducibility, discriminative ability and coverage, indicated that they offered similar analytical opportunities. An analysis of the variation between 29 small-cell lung cancer (SCLC) patients revealed that the differences between individuals are markedly similar for the two biofluids. However, significant differences between the levels of some specific metabolites were identified, as were differences in the intersubject variability of some metabolite levels. Glycerophosphocholines, erythritol, creatinine, hexadecanoic acid, and glutamine in plasma, but not in serum, were shown to correlate with life expectancy for SCLC patients, indicating the utility of metabolomic analyses in clinical prognosis and the particular utility of plasma in relation to the clinical management of SCLC.

Protein-based biopharmaceuticals are becoming increasingly widely used as therapeutic agents, and the characterization of these biopharmaceuticals poses a significant analytical challenge. In particular, monitoring posttranslational modifications (PTMs), such as glycosylation, is an important aspect of this characterization because these glycans can strongly affect the stability, immunogenicity, and pharmacokinetics of these biotherapeutic drugs. Raman spectroscopy is a powerful tool, with many emerging applications in the bioprocessing arena. Although the technique has a relatively rich history in protein science, only recently has Raman spectroscopy been investigated for assessing posttranslational modifications, including phosphorylation, acetylation, trimethylation, and ubiquitination. In this investigation, we develop for the first time Raman spectroscopy combined with multivariate data analyses, including principal components analysis and partial least-squares regression, for the determination of the glycosylation status of proteins and quantifying the relative concentrations of the native ribonuclease (RNase) A protein and RNase B glycoprotein within mixtures.

Sudan-1 has been used for coloring food. However, recent alarms worldwide about the carcinogenic and mutagenic properties of azo-compounds have led to concerns over their human consumption. In the U.K. in 2005, over 570 products were found to be contaminated with the azo dye Sudan-1 and this and the health risks associated with this dye resulted in the subsequent international ban of this additive in all foodstuff, at all levels, relating to human consumption. These incidents have also necessitated the need for high throughput low cost reliable approaches for the detection and quantification of food contaminated by such azo compounds. While there are a small number of analytical techniques that can be considered portable, many lack sensitivity. By contrast, we show that employing a portable Raman spectrometer, using surface enhanced Raman scattering (SERS), can provide good sensitivity, such that Sudan-1 can be quantified in a complex food matrix reliably over the range of 10−3 to 10−4 mol L−1. We also de...

Monitoring enzyme kinetics is an important aspect of biochemistry and is essential when studying metabolism. In this study we demonstrate that succinate dehydrogenase activity of mitochondria can be analyzed quantitatively by surface enhanced Raman scattering (SERS). We used the artificial electron acceptor reporter molecule 2,6-dichlorophenolindophenol (DCPIP) that when oxidized is SERS active. On reduction this redox dye changes from blue to colorless and is SERS inactive. This color change allows UV/vis spectrophotometry to be used as a standard reference measurement as well as SERS. SERS analysis incorporated kinetic time course measurements and employed a portable laser Raman spectrometer using an excitation wavelength of 785 nm in conjunction with gold colloids. Good correlation coefficients and quantitative data were observed with the additional advantage that analysis by SERS also provided good fingerprint spectral information from vibrational band frequencies thus allowing the potential of multiplexing enzyme reactions. We believe that this is the first demonstration of monitoring succinate dehydrogenase activity with SERS and that SERS has considerable potential for being applied to the analysis of metabolic processes.

The past decade has seen an increased interest in the application of several physicochemical analytical techniques for the rapid detection and identification of microorganisms. We report the development of UV resonance Raman (UVRR) spectroscopy for the reproducible acquisition of information rich Raman fingerprints from endospore-forming bacteria belonging to the genera Bacillus and Brevibacillus. UVRR was conducted at 244 nm, and spectra were collected in typically 60 s. Cluster analyses of these spectra showed that UVRR spectroscopy could be used to discriminate between these microorganisms to species level, and the clustering pattern from this phenotypic classification was highly congruent with phylogenetic trees constructed from 16S rDNA sequence analysis. Therefore, we conclude that UVRR spectroscopy when coupled with chemometrics constitutes a powerful approach to the characterization and speciation of microorganisms.

