Samples Of Complex Composition Research Articles

Molecularly imprinted inorganic supports in high-performance liquid chromatography and solid-phase extraction

Selective preconcentration of analytes or groups of similar analytes is often useful in the analysis of biological and environmental samples and other samples of complex composition. The problem of the selective recognition of analytes can be solved using the method of molecular imprinting, which is gaining wide acceptance as an alternative to the use of biological recognition agents, for example antibodies, and favors overcoming the shortcomings inherent to the last named ones. The use of molecularly imprinted materials as adsorbents for solid-phase extraction and high-performance liquid chromatography ensures an increase in the selectivity of analyte determination in complex matrixes. This paper is a review of methods for the preparation of molecular imprints on the surface of inorganic matrixes, including nanostructured ones, and of the applications of these materials to chemical analysis.

Avian influenza virus RNA extraction.

The efficient extraction and purification of viral RNA is critical for down-stream molecular applications whether it is the sensitive and specific detection of virus in clinical samples, virus gene cloning and expression, or quantification of avian influenza (AI) virus by molecular methods from experimentally infected birds. Samples can generally be divided into two types; enriched (e.g. virus stocks) and clinical. Clinical type samples, which may be tissues or swab material, are the most difficult to process due to the complex sample composition and possibly low virus titers. In this chapter two well established procedures for the isolation of AI virus RNA from common clinical specimen types and enriched virus stocks for further molecular applications will be presented.

Using arc-excitation atomic emission spectrometry in studying the microelement composition of bottom sediments

A procedure is developed for the simultaneous determination of trace metals in river, sea, and lake bottom sediments by atomic emission spectrometry. A present-day scenario using a new system of spectrum record and universal buffer mixtures ensures sufficiently reliable results of analysis of samples of complex composition.

Comprehensive two-dimensional gas chromatography in metabolomics

One of the major objectives in metabolomics is the identification of subtle changes in metabolite profiles as affected by genetic or environmental factors. Comprehensive two-dimensional gas chromatography (GC × GC) hyphenated to a fast-acquisition mass spectrometer is a well-established analytical technique to study the composition of complex samples due to its enhanced separation capacity, sensitivity, peak resolution, and reproducibility. This review reports applications of GC × GC to metabolomics studies of sample of different types (biofluid, cells, tissue, bacteria, yeast, plants), and discusses its advantages and limitations.

Sensitive sandwich ELISA based on a gold nanoparticle layer for cancer detection

The availability of techniques for the sensitive detection of early stage cancer is crucial for patient survival. Our previous research (Langmuir, 2011, 27, 2155-2158) showed that gold nanoparticle layers (GNPL) used in indirect format ELISA amplified the signal, and gave a lower limit of detection (LOD) compared with commercial ELISA plates. However, due to its intrinsic limitations, indirect ELISA is not suitable for samples of complex composition, such as serum, plasma, etc., thus limiting the clinical performance of this kind of ELISA. In the work reported here, a GNPL-based sandwich format ELISA was developed, which showed superiority in terms of detection limit and sensitivity in the determination of rabbit IgG in buffer. More importantly, experiments using plasma spiked with carcinoembryonic antigen (CEA) as a representative biomarker showed that our GNPL-based ELISA assay amplified the signal and lowered the LOD compared to other assays, including commercialized CEA ELISA kits. This simple and cost-effective GNPL-based sandwich ELISA holds promise in clinical applications.

Identification of iron oxidation states in the products of interaction of Na2O2 and Fe2O3 by Mössbauer absorption spectroscopy

The first stage of the solid-phase reaction of Na2O2 and Fe2O3 yields a tetravalent iron derivative. The product is unstable and disproportionates to form compounds with different oxidation states of iron. Analysis of their Mossbauer spectra was performed with the DISCVER program based on the Afanas’ev-Chuev method. At the early stage of analysis, the program identifies the maximal possible number of well-defined lines in the spectrum with a specified statistical quality and, thus, discerns a large number of known and unknown iron derivatives (phases) in samples of complex composition. Previously unknown highest oxidation states of iron from +5 to +8 were identified.

MALDI profiling of human lung cancer subtypes.

BackgroundProteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.Methodology/Principal FindingsIn this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree–based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non–small cell lung cancer histological subtypes.Conclusions/SignificanceA novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.

Using combinations of principal component scores from different spectral ranges in near-infrared region to improve discrimination for samples of complex composition

Principal component analysis (PCA) is widely used as an exploratory data analysis tool in the field of vibrational spectroscopy, particularly near-infrared (NIR) spectroscopy. PCA represents original spectral data containing large variables into a few feature-containing variables, or scores. Although multiple spectral ranges can be simultaneously used for PCA, only one series of scores generated by merging the selected spectral ranges is generally used for qualitative analysis. Alternatively, the combined use of an independent series of scores generated from separate spectral ranges has not been exploited. The aim of this study is to evaluate the use of PCA to discriminate between two geographical origins of sesame samples, when scores independently generated from separate spectral ranges are optimally combined. An accurate and rapid analytical method to determine the origin is essentially required for the correct value estimation and proper production distribution. Sesame is chosen in this study because it is difficult to visually discriminate the geographical origins and its composition is highly complex. For this purpose, we collected diffuse reflectance near-infrared (NIR) spectroscopic data from geographically diverse sesame samples over a period of eight years. The discrimination error obtained by applying linear discriminant analysis (LDA) was improved when separate scores from two spectral ranges were optimally combined, compared to the discrimination errors obtained when scores from singly merged two spectral ranges were used.

