Ethanol contamination of cerebrospinal fluid during standardized sampling and its effect on (1)H-NMR metabolomics.
ABSTRACT: Standardization of body fluid sampling, processing and storage procedures is pivotal to ensure data quality in metabolomics studies. Yet, despite strict adherence to standard sampling guidelines, we detected variable levels of ethanol in the (1)H-NMR spectra of human cerebrospinal fluid (CSF) samples (range 9.2?×?10(-3)-10.0 mM). The presence of ethanol in all samples and the wide range of concentrations clearly indicated contamination of the samples of some sort, which affected the (1)H-NMR spectra quality and the interpretation. To determine where in the sampling protocol the ethanol contamination occurs, we performed a CSF sampling protocol simulation with 0.9 % NaCl (saline) instead of CSF and detected ethanol in all simulation samples. Ethanol diffusion through air during sampling and preparation stages appeared the only logical explanation. With a bench study, we showed that ethanol easily diffuses into ex vivo CSF samples via air transmission. Ethanol originated from routinely used skin disinfectants containing ethanol and from laboratory procedures. Ethanol affected the CSF sample matrix at concentrations above ~9.4 mM and obscured a significant part of the (1)H-NMR spectrum. CSF sample preparation for (1)H-NMR-based metabolomics analyses should therefore be carried out in a well-ventilated atmosphere with laminar flow, and use of ethanol should be avoided.
Project description:Phyllanthus niruri is an important medicinal plant. To standardize the extract and guarantee its maximum benefit, processing methods optimization ought to be amenable and beneficial. Herein, three dried P. niruri samples, air (AD), freeze (FD) and oven (OD), extracted with various ethanol to water ratios (0%, 50%, 70%, 80% and 100%) were evaluated for their metabolite changes using proton nuclear magnetic resonance (¹H-NMR)-based metabolomics approach. The amino acids analysis showed that FD P. niruri exhibited higher content of most amino acids compared to the other dried samples. Based on principal component analysis (PCA), the FD P. niruri extracted with 80% ethanol contained higher amounts of hypophyllanthin and phenolic compounds based on the loading plot. The partial least-square (PLS) results showed that the phytochemicals, including hypophyllanthin, catechin, epicatechin, rutin, quercetin and chlorogenic, caffeic, malic and gallic acids were correlated with antioxidant and ?-glucosidase inhibitory activities, which were higher in the FD material extracted with 80% ethanol. This report optimized the effect of drying and ethanol ratios and these findings demonstrate that NMR-based metabolomics was an applicable approach. The FD P. niruri extracted with 80% ethanol can be used as afunctional food ingredient for nutraceutical or in medicinal preparation.
Project description:Metabolic profiling of cerebrospinal fluid (CSF) is a promising technique for studying brain diseases. Measurements should reflect the in vivo situation, so ex vivo metabolism should be avoided.To investigate the effects of temperature (room temperature vs. 4 °C), centrifugation and ethanol, as anti-enzymatic additive during CSF sampling on concentrations of glutamic acid, glutamine and other endogenous amines.CSF samples from 21 individuals were processed using five different protocols. Isotopically-labeled alanine, isoleucine, glutamine, glutamic acid and dopamine were added prior to sampling to trace any degradation. Metabolomics analysis of endogenous amines, isotopically-labeled compounds and degradation products was performed with a validated LC-MS method.Thirty-six endogenous amines were quantified. There were no statistically significant differences between sampling protocols for 31 out of 36 amines. For GABA there was primarily an effect of temperature (higher concentrations at room temperature than at 4 °C) and a small effect of ethanol (lower concentrations if added) due to possible degradation. O-phosphoethanolamine concentrations were also lower when ethanol was added. Degradation of isotopically-labeled compounds (e.g. glutamine to glutamic acid) was minor with no differences between protocols.Most amines can be considered stable during sampling, provided that samples are cooled immediately to 4 °C, centrifuged, and stored at -?80 °C within 2 h. The effect of ethanol addition for more unstable metabolites needs further investigation. This was the first time that labeled compounds were used to monitor ex vivo metabolism during sampling. This is a useful strategy to study the stability of other metabolites of interest.
