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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.

We present a highly efficient multichannel microfluidic electrochemical sensor integrated with an electroactive nanocarbon microelectrode for sensitive and selective detection of multiple biomarkers in different biological samples. Our results have shown that ionic liquid-assisted wet spinning followed by tailored growth of metal-organic frameworks and pyrolysis treatment led to structural and molecular engineering of mechanically robust all-carbon microfibers for excellent electrochemical activities. The flexible bottlebrush-like nanocarbon microelectrode features a "stem" of freestanding N, B-codoped graphene fiber and high-density "bristles" of Co, N-codoped carbon nanotube arrays, leading to promoted electrocatalytic mechanism that has been substantiated by density functional theory calculations. The structural characteristics, high catalytic activities, and favorable biocompatibility of the bottlebrush nanocarbon electrodes provide opportunities for multichannel, microfluidic detection of redox-active biomolecules, including hydrogen sulfide (H2S), dopamine (DA), uric acid (UA), and ascorbic acid (AA), and have been applied to on-chip monitoring of H2S and DA released from live cancer cells or neuroblastoma cells and DA, UA, and AA in trace amounts of body fluids such as sweat, finger blood, tears, saliva, and urine, which is of great significance for clinical diagnosis and prognosis in point-of-care testing. This work is supported by the National Natural Science Foundation of China (project no. 21874051) and Program for HUST Academic Frontier Youth Team (2019QYTD11).

Proteins in a human body continuously undergo glycation reactions with reducing sugars, forming early as well as advanced glycation end-products (AGEs) which are highly disease-relevant. Specifically, N-1-(deoxyfructosyl) valine of β-hemoglobin (HbA1c) has been considered as a marker of diabetes, but the exact map of glycated Hb peptides corelated with diabetes in different stages is poorly studied. Here, the pseudotargeted parallel reaction monitoring (PRM) method combined with relative retention time (iRT) endogenous peptides was proposed for exploring the roles of deoxyfructosyl (DF-), carboxymethyl (CM-), and carboxyethyl (CE-) based Hb modifications in clinical prognosis and diagnosis of type 2 diabetes mellitus (T2DM) and its complication. For building the pseudotargeted list, data-dependent acquisition (DDA) combined with multiple enzyme digestion was employed for the comprehensive identification of the three types of modification in vitro Hb and in vivo Hb. The introduction of the endogenous iRT peptides during PRM analysis facilitates being able to obtain accurate quantitative results. When applying this new strategy to quantify the three kinds of glycated Hb peptides in clinical samples, patients with T2DM in different pathophysiological conditions were fully distinguished from the controls, indicating the necessity of adopting multiple glycation types for the improved diagnosis of T2DM. Taken together, the newly developed pseudotargeted PRM method not only expands the horizons of glycated Hb by reliably assessing the actual status of T2DM but also reveals that endogenous iRT might be a viable option for label-free quantitative analysis.

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.

Process analytical technologies (PAT) are used within industry to give real-time measurements of critical quality parameters, ultimately improving the quality by design (QbD) of the final product and reducing manufacturing costs. Spectroscopic and spectrophotometric methods are readily employed within PAT due to their ease of use, compatibility toward a range of sample types, robustness, and multiplexing capabilities. We have developed a UV resonance Raman (UVRR) spectroscopy approach to quantify industrially relevant biotransformations accurately, focusing on nitrile metabolizing enzymes: nitrile hydratase (NHase) and amidase versus nitrilase activity. Sensitive detection of the amide intermediate by UVRR spectroscopy enabled discrimination between the two nitrile-hydrolyzing pathways. Development of a flow-cell apparatus further exemplifies its suitability toward PAT measurements, incorporating in situ analysis within a closed system. Multivariate curve resolution-alternating least-squares (MCR-ALS) was applied to the UVRR spectra, as well as off-line HPLC measurements, to enable absolute quantification of substrate, intermediate, and product. Further application of hard modeling to MCR-ALS deconvolved concentration profiles enabled accurate kinetic determinations, thus removing the requirement for comparative off-line HPLC. Finally, successful quantitative measurements of in vivo activity using whole-cell biotransformations, where two Escherichia coli strains expressing either NHase (transforming benzonitrile to benzamide) or amidase (further conversion of benzamide to benzoic acid), illustrate the power, practicality, and sensitivity of this novel approach of multistep and, with further refinement, we believe, multiple micro-organism biotransformations.