Direct mass spectrometry approaches to characterize polyphenol composition of complex samples

Lower molecular weight polyphenols including proanthocyanidin oligomers can be analyzed after HPLC separation on either reversed-phase or normal phase columns. However, these techniques are time consuming and can have poor resolution as polymer chain length and structural diversity increase. The detection of higher molecular weight compounds, as well as the determination of molecular weight distributions, remain major challenges in polyphenol analysis. Approaches based on direct mass spectrometry (MS) analysis that are proposed to help overcome these problems are reviewed. Thus, direct flow injection electrospray ionization mass spectrometry analysis can be used to establish polyphenol fingerprints of complex extracts such as in wine. This technique enabled discrimination of samples on the basis of their phenolic (i.e. anthocyanin, phenolic acid and flavan-3-ol) compositions, but larger oligomers and polymers were poorly detectable. Detection of higher molecular weight proanthocyanidins was also restricted with matrix-assisted laser desorption ionization (MALDI) MS, suggesting that they are difficult to desorb as gas-phase ions. The mass distribution of polymeric fractions could, however, be determined by analyzing the mass distributions of bovine serum albumin/proanthocyanidin complexes using MALDI-TOF-MS.

Visualization and interpretation of OPLS models based on 2D NMR data

Multivariate analysis on spectroscopic 1H NMR data is well established in metabolomics and other fields where the composition of complex samples is studied. However, biomarker identification can be hampered by overlapping resonances. 2D NMR data provides a more detailed “fingerprint” of the chemical structure and composition of the sample with greatly improved spectral resolution compared to 1H NMR data. In this report, we demonstrate a procedure for the construction of multivariate models based on frequency domain 2D NMR data where the loadings can be visualized as highly informative 2D loading spectra. This method is based on the analysis of raw spectral data without any need for peak picking or integration prior to analysis. Spectral features such as line widths and peak positions are thus retained. Hence, the loadings can be visualized and interpreted on a molecular level as pseudo 2D spectra in order to identify potential biomarkers. To demonstrate this strategy we have analyzed HSQC spectra acquired from populus phloem plant extracts originating from a set of designed experiments with OPLS regression.

Quality control of cosmetic mixtures by NIR spectroscopy

Cosmetic preparations typically consist of mixtures of various compounds of natural origin or their derivatives. Their analysis is made rather difficult by their usually high complexity and is utterly impossible with a single analytical method; also, there is usually little to be gained by determining every individual component of the mixture. Rather, analyses are aimed at ensuring a proper balance between the contents of each component and thus require the use of methods capable of delivering global information. The combined use of near-infrared (NIR) spectroscopy and multivariate spectral processing chemometric techniques has enabled the development of effective methods for establishing the composition of complex samples with acceptable levels of analytical properties, such as accuracy, precision and throughput. In this work, we developed partial least squares calibration models for the determination of each component in a cosmetic mixture, and global indices (viz. the hydroxyl value), simply from the NIR spectrum of the sample. The models thus obtained are accurate enough for use in quality control analyses of cosmetic preparations and provide an effective alternative to existing conventional global methods.

Dicationic ionic liquid stationary phase for GC-MS analysis of volatile compounds in herbal plants

The seeming "dual nature" of ionic liquids (ILs) for separating both apolar and polar compounds suggests that ILs may have a great potential for complex samples like essential oils from herbal plants that contain a great variety of compounds. In the present work, a geminal dicationic IL, 1,9-di(3-vinylimidazolium)nonane bis[(trifluoromethyl)sulfonyl]imidate, was investigated for this purpose. To find the best way to achieve satisfactory separations simultaneously for the compounds in essential oils, the dicationic IL was used as the stationary phase for capillary gas chromatography (GC) in two ways, either in its pure state or as a mixed stationary phase with monocationic ILs and a polysiloxane diluent. Interestingly, it was found that the mixed stationary phase exhibited a much better selectivity for polar and nonpolar compounds than either the dicationic IL or the polysiloxane, suggesting that a kind of synergistic effect occurred when these stationary phases were combined in the way described. A comparison with two commercial stationary phases (polar and nonpolar) indicated that this novel mixed stationary phase behaved in a way closer to a polar stationary phase in terms of selectivity and elution order. The present work demonstrates that the mixed stationary phase is efficient and selective and can be an alternative choice for the GC analysis of samples of complex composition.

