Using a cross‐model loadings plot to identify protein spots causing 2‐DE gels to become outliers in PCA

The multivariate method PCA is an exploratory tool often used to get an overview of multivariate data, such as the quantified spot volumes of digitized 2‐DE gels. PCA can reveal hidden structures present in the data, and thus enables identification of potential outliers and clustering. Based on PCA, we here present an approach for identification of protein spots causing 2‐DE gels to become outliers. The approach can potentially obviate analytical exclusion of entire 2‐DE gels.     

Chemometrics and visible-near infrared spectroscopic monitoring of red wine fermentation in a pilot scale

The modern wine industry needs tools for process control and quality assessment in order to better manage fermentation or bottling processes. During wine fermentation it is important to measure both substrate and product concentrations (e.g. sugars, phenolic compounds), however, the analysis of these compounds by traditional means requires sample preparation and in some cases several steps of purification are needed. The combination of visible/near-infrared (Vis/NIR) spectroscopy and chemometrics potentially provides an ideal solution to accurately and rapidly monitor physical or chemical changes in wine during processing without the need for chemical analysis. The aim of this study was to assess the possibility of combining spectral and multivariate techniques, such as principal component analysis (PCA), discriminant partial least squares (DPLS), or linear discriminant analysis (LDA), to monitor time-related changes that occur during red wine fermentation. Samples (n = 652) were collected at various times from several pilot scale fermentations with grapes from either Cabernet Sauvignon or Shiraz varieties, over three vintages (2001-2003) and scanned using a monochromator instrument (Foss-NIRSystems 6500, Silver Spring, MD) in transmission mode (400-2,500 nm). PCA was used to demonstrate consistent progressive spectral changes that occur through the time course of the fermentation. LDA using PCA scores showed that regardless of variety or vintage, samples belonging to a particular time point in fermentation could be correctly classified. This study demonstrates the potential of Vis/NIR spectroscopy combined with chemometrics, as a tool for the rapid monitoring of red wine fermentation.     

Multivariate analysis of sorghum volatiles for the fast screening of sugarcane aphid infestation

The sugarcane aphid, Melanaphis sacchari, has been a severe pest throughout the sorghum field in Texas, which can worse the sorghum yield economically. For this purpose of early detection, the mechanism of herbivore-induced plant volatiles (HIPVs) needs to be utilized in the detection method. In this study, the HayeSep Q adsorbent combined gas chromatography mass spectrometry (GC/MS) was tested to analyze the volatile organic compounds (VOCs) that sorghum can emit when they are in good shape as well as they are infested by the sugarcane aphids, and multivariate techniques were performed for the fast screening of the infestation. Several VOCs identified from Student’s t-test with p < 0.05 were finally chosen as variables for multivariate analysis, and both unsupervised learning of principal component analysis (PCA) and clustering analysis (CA) and supervised learning of linear discriminant analysis (LDA) were done, showing good performance on discrimination between healthy and infested sorghum.     

Sexual dimorphism in shells of Cochlostoma septemspirale (Caenogastropoda, Cyclophoroidea, Diplommatinidae, Cochlostomatinae)

Sexual dimorphisms in shell-bearing snails expressed by characteristic traits of their respective shells would offer the possibility for a lot of studies about gender distribution in populations, species, etc. In this study, the seven main shell characters of the snail Cochlostoma septemspirale were measured in both sexes: (1) height and (2) width of the shell, (3) height and (4) width of the aperture, (5) width of the last whorl, (6) rib density on the last whorl, and (7) intensity of the reddish or brown pigments forming three bands over the shell. The variation of size and shape was explored with statistical methods adapted to principal components analysis (PCA) and linear discriminant analysis (LDA). In particular, we applied some multivariate morphometric tools for the analysis of ratios that have been developed only recently, that is, the PCA ratio spectrum, allometry ratio spectrum, and LDA ratio extractor. The overall separation of the two sexes was tested with LDA cross validation.The results show that there is a sexual dimorphism in the size and shape of shells. Females are more slender than males and are characterised by larger size, a slightly reduced aperture height but larger shell height and whorl width. Therefore they have a considerable larger shell volume (about one fifth) in the part above the aperture. Furthermore, the last whorl of females is slightly less strongly pigmented and mean rib density slightly higher. All characters overlap quite considerably between sexes. However, by using cross validation based on the 5 continuous shell characters more than 90% of the shells can be correctly assigned to each sex.     

Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere packing did not lead to distinct changes in protein pattern. Applying DPLSR to the 2-DE data enabled the selection of protein spots critical for differentiation between 3 and 6 months frozen storage with 12 months frozen storage. Some of these protein spots have been identified by MS/MS, revealing myosin light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two alpha-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse protein changes in cod muscle proteins during storage has revealed new knowledge on the issue and enables a better understanding of biochemical processes occurring.     

Uncovering the unfoldome: enriching cell extracts for unstructured proteins by acid treatment

A method to enrich cell extracts in totally unfolded proteins was investigated. A literature search revealed that 14 of 29 proteins isolated by their failure to precipitate during perchloric acid (PCA) or trichloroacetic acid (TCA) treatment where also shown experimentally to be totally disordered. A near 100 000-fold reduction in yield was observed after 5% or 9% PCA treatment of total soluble E. coli protein. Despite this huge reduction, 158 and 142 spots were observed from the 5% and the 9% treated samples, respectively, on silver-stained 2-D SDS-PAGE gels loaded with 10 microg of protein. Treatment with 1% PCA was less selective with more visible spots and a greater than 3-fold higher yield. A substantial yield of unprecipitated protein was obtained after 3% TCA treatment, suggesting that the common use of TCA precipitation prior to 2-D gel analysis may result in loss of unstructured protein due to their failure to precipitate. Our preliminary analysis suggests that treating total protein extracts with 3-5% PCA and determining the identities of soluble proteins could be the starting point for uncovering unfoldomes (the complement of unstructured proteins in a given proteome). The 100 000-fold reduction in yield and concomitant reduction in number of proteins achieved by 5% PCA treatment produced a fraction suitable for analysis in its entirety using standard proteomic techniques. In this way, large numbers of totally unstructured proteins could be identified with minimal effort.     

Pre-processing of chromatographic data for principal component analysis

This paper examines the selection of the appropriate representation of chromatogram data prior to using principal component analysis (PCA), a multivariate statistical technique, for the diagnosis of chromatogram data sets. The effects of four process variables were investigated; flow rate, temperature, loading concentration and loading volume, for a size exclusion chromatography system used to separate three components (monomer, dimer, trimer). The study showed that major positional shifts in the elution peaks that result when running the separation at different flow rates caused the effects of other variables to be masked if the PCA is performed using elapsed time as the comparative basis. Two alternative methods of representing the data in chromatograms are proposed. In the first data were converted to a volumetric basis prior to performing the PCA, while in the second, having made this transformation the data were adjusted to account for the total material loaded during each separation. Two datasets were analysed to demonstrate the approaches. The results show that by appropriate selection of the basis prior to the analysis, significantly greater process insight can be gained from the PCA and demonstrates the importance of pre-processing prior to such analysis.     

In search of secreted protein biomarkers for the anti-inflammatory effect of beta2-adrenergic receptor agonists: application of DIGE technology in combination with multivariate and univariate data analysis tools

Two-dimensional difference gel electrophoresis (DIGE) in combination with univariate (Student's t-test) and multivariate data analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to study the anti-inflammatory effects of the beta(2)-adrenergic receptor (beta(2)-AR) agonist zilpaterol. U937 macrophages were exposed to the endotoxin lipopolysaccharide (LPS) to induce an inflammatory reaction, which was inhibited by the addition of zilpaterol (LZ). This inhibition was counteracted by addition of the beta(2)-AR antagonist propranolol (LZP). The extracellular proteome of the U937 cells induced by the three treatments were examined by DIGE. PCA was used as an explorative tool to investigate the clustering of the proteome dataset. Using this tool, the dataset obtained from cells treated with LPS and LZP were separated from those obtained from LZ treated cells. PLS-DA, a multivariate data analysis tool that also takes correlations between protein spots and class assignment into account, correctly classified the different extracellular proteomes and showed that many proteins were differentially expressed between the proteome of inflamed cells (LPS and LZP) and cells in which the inflammatory response was inhibited (LZ). The Student's t-test revealed 8 potential protein biomarkers, each of which was expressed at a similar level in the LPS and LZP treated cells, but differently expressed in the LZ treated cells. Two of the identified proteins, macrophage inflammatory protein-1beta (MIP-1beta) and macrophage inflammatory protein-1alpha (MIP-1alpha) are known secreted proteins. The inhibition of MIP-1beta by zilpaterol and the involvement of the beta(2)-AR and cAMP were confirmed using a specific immunoassay.     

