A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

Investigation of the postcure reaction and surface energy of epoxy resins using time-of-flight secondary ion mass spectrometry and contact-angle measurements

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to investigate correlations between the molecular changes and postcuring reaction on the surface of a diglycidyl ether of bisphenol A and diglycidylether of bisphenol F based epoxy resin cured with two different amine-based hardeners. The aim of this work was to present a proof of concept that ToF-SIMS has the ability to provide information regarding the reaction steps, path, and mechanism for organic reactions in general and for epoxy resin curing and postcuring reactions in particular. Contact-angle measurements were taken for the cured and postcured epoxy resins to correlate changes in the surface energy with the molecular structure of the surface. Principal components analysis (PCA) of the ToFSIMS positive spectra explained the variance in the molecular information, which was related to the resin curing and postcuring reactions with different hardeners and to the surface energy values. The first principal component captured information related to the chemical phenomena of the curing reaction path, branching, and network density based on changes in the relative ion density of the aliphatic hydrocarbon and the C₇H₇O⁺ positive ions. The second principal component captured information related to the difference in the surface energy, which was correlated to the difference in the relative intensity of the C_xH_yN_z⁺ ions of the samples. PCA of the negative spectra provided insight into the extent of consumption of the hardener molecules in the curing and postcuring reactions of both systems based on the relative ion intensity of the nitrogen-containing negative ions and showed molecular correlations with the sample surface energy.

Time-of-flight secondary ion mass spectrometry investigation of epoxy resin curing behavior in real time

Time-of-flight secondary ion mass spectrometry and principal components analysis were used in real time to monitor the progress of curing reactions on the surface of a diglycidyl ether of bisphenol A (DGEBA) and diglycidyl ether of bisphenol F (DGEBF) epoxy resin blend reacted with the diamine hardener isophorone diamine at different time intervals. Molecular ions in the mass spectra that characterized the curing reactions steps, including blocking, coupling, branching, and crosslinking, were identified. The aliphatic hydrocarbon ions were correlated to the curing reaction rate, and this indicated that coupling and branching occurred much faster than the blocking and crosslinking curing reactions steps. The total conversion of the coupling and branching reaction steps were followed on the basis of changes with time in the relative ion intensity of molecular ions assigned to the DGEBA/DGEBF, aliphatic hydrocarbon, epoxide, and aromatic ring structures. Indicative measures of crosslinking density were monitored through the observation of changes in the ratio of the relative intensities of the aliphatic hydrocarbon and hydroxyl molecular ions over time. The curing reaction conversion was established by the observation of the changes in the relative ion intensity of the molecular ions that were related to the DGEBA/ DGEBF molecules.

Analysis of self-assembled monolayer interfaces by electrospray mass spectrometry : a gentle approach

A general mass spectrometry technique for the characterization of alkanethiol-modified surfaces is presented. Alkanethiol self-assembled onto a gold surface (in this case, peptides were attached to the gold surface via a thiolate bond) was reductively desorbed in 0.05 M KOH in the presence of octadecyl-derivatized silica gel. The peptide adsorbed onto the silica gel, whereupon it could be filtered, washed to remove any salts, and then eluted using a mixture of 4:1 v/v methanol/water. The eluant containing the peptide was injected into a Fourier transform ion-cyclotron resonance mass spectrometer (FTICR/MS) via electrospray ionization. The spectrum showed no fragmentation of the peptide, demonstrating the gentleness of the technique. This simple procedure is not limited to FTICR/MS and could be adapted to other mass spectrometers.

Dimensionality reduction of protein mass spectrometry data using random projection

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.

Analysis of residues of prometryne and acetochlor in soil-water system by solid-phase extraction and gas chromatography/mass spectrometry

A highly sensitive and simple analytical method was developed for analyzing the binary mixed pesticides of prometryne and acetochlor in soil–water system by gas chromatography/mass spectrometry (GC/MS). The sample solution was first purified by C18 solid-phase extraction column, which was leached by acetone. The leachate was enriched to 1.0 mL by pressure blowing concentrator and then analyzed by GC/MS. The linear calibration curves were showed in the range of 1–15 μg/mL with a correlation coefficient of 0.9991. The average recoveries (n = 5) were between 95.3 and 115.7%, with relative standard deviations ranged from 1.71 and 7.95%. The limits of detection of Prometryne/Acetochlor were up to 0.06 and 0.17 μg/mL, respectively. This method provides a reliable approach to examine and evaluate the residues of prometryne and acetochlor in the soil–water system.

Mass spectrometry cancer data classification using wavelets and genetic algorithm

This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.

Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system

An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.

Phytochemical analysis of Hibiscus caesius using high performance liquid chromatography coupled with mass spectrometry

Various species in genus Hibiscus are traditionally known for their therapeutic attributes. The present study focused on the phytochemical analysis of a rather unexplored species Hibiscus caesius (H. caesius), using high-pressure liquid chromatography coupled with mass spectrometry (HPLC-MS). The analysis revealed five major compounds in the aqueous extract, viz. vanillic acid, protocatechoic acid, quercetin, quercetin glucoside and apigenin, being reported for the first time in H. caesius. Literature suggests that these compounds have important pharmacological traits such as anti-cancer, anti-inflammatory, anti-bacterial and hepatoprotective etc. however, this requires further pharmacological investigations at in vitro and in vivo scale. The above study concluded the medicinal potential of H. caesius.

Analysis of mammalian cell proliferation and macromolecule synthesis using deuterated water and gas chromatography-mass spectrometry.

Deuterated water (²H₂O), a stable isotopic tracer, provides a convenient and reliable way to label multiple cellular biomass components (macromolecules), thus permitting the calculation of their synthesis rates. Here, we have combined ²H₂O labelling, GC-MS analysis and a novel cell fractionation method to extract multiple biomass components (DNA, protein and lipids) from the one biological sample, thus permitting the simultaneous measurement of DNA (cell proliferation), protein and lipid synthesis rates. We have used this approach to characterize the turnover rates and metabolism of a panel of mammalian cells in vitro (muscle C2C12 and colon cancer cell lines). Our data show that in actively-proliferating cells, biomass synthesis rates are strongly linked to the rate of cell division. Furthermore, in both proliferating and non-proliferating cells, it is the lipid pool that undergoes the most rapid turnover when compared to DNA and protein. Finally, our data in human colon cancer cell lines reveal a marked heterogeneity in the reliance on the de novo lipogenic pathway, with the cells being dependent on both 'self-made' and exogenously-derived fatty acid.