A statistical method for chromatographic alignment of LC-MS data

Integrated liquid-chromatography mass-spectrometry (LC-MS) is becoming a widely used approach for quantifying the protein composition of complex samples. The output of the LC-MS system measures the intensity of a peptide with a specific mass-charge ratio and retention time. In the last few years, this technology has been used to compare complex biological samples across multiple conditions. One challenge for comparative proteomic profiling with LC-MS is to match corresponding peptide features from different experiments. In this paper, we propose a new method--Peptide Element Alignment (PETAL) that uses raw spectrum data and detected peak to simultaneously align features from multiple LC-MS experiments. PETAL creates spectrum elements, each of which represents the mass spectrum of a single peptide in a single scan. Peptides detected in different LC-MS data are aligned if they can be represented by the same elements. By considering each peptide separately, PETAL enjoys greater flexibility than time warping methods. While most existing methods process multiple data sets by sequentially aligning each data set to an arbitrarily chosen template data set, PETAL treats all experiments symmetrically and can analyze all experiments simultaneously. We illustrate the performance of PETAL on example data sets.

Ion-Chromatographic Determination of Low Concentrations of Nitrate in Solutions of High Salinity

The determination of low concentrations of inorganic anions in water samples of high salinity is one of the most difficult tasks in analytical chemistry. There is, however, an increasing demand for the exact and reliable determination of nutrients even at very low concentration levels in environmental samples of complex composition. We therefore present the development, quality control and application of an easy and rugged method that makes possible the determination of trace amounts of nitrate in solutions with high concentrations of chloride (>30 000 mg L−1) and a hundred-fold excess of bromide ions, based on ion chromatography with UV detection at λ = 210 nm. While chloride ions show no absorbance at this wavelength, a bromide concentration >1500 µg L−1 severely interferes with nitrate determination at the trace level. On the contrary, at nitrate concentrations of >500 µg L−1, this bromide interference becomes negligible. Consequently, to overcome the impact of bromide on the trace level determination of nitrate, all samples are spiked with a definite volume of a nitrate standard solution to obtain an overall nitrate concentration of >500 µg L−1 which allows the exact and reliable determination of nitrate concentrations down to 25 µg L−1. We will report the successful application of the method to samples of high salinity.

New Robust Bilinear Least Squares Method for the Analysis of Spectral-pH Matrix Data

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.

Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling

Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios.

Fluorescence resonance energy transfer-based detection of analytes using antiidiotypic affinity protein pairs

A new method for specific detection of proteins based on fluorescence resonance energy transfer (FRET) using affinity proteins (affibodies) derived from combinatorial engineering of Staphylococcal protein A has been developed. Antiidiotypic affibody pairs were used in a homogeneous competitive binding assay, where the idiotypic, target-specific affibody was labeled with fluorescein and the antiidiotypic affibody was labeled with tetramethylrhodamine. Intermolecular FRET between the two fluorescent probes was observed in the antiidiotypic affibody complex, but upon addition of target protein the antiidiotypic affibody was displaced, which was monitored by a shift in the relative emission of the donor and acceptor fluorophores. The feasibility of the system was demonstrated by the detection of IgA and Taq DNA polymerase with high specificity, using two different antiidiotypic affibody pairs. Detection of Taq DNA polymerase in 25% human plasma was successfully carried out, demonstrating that the method can be used for analysis of proteins in samples of complex composition.

Surface‐Analytical Studies on Environmental and Geochemical Surface Processes

AbstractFor Abstract see ChemInform Abstract in Full Text.

Cyclodextrin-modified micellar electrokinetic chromatography for the analysis of Esterom, a topical product consisting of hydrolyzed benzoylecgonine in propylene glycol.

Esterom, a new drug currently in human clinical trials, is a mixture of compounds in a propylene glycol vehicle. It is being evaluated as a topical treatment to aid in the relief of muscle pain and to increase range of motion. Benzoylecgonine is the major component of Esterom and there are at least nine other minor constituents, including four hydroxypropyl esters that have multiple diasteriomers. The aim of the study was to develop a capillary electrophoresis method for the simultaneous separation of the main components in Esterom, including the multiple proposed diastereomers of the esters. Due to the complex sample composition, the use of micelles and cyclodextrins as buffer modifiers was evaluated. A cyclodextrin-modified micellar electrokinetic chromatography method was able to determine 7 of the 8 UV-active Esterom components, with baseline separation of 7 of the 10 diastereomers of the hydroxypropyl esters.

Surface-analytical studies on environmental and geochemical surface processes.

The surface chemical compositions of solid samples from environmental and geological sources can differ from the bulk or average compositions, because of changes caused by adsorption, dissolution, oxidation, etc. Accordingly, analytical information on surface layers is important for a better understanding of the environmental chemistry involving solid surfaces. The rapid development of surface-analytical techniques has enabled us to probe the surface chemistry of environmental and geological solid samples of complex composition. This article demonstrates some examples of important items of information that can be obtained by applying surface-analytical techniques, such as X-ray photoelectron spectroscopy and secondary ion mass spectrometry, to environmental and geological samples. The surface analysis of fly ashes, soils, sediments and weathered silicate minerals is reviewed.