Application of sequential paired covariance to liquid chromatography-mass spectrometry data enhancements in both the signal-to-noise ratio and the resolution of analyte peaks in the chromatogram

Sequential paired covariance for improved visualization of mass spectrometry imaging datasets

Untargeted analyses in mass spectrometry imaging produce hundreds of ion images representing spatial distributions of biomolecules in biological tissues. Due to the large diversity of ions detected in untargeted analyses, normalization standards are often difficult to implement to account for pixel-to-pixel variability in imaging studies. Many normalization strategies exist to account for this variability, but they largely do not improve image quality. In this study, we present a new approach for improving image quality and visualization of tissue features by application of sequential paired covariance (SPC). This approach was demonstrated using previously published tissue datasets such as rat brain and human prostate with different biomolecules like metabolites and N-linked glycans. Data transformation by SPC improved ion images resulting in increased smoothing of biological features compared with commonly used normalization approaches.

An adaptive single-well stochastic resonance algorithm applied to trace analysis of clenbuterol in human urine

Based on the theory of stochastic resonance, an adaptive single-well stochastic resonance (ASSR) coupled with genetic algorithm was developed to enhance the signal-to-noise ratio of weak chromatographic signals. In conventional stochastic resonance algorithm, there are two or more parameters needed to be optimized and the proper parameters values were obtained by a universal searching within a given range. In the developed ASSR, the optimization of system parameter was simplified and automatic implemented. The ASSR was applied to the trace analysis of clenbuterol in human urine and it helped to significantly improve the limit of detection and limit of quantification of clenbuterol. Good linearity, precision and accuracy of the proposed method ensure that it could be an effective tool for trace analysis and the improvement of detective sensibility of current detectors.

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.

Coupled stochastic resonance to improve chromatography determinations

Traditional bistable stochastic resonance has been demonstrated as an effective tool to detect the weak signal in a strong noise background. To achieve a better signal-to-noise ratio for the output signal, a coupled stochastic resonance was developed by nonlinearly coupling two double-well potential systems. The response characteristics of coupled stochastic resonance subjected to analytical signals have been investigated and compared with those of bistable stochastic resonance. The improvement of chromatographic determination with the proposed coupled stochastic resonance was validated by both simulated signals and chromatographic signals. The weak signals from both simulated data and plasma samples with different concentrations were all amplified significantly and the quantitative relationship between different concentrations and responses was kept well. It is reasonable to believe that coupled stochastic resonance could play an important role in applications where quantitative determination of low-concentration samples is crucial.

Chemometric analysis of complex hyphenated data

Component detection algorithm (CODA) is a method to quickly extract the high-quality mass chromatograms from complex liquid chromatography/electrospray ionization mass spectrometry (LC/MS) data sets, saving operators hours of analysis time. It appeared, however, that mass chromatograms with a limited baseline problem were ignored. This paper describes several methods to increase the tolerance for mass chromatograms with a baseline.

Robust estimation of peptide abundance ratios and rigorous scoring of their variability and bias in quantitative shotgun proteomics

The abundance ratio between the light and heavy isotopologues of an isotopically labeled peptide can be estimated from their selected ion chromatograms. However, quantitative shotgun proteomics measurements yield selected ion chromatograms at highly variable signal-to-noise ratios for tens of thousands of peptides. This challenge calls for algorithms that not only robustly estimate the abundance ratios of different peptides but also rigorously score each abundance ratio for the expected estimation bias and variability. Scoring of the abundance ratios, much like scoring of sequence assignment for tandem mass spectra by peptide identification algorithms, enables filtering of unreliable peptide quantification and use of formal statistical inference in the subsequent protein abundance ratio estimation. In this study, a parallel paired covariance algorithm is used for robust peak detection in selected ion chromatograms. A peak profile is generated for each peptide, which is a scatterplot of ion intensities measured for the two isotopologues within their chromatographic peaks. Principal component analysis of the peak profile is proposed to estimate the peptide abundance ratio and to score the estimation with the signal-to-noise ratio of the peak profile (profile signal-to-noise ratio). We demonstrate that the profile signal-to-noise ratio is inversely correlated with the variability and bias of peptide abundance ratio estimation.

Second-order peak detection for multicomponent high-resolution LC/MS data

The first step when analyzing multicomponent LC/MS data from complex samples such as biofluid metabolic profiles is to separate the data into information and noise via, for example, peak detection. Due to the complex nature of this type of data, with problems such as alternating backgrounds and differing peak shapes, this can be a very complex task. This paper presents and evaluates a two-dimensional peak detection algorithm based on raw vector-represented LC/MS data. The algorithm exploits the fact that in high-resolution centroid data chromatographic peaks emerge flanked with data voids in the corresponding mass axis. According to the proposed method, only 4 per thousand of the total amount of data from a urine sample is defined as chromatographic peaks; however, 94% of the raw data variance is captured within these peaks. Compared to bucketed data, results show that essentially the same features that an experienced analyst would define as peaks can automatically be extracted with a minimum of noise and background. The method is simple and requires a priori knowledge of only the minimum chromatographic peak width-a system-dependent parameter that is easily assessed. Additional meta parameters are estimated from the data themselves. The result is well-defined chromatographic peaks that are consistently arranged in a matrix at their corresponding m/z values. In the context of automated analysis, the method thus provides an alternative to the traditional approach of bucketing the data followed by denoising and/or one-dimensional peak detection. The software implementation of the proposed algorithm is available at http://www.anchem.su.se/peakd as compiled code for Matlab.