Assessment of protein spot components applying correspondence analysis for peptide mass fingerprint data

Proteins separated by two-dimensional gel electrophoresis (2-DE) may be distributed over several spots. Otherwise, one spot may contain more than one component. The same protein occurring in several spots supposedly represents differently modified protein species that might be of biological relevance. Identification of spots with peptide mass fingerprinting and database searching leads only to the detection of the major spot components. If a spot also contains additional minor protein components, quantitation of spots with protein staining techniques or antibody detection becomes misleading. In order to find spots containing minor components we applied correspondence analysis, a multivariate data exploration method, to peptide mass fingerprint data. Correspondence analysis using peak lists revealed groups of spots containing the same protein with their characteristic mass-to-charge ratio (m/z) values. In order to detect different protein spot components an interactive threshold setting and removal of m/z values with subsequent recalculation of the correspondence analysis using our software tool CorrAn are performed. The usefulness of this methodical approach was shown by a data set of peptide mass fingerprints of 284 spots of Helicobacter pylori 26695 separated by 2-DE.     

Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.     

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent ‘super traits'' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle.     

Biplot presentation of diet composition data: an alternative for fish stomach contents analysis

A multivariate analysis derived from principal components analysis (PCA), and which allows the investigation on diet composition data, is introduced. To illustrate the method, prey composition data of stomach contents of brown trout Salmo trutta L. collected in a regulated stream were used. The diet composition, foraging strategies and related patterns of fish diet variation were analysed at a macrohabitat scale (i.e. riffles and glides) by way of biplots. These graphical presentations were consistent with PCA on proportions.     

Proteomic dataset of mouse aortic smooth muscle cells

In an accompanying study (in this issue, DOI 10.1002/pmic.200402044), we have characterised the proteome of Sca-1(+) progenitor cells, which may function as precursors of vascular smooth muscle cells (SMCs). In the present study, we have analysed and mapped protein expression in aortic SMCs of mice, using 2-DE, MALDI-TOF MS and MS/MS. The 2-D system comprised a non-linear immobilised pH 3-10 gradient in the first dimension (separating proteins with pI values of pH 3-10), and 12%T SDS-PAGE in the second dimension (separating proteins in the range 15,000-150,000 Da). Of the 2400 spots visualised, a subset of 267 protein spots was analysed, with 235 protein spots being identified corresponding to 154 unique proteins. The data presented here are the first map of aortic SMCs and the most extensive analysis of SMC proteins published so far. This valuable tool should provide a basis for comparative studies of protein expression in vascular smooth muscle of transgenic mice and is available on our website hhtp://www.vascular-proteomics.com.     

Proteome analysis of early post-mortem changes in two bovine muscle types: M. longissimus dorsi and M. semitendinosis

To study early post-mortem changes in muscle tissues from bull calves, cytosole proteins from two muscles: M. longissimus dorsi (LD) and M. semitendinosis (ST) at 0 and 24 h after slaughter were analysed by 2-DE. Principal component analysis (PCA) and rotation testing were used to analyse the protein patterns in the two muscles in order to select protein spots that were significantly different at the two time-points. Selected proteins were identified by MALDI-TOF/TOF. Five proteins, namely cofilin, lactoylglutathione lyase, substrate protein of mitochondrial ATP-dependent proteinase SP-22, HSP 27 and HSP20, were changed in both LD and ST muscles during post-mortem storage. Fifteen additional protein changes were observed in either LD or ST muscles, and some of these changes have not previously been observed to change during post-mortem storage of bovine muscles. Further studies will reveal the relevance of these biomarkers for meat quality.     

Rapid diagnosis of malignant pleural mesothelioma and its discrimination from lung cancer and benign exudative effusions using blood serum

Malignant pleural mesothelioma (MPM), an aggressive cancer associated with exposure to fibrous minerals, can only be diagnosed in the advanced stage because its early symptoms are also connected with other respiratory diseases. Hence, understanding the molecular mechanism and the discrimination of MPM from other lung diseases at an early stage is important to apply effective treatment strategies and for the increase in survival rate. This study aims to develop a new approach for characterization and diagnosis of MPM among lung diseases from serum by Fourier transform infrared spectroscopy (FTIR) coupled with multivariate analysis. The detailed spectral characterization studies indicated the changes in lipid biosynthesis and nucleic acids levels in the malignant serum samples. Furthermore, the results showed that healthy, benign exudative effusion, lung cancer, and MPM groups were successfully separated from each other by applying principal component analysis (PCA), support vector machine (SVM), and especially linear discriminant analysis (LDA) to infrared spectra.     