The application of Raman spectroscopy as a detection method coupled with liquid chromatography (LC) has recently attracted considerable interest, although this has currently been limited to isocratic elution. The combination of LC with rapidly advancing Raman techniques, such as surface-enhanced Raman scattering (SERS), allows for rapid separation, identification and quantification, leading to quantitative discrimination of closely eluting analytes. This study has demonstrated the utility of SERS in conjunction with reversed-phase liquid chromatography (RP-LC), for the detection and quantification of the therapeutically relevant drug molecule methotrexate (MTX) and its metabolites 7-hydroxy methotrexate (7-OH MTX) and 2,4-diamino-N(10)-methylpteroic acid (DAMPA) in pure solutions and mixtures, including spikes into human urine from a healthy individual and patients under medication. While the RP-LC analysis developed employed gradient elution, where the chemical constituents of the mobile phase were modified stepwise during analysis, this did not overtly interfere with the SERS signals. In addition, the practicability and clinical utility of this approach has also been demonstrated using authentic patients' urine samples. Here, the identification of MTX, 7-OH MTX and DAMPA are based on their unique SERS spectra, providing limits of detection of 2.36, 1.84, and 3.26 μM respectively. Although these analytes are amenable to LC and LC-MS detection an additional major benefit of the SERS approach is its applicability toward the detection of analytes that do not show UV absorption or are not ionised for mass spectrometry (MS)-based detection. The results of this study clearly demonstrate the potential application of online LC-SERS analysis for real-time high-throughput detection of drugs and their related metabolites in human biofluids.

There has been an increasing demand for rapid and sensitive techniques for the identification and quantification of pharmaceutical compounds in human biofluids during the past few decades, and surface-enhanced Raman scattering (SERS) is one of a number of physicochemical techniques with the potential to meet these demands. In this study we have developed a SERS-based analytical approach for the assessment of human biofluids in combination with chemometrics. This novel approach has enabled the detection and quantification of the β-blocker propranolol spiked into human serum, plasma, and urine at physiologically relevant concentrations. A range of multivariate statistical analysis techniques, including principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least-squares regression (PLSR) were employed to investigate the relationship between the full SERS spectral data and the level of propranolol. The SERS spectra when combined with PCA and PC-DFA demonstrated clear differentiation of neat biofluids and biofluids spiked with varying concentrations of propranolol ranging from 0 to 120 μM, and clear trends in ordination scores space could be correlated with the level of propranolol. Since PCA and PC-DFA are categorical classifiers, PLSR modeling was subsequently used to provide accurate propranolol quantification within all biofluids with high prediction accuracy (expressed as root-mean-square error of predictions) of 0.58, 9.68, and 1.69 for serum, plasma, and urine respectively, and these models also had excellent linearity for the training and test sets between 0 and 120 μM. The limit of detection as calculated from the area under the naphthalene ring vibration from propranolol was 133.1 ng/mL (0.45 μM), 156.8 ng/mL (0.53 μM), and 168.6 ng/mL (0.57 μM) for serum, plasma, and urine, respectively. This result shows a consistent signal irrespective of biofluid, and all are well within the expected physiological level of this drug during therapy. The results of this study demonstrate the potential of SERS application as a diagnostic screening method, following further validation and optimization to improve detection of pharmaceutical compounds and quantification in human biofluids, which may open up new exciting opportunities for future use in various biomedical and forensic applications.

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.

The majority of works in metabolomics employ approaches based on principal components analysis (PCA) and partial least-squares, primarily to determine whether samples fall within large groups. However, analytical chemists rarely tackle the problem of individual fingerprinting, and in order to do this effectively, it is necessary to study a large number of small groups rather than a small number of large groups and different approaches are required, as described in this paper. Furthermore, many metabolomic studies on mammals and humans involve analyzing compounds (or peaks) that are present in only a certain portion of samples, and conventional approaches of PCA do not cope well with sparse matrices where there may be many 0s. There is, however, a large number of qualitative similarity measures available for this purpose that can be exploited via principal coordinates analysis (PCO). It can be shown that PCA scores are a specific case of PCO scores, using a quantitative similarity measure. A large-scale study of human sweat consisting of nearly 1000 gas chromatography/mass spectrometry analyses from the sweat of an isolated population of 200 individuals in Carinthia (Southern Austria) sampled once per fortnight over 10 weeks was employed in this study and grouped into families. The first step was to produce a peak table requiring peak detection, alignment, and integration. Peaks were reduced from 5080 to 373 that occurred in at least 1 individual over 4 out of 5 fortnights. Both qualitative (presence/absence) and quantitative (equivalent to PCA) similarity measures can be computed. PCO and the Kolomorogov-Smirnoff (KS) rank test are applied to these similarity matrices. It is shown that for this data set there is a reproducible individual fingerprint, which is best represented using the qualitative similarity measure as assessed both by the Hotelling t2 statistic as applied to PCO scores and the probabilities associated with the KS rank test.

