Application of thiophilic chromatography to deplete serum immunoglobulins in sample preparation for bidimensional electrophoresis

Serum is a typical sample for non-invasive studies in clinical research. Its proteome characterization is challenging, since requires extensive protein depletion. Methods used nowadays for removal of high-abundance proteins are expensive or show quite often a low loading capacity, which has strong repercussions on the number of samples and replicates per analysis.In order to deplete immunoglobulins (Igs) and albumin (HSA) from 1 mL serum samples, we have developed a protocol based on a combination of thiophilic chromatography, not previously used in clinical proteomics, and a HSA-specific resin. Ig/HSA-depleted samples, immunoglobulinome and albuminone were analyzed by 2-DE. Thiophilic chromatography, coupled with HSA-depletion, allows a good 2-DE resolution as well as the visualization of new spots. Moreover, it yields enough protein to evaluate technical variability and facilitate subsequent protein identification. To validate the protocol, we carried out a preliminary comparative study between triplicate Igs/HSA-depleted serum samples from healthy control individuals and recently diagnosed/untreated rheumatoid arthritis (RA) patients. RA patients showed several acute phase proteins, as well as additional serum proteins, differentially and significantly regulated.Therefore, thiophilic chromatography can be used as an efficient and economical method in 2-DE to deplete immunoglobulins from large human serum samples before a more extensive fractioning.     

Feature extraction and selection from volatile compounds for analytical classification of Chinese red wines from different varieties

This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures.     

TBSOTf-assisted three component coupling of epoxides, THF, and ylides derived from the phosphoniosilylation products of enones and α,β-unsaturated lactones

Ylides 3, derived from the phosphoniosilylation products of enones and α,β-unsaturated lactones, were reacted with epoxides 4 and TBSOTf in THF. Interestingly, the three component coupling products 5-8 were obtained after subsequent desilylation.     

Differentiation of opium and poppy straw using capillary electrophoresis and pattern recognition techniques

Opium samples from four different locations and poppy straw from different plant varieties have been assayed using micellar capillary electrophoresis incorporating a sweeping technique. Individual alkaloids (morphine, codeine, papaverine, noscapine, thebaine, oripavine, reticuline and narceine) were quantitatively determined in the different samples by a validated capillary electrophoresis method. Unsupervised pattern recognition of the opium samples and the poppy straw samples using hierarchical cluster analysis (HCA) and principal component analysis (PCA), showed distinct clusters. Supervised pattern recognition using soft independent modelling of class analogy (SIMCA) was performed to show individual groupings and allow unknown samples to be classified according to the models built using the CZE assay results.     

Principal component analysis of urine metabolites detected by NMR and DESI–MS in patients with inborn errors of metabolism

Urine metabolic profiles of patients with inborn errors of metabolism were examined with nuclear magnetic resonance (NMR) and desorption electrospray ionization mass spectrometry (DESI-MS) methods. Spectra obtained from the study of urine samples from individual patients with argininosuccinic aciduria (ASA), classic homocystinuria (HCY), classic methylmalonic acidemia (MMA), maple syrup urine disease (MSUD), phenylketonuria (PKU) and type II tyrosinemia (TYRO) were compared with six control patient urine samples using principal component analysis (PCA). Target molecule spectra were identified from the loading plots of PCA output and compared with known metabolic profiles from the literature and metabolite databases. Results obtained from the two techniques were then correlated to obtain a common list of molecules associated with the different diseases and metabolic pathways. The combined approach discussed here may prove useful in the rapid screening of biological fluids from sick patients and may help to improve the understanding of these rare diseases.     