Direct injection electrospray ionization mass spectrometry (ESI-MS) without prior analyte separation was investigated for the analysis of whole cell suspensions of bacteria. Thirty-six strains of aerobic endospore-forming bacteria, consisting of six Bacillus species and one Brevibacillus species, were studied. ESI was performed in the positive ion mode on the bacterial suspensions. Several peaks in the range of 250−1500 m/z were observed to contribute to variations in the spectral information among the species. Application of cluster analysis to the spectral data showed that this ESI-MS technique was capable of discriminating strains of the species B. subtilis. This investigation demonstrates the feasibility of measuring liquid samples with minimal sample preparation that can be useful for discrimination at the subspecies level. A change in the cone potential in the electrospray ion source was found to influence the spectral information of representative strains of all of the seven species tested. This ha...

Thirty-six strains of aerobic endospore-forming bacteria confirmed by polyphasic taxonomic methods to belong to Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacillus subtilis (including Bacillus niger and Bacillus globigii), Bacillus sphaericus, and Brevi laterosporus were grown axenically on nutrient agar, and vegetative and sporulated biomasses were analyzed by Curie-point pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier-transform infrared spectroscopy (FT-IR). Chemometric methods based on rule induction and genetic programming were used to determine the physiological state (vegetative cells or spores) correctly, and these methods produced mathematical rules which could be simply interpreted in biochemical terms. For PyMS it was found that m/z 105 was characteristic and is a pyridine ketonium ion (C6H3ON+) obtained from the pyrolysis of dipicolinic acid (pyridine-2,6-dicarboxylic acid; DPA), a substance found in spores but not in vegetative cells; this was confirmed using pyrolysis-gas chromatography/mass spectrometry. In addition, a pyridine ring vibration at 1447-1439 cm-1 from DPA was found to be highly characteristic of spores in FT-IR analysis. Thus, although the original data sets recorded hundreds of spectral variables from whole cells simultaneously, a simple biomarker can be used for the rapid and unequivocal detection of spores of these organisms.

A technique for the analysis of multivariate data by genetic programming (GP) is described, with particular reference to the quantitative analysis of orange juice adulteration data collected by pyrolysis mass spectrometry (PyMS). The dimensionality of the input space was reduced by ranking variables according to product moment correlation or mutual information with the outputs. The GP technique as described gives predictive errors equivalent to, if not better than, more widespread methods such as partial least squares and artificial neural networks but additionally can provide a means for easing the interpretation of the correlation between input and output variables. The described application demonstrates that by using the GP method for analyzing PyMS data the adulteration of orange juice with 10% sucrose solution can be quantified reliably over a 0-20% range with an RMS error in the estimate of ∼1%.

For pyrolysis mass spectrometry (PyMS) to be used for the routine identification of microorganisms, for quantifying determinands in biological and biotechnological systems, and in the production of useful mass spectral libraries, it is paramount that newly acquired spectra be compared to those previously collected. Neural network and other multivariate calibration models have been used to relate mass spectra to the biological features of interest. As commonly observed, however, mass spectral fingerprints showed a lack of long-term reproducibility, due to instrumental drift in the mass spectrometer; when identical materials were analyzed by PyMS at dates from 4 to 20 months apart, neural network models produced at earlier times could not be used to give accurate estimates of determinand concentrations or bacterial identities. Neural networks, however, can be used to correct for pyrolysis mass spectrometer instrumental drift itself, so that neural network or other multivariate calibration models created using previously collected data can be used to give accurate estimates of determinand concentration or the nature of bacteria (or, indeed, other materials) from newly acquired pyrolysis mass spectra. This approach is not limited solely to pyrolysis mass spectrometry but is generally applicable to any analytical tool which is prone to instrumental drift, such as IR, ESR, NMR and other spectroscopies, and gas and liquid chromatography, as well as other types of mass spectrometry.

Binary mixtures of the protein lysozyme with glycogen, of DNA or RNA in glycogen, and the tertiary mixture of cells of the bacteria Bacillus subtilh, Escherichia coli, and Staphylococcusaurecls were subjected to pyrolysis mass spectrometry. To analyze the pyrolysis mass spectra so as to obtain quantitative information representative of the complex components of the mixtures, partial least-squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard back-propagation algorithm, and the nodes used a sigmoidal squashing function. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of determinands in samples on which they had not been trained. Neural networks were found to provide the most accurate predictions. We also report that scaling the individual nodes on the input layer of ANNs significantly decreased the time taken for the ANNs to learn. Removing masses of low intensity, which perhaps mainly contributed noise to the pyrolysis mass spectra, had little effect on the accuracy of the ANN predictions though could dramatically speed up the learning process (by more than 100-fold) and slightly improved the accuracy of PLS calibrations.