Quantitative analysis of bilobalide and ginkgolides from Ginkgo biloba leaves and Ginkgo products using (1)H-NMR

1H-NMR spectrometry was applied to the quantitative analysis of the bilobalide, ginkgolides A, B, and C in Ginkgo biloba leaves and six kinds of commercial Ginkgo products without any chromatographic purification. The experiment was performed by the analysis of each singlet H-12, which were well separated in the range of delta 6.0-7.0 in the (1)H-NMR spectrum. However, the H-12 protons of bilobalide and ginkgolides may have overlapped with H-6 or H-8 protons of the Ginkgo flavonoids. Therefore, the optimum (1)H-NMR solvent for the analysis of the compound was selected through the evaluation of solvent effects on the resolution of these signals from the compounds. Acetone-d(6)-benzene-d(6) (50 : 50) was found to be the best one among the solvents evaluated. The quantity of the compounds was calculated by the relative ratio of the intensity of each compound to the known amount of internal standard (25 microgram), phloroglucinol. This method allows rapid and simple quantitation of underivatized bilobalide and ginkgolides in 5 min without any pre-purification steps.     

<Superscript>1</Superscript>H-nuclear magnetic resonance spectroscopy-based metabolic assessment in a rat model of obesity induced by a high-fat diet

MicroRNAs (miRNAs) are a class of small noncoding RNAs ∼22 nt in length that regulate gene expression and play fundamental roles in multiple biological processes, including cell differentiation, proliferation and apoptosis as well as disease processes. The study of miRNA has thus become a rapidly emerging field in life science. The detection of miRNA expression is a very important first step in miRNA exploration. Several methodologies, including cloning, northern blotting, real-time RT-PCR, microRNA arrays and ISH (in situ hybridization), have been developed and applied successfully in miRNA profiling. This review discusses the main existing microRNA detection technologies, while emphasizing microRNA arrays.     

Simple preparative gas chromatographic method for isolation of menthol and menthone from peppermint oil, with quantitative GC-MS and (1) H NMR assay

The quantitative performance of a simple home‐built preparative gas chromatography (prep‐GC) arrangement was tested, incorporating a micro‐fluidic Deans switch, with collection of the target compound in a deactivated uncoated capillary tube. Repeat injections of a standard solution and peppermint sample were made into the prep‐GC instrument. Individual compounds were eluted from the trapping capillary, and made up to constant volume. Chloronaphthalene internal standard was added in some cases. Recovered samples were quantitatively assayed by using GC‐MS. Calibration linearity of GC‐MS for menthol standard area response against number of injections (2–20 repeat injections) was excellent, giving R² of 0.996. For peppermint, menthol correlation over 2–20 repeated injections was 0.998 for menthol area ratio (versus IS) data. Menthone calibration for peppermint gave an R² of 0.972. ¹H NMR spectroscopy was conducted on both menthol and menthone. Good correspondence with reference spectra was obtained. About 80 μg of isolated menthol and menthone solute was collected over a sequence of 80 repeat injections from the peppermint sample, as assayed by 600 MHz ¹H NMR analysis (～100% recovery for menthol from peppermint). A procedure is proposed for prediction of number of injections required to acquire sufficient material for NMR detection.     

Rapid sequential separation of essential oil compounds using continuous heart-cut multi-dimensional gas chromatography-mass spectrometry

A method for separation and identification of peaks in essential oil samples based on rapid repetitive heart-cutting using multidimensional gas chromatography (MDGC)-mass spectrometry (MS) coupled with a cryotrapping interface is described. Lavender essential oil is analyzed by employing repetitive heart-cut intervals of 1.00 and 1.50 min, achieved in a parallel MDGC-MS/GC-FID experiment. The number of peaks that were detected in 1D GC operation above a given response threshold more than tripled when MDGC-MS employing the cryotrapping module method was used. In addition, MDGC-MS enabled detection of peaks that were not individually evident in 1D GC-MS, owing to effective deconvolution in time of previously overlapped peaks in 1D GC. Thus separation using the cryomodulation approach, without recourse to using deconvolution software, was possible. Peaks widths decreased by about 5-7-fold with the described method, peak capacity increased from about 9 per min to 60 per min, and greater sensitivity results. Repeatability of retention times for replicate analyses in the multidimensional mode was better than 0.02% RSD. The present study suggests that the described heart-cutting technique using MDGC-MS can be used for general improvement in separation and identification of volatile compounds.     

Characterization and classification of rat neural stem cells and differentiated cells by comparative metabolic and lipidomic profiling

It is necessary to characterize and classify neural stem cells (NSCs) and differentiated cells (DCs) for potential use of NSC to treat neurodegenerative diseases. We therefore performed an analysis of NSCs and DCs using gas chromatography mass spectrometry (GC-MS) and direct infusion mass spectrometry (DI-MS) with elaborate multivariate statistical analysis for the characterization and classification of rat NSCs and DCs. GC-MS and DI-MS detected a total of 92 metabolites and lipids in NSCs and DCs, and the levels of 72 of them differed significantly between NSCs and DCs. The optimal model for partial least squares (PLS) discriminant analysis was constructed by applying 3 and 2 PLS components with a unit-variance scaling method for classifying NSCs and DCs based on the data obtained in the GC-MS and DI-MS analyses, respectively. The obtained results from PCA and PLS-DA suggest that creatinine, lactic acid, lysine, glutamine, glycine, pyroglutamic acid, PG 18:1/20:2, PS 18:0/20:2, PI 18:0/20:3, PC 16:0/20:4, PI 16:0/20:4, and PI 18:1/20:4 were the main contributors that provided distinct characteristics of NSCs and DCs. The results of this study suggest objective and complementary criteria for the characterization and classification of NSCs and DCs for potential clinical applications.

Graphical abstract