Project description:The field of metabolomics generally lacks standardized methods for the preparation of samples prior to analysis. This is especially true for metabolomics of reef-building corals, where the handful of studies that were published employ a range of sample preparation protocols. The utilization of metabolomics may prove essential in understanding coral biology in the face of increasing environmental threats, and an optimized method for preparing coral samples for metabolomics analysis would aid this cause. The current study evaluates three important steps during sample processing of stony corals: (i) metabolite extraction, (ii) metabolism preservation, and (iii) subsampling. Results indicate that a modified Bligh and Dyer extraction is more reproducible across multiple coral species compared to methyl tert-butyl ether and methanol extractions, while a methanol extraction is superior for feature detection. Additionally, few differences were detected between spectra from frozen or lyophilized coral samples. Finally, extraction of entire coral nubbins increased feature detection, but decreased throughput and was more susceptible to subsampling error compared to a novel tissue powder subsampling method. Overall, we recommend the use of a modified Bligh and Dyer extraction, lyophilized samples, and the analysis of brushed tissue powder for the preparation of reef-building coral samples for ¹H NMR metabolomics.
Project description:BACKGROUND: Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) 1H, projections of 2D 1H, 1H J-resolved (pJRES), and intact 2D J-resolved (JRES). RESULTS: Here, the effects of the glog transform are compared against two commonly used variance stabilising techniques, autoscaling and Pareto scaling, as well as unscaled data. The four methods are evaluated in terms of the effects on the variance of NMR metabolomics data and on the classification accuracy following multivariate analysis, the latter achieved using principal component analysis followed by linear discriminant analysis. For two of three datasets analysed, classification accuracies were highest following glog transformation: 100% accuracy for discriminating 1D NMR spectra of hypoxic and normoxic invertebrate muscle, and 100% accuracy for discriminating 2D JRES spectra of fish livers sampled from two rivers. For the third dataset, pJRES spectra of urine from two breeds of dog, the glog transform and autoscaling achieved equal highest accuracies. Additionally we extended the glog algorithm to effectively suppress noise, which proved critical for the analysis of 2D JRES spectra. CONCLUSION: We have demonstrated that the glog and extended glog transforms stabilise the technical variance in NMR metabolomics datasets. This significantly improves the discrimination between sample classes and has resulted in higher classification accuracies compared to unscaled, autoscaled or Pareto scaled data. Additionally we have confirmed the broad applicability of the glog approach using three disparate datasets from different biological samples using 1D NMR spectra, 1D projections of 2D JRES spectra, and intact 2D JRES spectra.
Project description:BACKGROUND: Despite wide-spread use of Nuclear Magnetic Resonance (NMR) in metabolomics for the analysis of biological samples there is a lack of graphically driven, publicly available software to process large one and two-dimensional NMR data sets for statistical analysis. RESULTS: Here we present MetaboLab, a MATLAB based software package that facilitates NMR data processing by providing automated algorithms for processing series of spectra in a reproducible fashion. A graphical user interface provides easy access to all steps of data processing via a script builder to generate MATLAB scripts, providing an option to alter code manually. The analysis of two-dimensional spectra (¹H,¹³C-HSQC spectra) is facilitated by the use of a spectral library derived from publicly available databases which can be extended readily. The software allows to display specific metabolites in small regions of interest where signals can be picked. To facilitate the analysis of series of two-dimensional spectra, different spectra can be overlaid and assignments can be transferred between spectra. The software includes mechanisms to account for overlapping signals by highlighting neighboring and ambiguous assignments. CONCLUSIONS: The MetaboLab software is an integrated software package for NMR data processing and analysis, closely linked to the previously developed NMRLab software. It includes tools for batch processing and gives access to a wealth of algorithms available in the MATLAB framework. Algorithms within MetaboLab help to optimize the flow of metabolomics data preparation for statistical analysis. The combination of an intuitive graphical user interface along with advanced data processing algorithms facilitates the use of MetaboLab in a broader metabolomics context.
Project description:High-Resolution Magic-Angle Spinning (HR-MAS) NMR spectroscopy has become an extremely versatile analytical tool to study heterogeneous systems endowed with liquid-like dynamics. Spinning frequencies of several kHz are however required to obtain NMR spectra, devoid of spinning sidebands, with a resolution approaching that of purely isotropic liquid samples. An important limitation of the method is the large centrifugal forces that can damage the structure of the sample. In this communication, we show that optimizing the sample preparation, particularly avoiding air bubbles, and the geometry of the sample chamber of the HR-MAS rotor leads to high-quality low-sideband NMR spectra even at very moderate spinning frequencies, thus allowing the use of well-established solution-state NMR procedures for the characterization of small and highly dynamic molecules in the most fragile samples, such as live cells and intact tissues.