Studies on the Thermal Decomposition of Benzoic Acid and its Derivatives

The thermal decomposition of benzoic acid and its derivatives containing —OH, —NH₂, —COOH and —SO₃H functional groups as substituents in ortho, meta and (or) para position together with sulphanilic acid was investigated. The analyses were performed using derivatograph, sample mass ranged from 50 to 200 mg, heating rates from 3 to 15 K min⁻¹ and static air atmosphere. It has been established that thermal decomposition of these aromatic acids proceeds through three common stages. In the first stage the phase transformations occur. The following two stages are due to the formation of intermediate products of the thermal decomposition and their combustion. Principal component analysis (PCA) was applied for evaluation of the results. Thanks to this method the influence of specific functional groups and their positions on the benzene ring on the thermal decomposition of the compounds under investigation was determined. This revised version was published online in July 2006 with corrections to the Cover Date.     

Molecular speciation of microparticles: application of pattern recognition techniques to laser microprobe mass spectrometric data

Attempts were made to enhance the ability of laser microprobe mass spectrometry (LAMMS) to identify molecular species in individual microparticles by applying pattern recognition methods. Principal component analysis (PCA) and canonical discriminant analysis were applied to LAMMS data for nickel-containing environmental particles. Detailed comparison of the two statistical methods demonstrated the utility of PCA. The successful application was highly dependent on the use of appropriate spectral normalization and feature extraction techniques prior to PCA. Although the test system involved only a small number of standard compounds, the LAMMS data were complicated by the effects of intra-particle heterogeneity common to environmental samples and by instrumental limitations. Pattern recognition techniques provided more accurate quantitative assignments of molecular species than were available by qualitative inspection of characteristic cluster ions or by simple spectral subtraction to compare particle data with a library of standard compounds. Results were substantiated by comparison with bulk analysis studies using wet chemical techniques.     

一种面向多组分定量的通用谱图解析方法

在一个多组分系统中各组分的定量分析是非常重要的。现代光谱如拉曼光谱(RS)、傅里叶变换红外(FT-IR)光谱、紫外-可见(UV-Vis)光谱、核磁共振(NMR)、质谱(MS)等,可通过获取丰富峰信号的谱图对样品进行各角度的详尽描述。然而,由于谱图的复杂性及其解析工作的繁重,使得仅通过样品谱图来同时量化混合物中的每个组分成为很具挑战性的工作。在这项研究中,我们首次介绍了一个名为定量主成分分析(q PCA)的可靠策略,快速计算混合物中每个成分的比例,而不需要任何手动解谱。通过使用纯组分的谱图作为参考,多组分系统的谱图可以通过PCA自动分辨并解析,然后就可以使用我们的计算方法来计算每个组分的比例。计算机建模实验和RS、FT-IR、UV-Vis、NMR、MS实验都证明了这一策略胜任多组分系统的快速定量工作。     

Electrochemical approach for synthesis of 3-substituted indole derivatives

An efficient and economical method was developed for synthesis of 3-substituted indole by using electrochemically induced condensation of various aldehyde, indole and malononitrile.     

Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

Near infrared (NIR) spectrometry was used for the rapid characterization of quality parameters in desi chickpea flour (besan). Partial least square regression, principal component regression (PCR), interval partial least squares (iPLS), and synergy interval partial least squares (siPLS) were used to determine the protein, carbohydrate, fat, and moisture concentrations of besan. Spectra were collected in reflectance mode using a lab-built predispersive filter-based instrument from 700 to 2500?nm. The quality parameters were also determined by standard methods. The root mean square error (RMSE) for the calibration and validation sets was used to evaluate the performance of the models. The correlation coefficients for moisture, fat, protein, and carbohydrates in chickpea flour exceeded 0.96 using PLS and PCR models using the full spectral range. Wavelengths from 2100 to 2345?nm had the lowest RMSE for quality parameters by iPLS. The error was further decreased by 0.41, 0.1, and 1.1% for carbohydrates, fats, and proteins by siPLS. The NIR spectral regions yielding the lowest RMSE of prediction were 1620–2345?nm for carbohydrates, 1180–1590?nm and 1860–2094?nm for fat, and 1700–2345?nm for proteins. The study shows that chickpea flour quality parameters were accurately determined using the optimized wavelengths.