Component Detection Weighted Index of Analogy: Similarity Recognition on Liquid Chromatographic Mass Spectral Data for the Characterization of Route/Process Specific Impurities in Pharmaceutical Tablets

Detection and identification of impurities in pharmaceuticals is an essential task for determining the possible infringement of a patent. This article reports a multivariate analysis method to distinguish between tablets of the same substance on the basis of their origin, by characterizing route/process specific impurities via diagnostic ion chromatograms, using liquid chromatography/mass spectrometry (LC/MS). The approach is based on the formulation of a novel index that quantifies the similarity between LC/MS samples, named the component detection weighted index of analogy. The index estimates similarity by fully exploiting the two-dimensional nature of the data, where the relative contribution of chromatograms relates to their quality and noise level. Results show that well-defined clusters are formed according to the origin of tablets; a series of ions are identified as characterizing each class and can be used to predict the origin of unknown tablet samples. The method presented is designed for analysis of larger data sets and can be suitable for exploratory analysis where any a priori knowledge on the data is scarce or absent, hence requiring the acquisition of chromatograms in a broad m/z range.

A Universal Denoising and Peak Picking Algorithm for LC−MS Based on Matched Filtration in the Chromatographic Time Domain

A new denoising and peak picking algorithm (MEND, matched filtration with experimental noise determination) for analysis of LC-MS data is described. The algorithm minimizes both random and chemical noise in order to determine MS peaks corresponding to sample components. Noise characteristics in the data set are experimentally determined and used for efficient denoising. MEND is shown to enable low-intensity peaks to be detected, thus providing additional useful information for sample analysis. The process of denoising, performed in the chromatographic time domain, does not distort peak shapes in the m/z domain, allowing accurate determination of MS peak centroids, including low-intensity peaks. MEND has been applied to denoising of LC-MALDI-TOF-MS and LC-ESI-TOF-MS data for tryptic digests of protein mixtures. MEND is shown to suppress chemical and random noise and baseline fluctuations, as well as filter out false peaks originating from the matrix (MALDI) or mobile phase (ESI). In addition, MEND is shown to be effective for protein expression analysis by allowing selection of a large number of differentially expressed ICAT pairs, due to increased signal-to-noise ratio and mass accuracy.

Shah convolution Fourier transform detection: multiple-sample injection technique

For the direct measurement of electrophoretic mobility, multiple-point (Shah function) detected, time-domain detector signals were converted into frequency-domain plots by means of Fourier transformation. Multiple sample plugs (up to a maximum of three) were introduced into the separation channel and the resultant time-domain signals were then Fourier-transformed. The multiple-sample injection technique has been successfully demonstrated for a one-component system and a separation. Though the number of fluorescing zones flowing through the illuminated length of the channel is greater than the number of analytes in the solution, Shah convolution Fourier transform detection (SCOFT) is able to identify the number of fluorescent species in the solution based on their migration velocities. The height of the fundamental peak increases as the number of injected sample plugs is increased. More importantly, the signal-to-noise ratio (S/N) is found to be proportional to the number of injected sample plugs. With these findings, the multiple-sample injection technique certainly has got many potential applications in trace analysis. The technique would be equally applicable to other separation techniques (e.g., high-performance liquid chromatography) and detection methods (e.g., absorption, refractive index).

Analyte identification in capillary electrophoretic separation techniques

A review on applications of on-line hyphenation in capillary electrophoresis and capillary electrochromatography for the identification of migrating analytes is presented. There is an urgent need for unambiguous analyte identification by combining spectral information and observed migration times, because the parameters influencing the migration times and separation efficiencies in these separation techniques are not easily controlled, especially when real samples containing unknown interferences have to be analyzed. The spectrometric techniques covered here are ultraviolet and visible radiation (UV/Vis) absorption, fluorescence including fluorescence line-narrowing spectroscopy, Raman spectroscopy, nuclear magnetic resonance and mass spectrometry. Attention is essentially confined to literature reports in which the extra information provided by the detector is really used for identification purposes, especially in real-life samples, while the interfacing as such and analyte detectabilities in standard solutions are only briefly discussed. This article covers an extensive fraction of the literature published on this topic until the beginning of 1998.