Biospectroscopy to metabolically profile biomolecular structure: a multistage approach linking computational analysis with biomarkers

Biospectroscopy is employed to derive absorbance spectra representative of biomolecules present in biological samples. The mid-infrared region (λ = 2.5 μm-25 μm) is absorbed to give a biochemical-cell fingerprint (v = 1800-900 cm(-1)). Cellular material produces complex spectra due to the variety of chemical bonds present. The complexity and size of spectral data sets warrant multivariate analysis for data reduction, interpretation, and classification. Various multivariate analyses are available including principal component analysis (PCA), partial least-squares (PLS), linear discriminant analysis (LDA), and evolving fuzzy rule-based classifier (eClass). Interpretation of both visual and numerical results facilitates biomarker identification, cell-type discrimination, and predictive and mechanistic understanding of cellular behavior. Biospectroscopy is a high-throughput nondestructive technology. A comparison of biomarkers/mechanistic knowledge determined from conventional approaches to biospectroscopy coupled with multivariate analysis often provides complementary answers and a novel approach for diagnosis of disease and cell biology.     

Proteome analysis of Arabidopsis thaliana by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionisation-time of flight mass spectrometry

In the present study we show results of a large-scale proteome analysis of the recently sequenced plant Arabidopsis thaliana. On the basis of a previously published sequential protein extraction protocol, we prepared protein extracts from eight different A. thaliana tissues (primary leaf, leaf, stem, silique, seedling, seed, root, and inflorescence) and analysed these by two-dimensional gel electrophoresis. A total of 6000 protein spots, from three of these tissues, namely primary leaf, silique and seedling, were excised and the contained proteins were analysed by matrix assisted laser desorption/ionisation time of flight mass spectrometry peptide mass fingerprinting. This resulted in the identification of the proteins contained in 2943 spots, which were found to be products of 663 different genes. In this report we present and discuss the methodological and biological results of our plant proteome analysis.     

The use of principal component analysis as a database mining tool for the exploratory diagnosis of chromatographic processes

The work reported in this paper examines the use of principal component analysis (PCA), a technique of multivariate statistics to facilitate the extraction of meaningful diagnostic information from a data set of chromatographic traces. Two data sets mimicking archived production records were analysed using PCA. In the first a full-factorial experimental design approach was used to generate the data. In the second, the chromatograms were generated by adjusting just one of the process variables at a time. Data base mining was achieved through the generation of both gross and disjoint principal component (PC) models. PCA provided easily interpretable 2-dimensional diagnostic plots revealing clusters of chromatograms obtained under similar operating conditions. PCA methods can be used to detect and diagnose changes in process conditions, however results show that a PCA model may require recalibration if an equipment change is made. We conclude that PCA methods may be useful for the diagnosis of subtle deviations from process specification not readily distinguishable to the operator.     

Liquid chromatography–mass spectrometry-based metabolomics for authenticity assessment of fruit juices

High performance liquid chromatography?Cmass spectrometry (HPLC?CMS) technique, employing a hybrid triple quadrupole/linear ion trap (QqQ/LIT) mass analyzer, was used for comprehensive metabolomic fingerprinting of several fruit juices types, prepared from expensive (orange) or relatively low-priced (apple, grapefruit) fruits. Following the automated data mining and pre-treatment step, the suitability of the multivariate HPLC?CMS metabolomic data for authentication, i.e., classification of fruit juice and adulteration detection, was assessed with the use of advanced chemometric tools (principal component analysis, PCA, and linear discrimination analysis, LDA). The LDA classification model, constructed and validated employing a highly variable samples set, was able to reliably detect 15% addition of apple or grapefruit juice to orange juice. In the final stage of this study, high performance liquid chromatography?Cquadrupole?Cquadrupole-time-of-flight mass spectrometry (HPLC?CQqTOFMS) measurements were performed in order to obtain data for identification of pre-selected marker compounds using elemental formula calculation and online databases search.