Microbiological safety is one of the cornerstones of quality control in the food industry. Identification and quantification of spoilage bacteria in pasteurized milk and meat in the food industry currently relies on accurate and sensitive yet time-consuming techniques which give retrospective values for microbial contamination. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), a proven technique in the field of protein and peptide identification and quantification, may be a valuable alternative approach for the rapid assessment of microbial spoilage. In this work we therefore developed MALDI-TOF-MS as a novel analytical approach for the assessment of food that when combined with chemometrics allows for the detection and quantification of milk and pork meat spoilage bacteria. To develop this approach, natural spoilage of pasteurized milk and raw pork meat samples incubated at 15 °C and at room temperature, respectively, was conducted. Samples were collected for MALDI-TOF-MS analysis (which took 4 min per sample) at regular time intervals throughout the spoilage process, with concurrent calculation and documentation of reference total viable counts using traditional microbiological methods (these took 2 days). Multivariate statistical techniques such as principal component discriminant function analysis, canonical correlation analysis, partial least-squares (PLS) regression, and kernel PLS (KPLS) were used to analyze the data. The results from MALDI-TOF-MS combined with PLS or KPLS gave excellent bacterial quantification results for both milk and meat spoilage, and typical root mean squared errors for prediction in test spectra were between 0.53 and 0.79 log unit. Overall these novel findings strongly indicate that MALDI-TOF-MS when combined with chemometric approaches would be a useful adjunct for routine use in the milk and meat industry as a fast and accurate viable bacterial detection and quantification method.

Staphylococcus aureus is one of the main pathogenic microorganisms found in milk and dairy products and has been involved in bacterial foodborne outbreaks in the past. Current enumeration techniques for bacteria are very time-consuming, typically taking 24 h or longer, and bacterial antagonism in the form of lactic acid bacteria (LAB) may inhibit the growth of S. aureus . Therefore, the aim of this investigation was to establish the accuracy and sensitivity of rapid nondestructive metabolic fingerprinting techniques, such as Fourier transform infrared (FT-IR) spectroscopy and Raman spectroscopy (RS), in combination with multivariate analysis techniques, for the detection and enumeration of S. aureus in milk, as well as to study the growth interaction between S. aureus and Lactococcus lactis ssp. cremoris , a common LAB. The two bacterial species were investigated both in a pure monoculture and in a combined inoculated coculture after inoculation into ultraheated milk during the first 24 h of growth at 37 °C. Plating techniques were used to obtain primary reference data for viable bacteria counts. Principal component discriminant function analysis, canonical correlation analysis, partial least-squares (PLS), and kernel PLS (KPLS) multivariate statistical techniques were employed to analyze the data. FT-IR provided very reasonable quantification results both with PLS and KPLS, the latter providing marginally better predictions, with correlation coefficients in the test set (Q(2)) and training set (R(2)) varying from 0.64 to 0.76 and from 0.78 to 0.88 for different bacterial sample combinations. RS results were less encouraging with high degrees of error and poor correlation to viable bacterial counts. S. aureus growth was not inhibited by the presence of the LAB, but metabolic fingerprinting of the coculture indicated that the phenotype of this dual bacterial culture was closer to that of pure LAB cultures. In conclusion, FT-IR spectroscopy in combination with the above multivariate techniques appears to be a promising discrimination and enumeration analytical technique for the two bacterial species. In addition, it has been demonstrated that the L. cremoris metabolic effect in milk dominates that of S. aureus even though there was no growth antagonism observed.

International audience; A metabolomics approach combining 1H NMR and gas chromatography−electrospray ionization time-of-flight mass spectrometry (GC−EI-TOFMS) profiling was employed to characterize melon (Cucumis melo L.) fruit. In a first step, quantitative 1H NMR of polar extracts and principal component analyses (PCA) of the corresponding data highlighted the major metabolites in fruit flesh, including sugars, organic acids, and amino acids. In a second step, the spatial localization of metabolites was investigated using both analytical techniques. Direct 1H NMR profiling of juice or GC−EI-TOFMS profiling of tissue extracts collected from different locations in the fruit flesh provided information on advantages and drawbacks of each technique for the analysis of a sugar-rich matrix such as fruit. 1H NMR and GC−EI-TOFMS data sets were compared using independently performed PCA and multiblock hierarchical PCA (HPCA), respectively. In addition a correlation-based multiblock HPCA was used for direct comparison of both analytical data sets. These data analyses revealed several gradients of metabolites in fruit flesh which can be related with differences in metabolism and indicated the suitability of multiblock HPCA for correlation of data from two (or potentially more) metabolomics platforms.