Project description:Neurochemical biomarkers are urgently sought in ALS. Metabolomic analysis of cerebrospinal fluid (CSF) using proton nuclear magnetic resonance ((1)H-NMR) spectroscopy is a highly sensitive method capable of revealing nervous system cellular pathology. The (1)H-NMR CSF metabolomic signature of ALS was sought in a longitudinal cohort. Six-monthly serial collection was performed in ALS patients across a range of clinical sub-types (n = 41) for up to two years, and in healthy controls at a single time-point (n = 14). A multivariate statistical approach, partial least squares discriminant analysis, was used to determine differences between the NMR spectra from patients and controls. Significantly predictive models were found using those patients with at least one year's interval between recruitment and the second sample. Glucose, lactate, citric acid and, unexpectedly, ethanol were the discriminating metabolites elevated in ALS. It is concluded that (1)H-NMR captured the CSF metabolomic signature associated with derangements in cellular energy utilization connected with ALS, and was most prominent in comparisons using patients with longer disease duration. The specific metabolites identified support the concept of a hypercatabolic state, possibly involving mitochondrial dysfunction specifically. Endogenous ethanol in the CSF may be an unrecognized novel marker of neuronal tissue injury in ALS.
Project description:BACKGROUND:Myriad infectious and noninfectious causes of encephalomyelitis (EM) have similar clinical manifestations, presenting serious challenges to diagnosis and treatment. Metabolomics of cerebrospinal fluid (CSF) was explored as a method of differentiating among neurological diseases causing EM using a single CSF sample. METHODOLOGY/PRINCIPAL FINDINGS:1H NMR metabolomics was applied to CSF samples from 27 patients with a laboratory-confirmed disease, including Lyme disease or West Nile Virus meningoencephalitis, multiple sclerosis, rabies, or Histoplasma meningitis, and 25 controls. Cluster analyses distinguished samples by infection status and moderately by pathogen, with shared and differentiating metabolite patterns observed among diseases. CART analysis predicted infection status with 100% sensitivity and 93% specificity. CONCLUSIONS/SIGNIFICANCE:These preliminary results suggest the potential utility of CSF metabolomics as a rapid screening test to enhance diagnostic accuracies and improve patient outcomes.
Project description:Healthcare facilities (HF) represent an at-risk environment for legionellosis transmission occurring after inhalation of contaminated aerosols. In general, the control of water is preferred to that of air because, to date, there are no standardized sampling protocols. Legionella air contamination was investigated in the bathrooms of 11 HF by active sampling (Surface Air System and Coriolis®μ) and passive sampling using settling plates. During the 8-hour sampling, hot tap water was sampled three times. All air samples were evaluated using culture-based methods, whereas liquid samples collected using the Coriolis®μ were also analyzed by real-time PCR. Legionella presence in the air and water was then compared by sequence-based typing (SBT) methods. Air contamination was found in four HF (36.4%) by at least one of the culturable methods. The culturable investigation by Coriolis®μ did not yield Legionella in any enrolled HF. However, molecular investigation using Coriolis®μ resulted in eight HF testing positive for Legionella in the air. Comparison of Legionella air and water contamination indicated that Legionella water concentration could be predictive of its presence in the air. Furthermore, a molecular study of 12 L. pneumophila strains confirmed a match between the Legionella strains from air and water samples by SBT for three out of four HF that tested positive for Legionella by at least one of the culturable methods. Overall, our study shows that Legionella air detection cannot replace water sampling because the absence of microorganisms from the air does not necessarily represent their absence from water; nevertheless, air sampling may provide useful information for risk assessment. The liquid impingement technique appears to have the greatest capacity for collecting airborne Legionella if combined with molecular investigations.
Project description:Isotopically labeling a metabolite and tracing its metabolic fate has provided invaluable insights about the role of metabolism in human diseases in addition to a variety of other issues. 13C-labeled metabolite tracers or unlabeled 1H-based NMR experiments are currently the most common application of NMR to metabolomics studies. Unfortunately, the coverage of the metabolome has been consequently limited to the most abundant carbon-containing metabolites. To expand the coverage of the metabolome and enhance the impact of metabolomics studies, we present a protocol for 15N-labeled metabolite tracer experiments that may also be combined with routine 13C tracer experiments to simultaneously detect both 15N- and 13C-labeled metabolites in metabolic samples. A database consisting of 2D 1H-15N HSQC natural-abundance spectra of 50 nitrogen-containing metabolites are also presented to facilitate the assignment of 15N-labeled metabolites. The methodology is demonstrated by labeling Escherichia coli and Staphylococcus aureus metabolomes with 15N1-ammonium chloride, 15N4-arginine, and 13C2-acetate. Efficient 15N and 13C metabolite labeling and identification were achieved utilizing standard cell culture and sample preparation protocols.