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Raman spectroscopy is a label-free, real-time diagnostic tool that shows great promise in identifying cell differences. We have evaluated the discriminatory power of Raman spectroscopy using a unique model system consisting of two isogenic cancer cell lines derived from the MDA-MB-435 cell line. The two cell lines are equally tumorigenic in mice, but while M-4A4 gives rise to metastasis, NM-2C5 only disseminates single cells that remain dormant in distant organs. Previous comparative proteomic and transcriptomic analyses of the two cell lines have shown that they differ only in the expression level of a few proteins and genes. Raman maps were recorded of single cells after fixation and drying using 785 nm laser excitation. K-means clustering reduced the amount of data from each cell and improved the signal-to-noise ratio of cluster-averaged spectra. Spectra representing the nucleus were discarded as they showed much smaller differences between the two cell lines compared to cytoplasm spectra. Partial least squares-discriminant analysis (PLS-DA) was applied to distinguish the two cell lines. A cross-validated PLS-DA resulted in 92% correctly classified samples. Spectral differences were assigned to a higher unsaturated fatty acid content in the metastatic vs nonmetastatic cell line. Our study demonstrates the unique ability of Raman spectroscopy to distinguish minute differences at the subcellular level and yield new biological information. Our study is the first to demonstrate the association between polyunsaturated fatty acid content and metastatic ability in this unique cell model system and is in agreement with previous studies on this topic. 10.1021/ac902717d

A sensor array containing 9 cross-reactive sensing fluorescent elements with different affinity and selectivity to 10 metal cations ([Ca.sup.2+], [Mg.sup.2+], [Cd.sup.2+], [Hg.sup.2+], [Co.sup.2+], [Zn.sup.2+], [Cu.sup.2+], [Ni.sup.2+], [Al.sup.3+], [Ga.sup.3+]) is described. The discriminatory capacity of the array was tested at different ranges of pH and at different cation concentrations using linear discriminant analysis (LDA). Qualitative identification of cations can be determined with over 96% of accuracy in a concentration range covering 3 orders of a magnitude (5-5000 [micro]M). Quantitative analysis can be achieved with over 90% accuracy in the concentration range between 10 and 5000 [micro]M. The array performance was also tested in identification of nine different mineral water brands utilizing their various electrolyte compositions and their [Ca.sup.2+], [Mg.sup.2+], and [Zn.sup.2+] levels. LDA cross-validation routine shows 100% correct classification for all trials. Preliminary results suggest that similar arrays could be used in testing of the consistency of the purification and manufacturing process of purified and mineral waters.

In this study, the orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to assess the in situ chemical composition of two different cell types in mouse liver samples, hepatocytes and erythrocytes. High spatial resolution FT-IR micro-spectroscopy equipped with a focal plan array (FPA) detector is capable of simultaneously recording over 4000 spectra from 64 x 64 pixels with a maximum spatial resolution of about 5 [micro]m x 5 [micro]m, which allows for the differentiation of individual cells. The main benefit with OPLS-DA lies in the ability to separate predictive variation (between cell type) from variation that is uncorrelated to cell type in order to facilitate understanding of different sources of variation. OPLS-DA was able to differentiate between chemical properties and physical properties (e.g., edge effects). OPLS-DA model interpretation of the chemical features that separated the two cell types clearly highlighted proteins and lipids/bile acids. The modeled variation that was uncorrelated to cell type made up a larger portion of the total variation and displayed strong variability in the amide I region. This could be traced back to a gradient in the high intensity (high-density) areas vs the low intensity areas (close to empty areas) that as a result of normalization had an adverse effect on FT-IR spectral profiles. This highlights that OPLS-DA provides an effective solution to identify different sources of variability, both predictive and uncorrelated, and also facilitates understanding of any sampling, experimental, or preprocessing issues.

We previously reported a method for the automated (objective) selection of a PARAFAC model having an appropriate number of factors for mathematical resolution of signal from a target analyte in GC x GC-TOFMS data (i.e., for an analysis in which the identity of the analyte is known a priori). While the previous target method has been successfully applied in several studies, the target method requires that the identity of the analyte be known. Also, multiple applications of the target method are required in cases where several analytes of interest are present in a single subsection of the chromatogram. Thus, having to know the analyte identity a priori restricts the applicability of the automated implementation of PARAFAC. The method presented in this report generalizes the previous method to allow analysis of one or more nontarget analyte signals in a subsection of a GC x GC-TOFMS chromatogram (i.e., for analyses when identities of analyte and interferences are not known a priori), thereby addressing and overcoming the limitations of the target method. Herein, we put the nontarget analyte PARAFAC method into theoretical context and illustrate the mechanics of the method using simulated data. We use real experimental GC x GC-TOFMS data to demonstrate the broad applicability of the method, with various analysis situations selected to illustrate challenging chemical analysis scenarios.

The majority of works in metabolomics employ approaches based on principal components analysis (PCA) and partial least-squares, primarily to determine whether samples rail within large groups. However, analytical chemists rarely tackle the problem of individual fingerprinting, and in order to do this effectively, it is necessary to study a large number of small groups rather than a small number of large groups and different approaches are required, as described in this paper. Furthermore, many metabolomic studies on mammals and humans involve analyzing compounds (of peaks) that are present in only a certain portion of samples, and conventional approaches of PCA do not cope well with sparse matrices where there may be many Os. There is, however, a large number of qualitative similarity measures available for this purpose that can be exploited via principal coordinates analysis (PCO). It can be shown that PCA scores are a specific case of PCO scores, using a quantitative similarity measure. A large-scale study of human sweat consisting of nearly 1000 gas chromatography/mass spectrometry analyses from the sweat of an isolated population of 200 individuals in Carinthia (Southern Austria) sampled once per fortnight over 10 weeks was employed in this study and grouped into families. The first step was to produce a peak table requiring peak detection, alignment, and integration. Peaks were reduced from 5080 to 373 that occurred in at least i individual over 4 out of 5 fortnights. Both qualitative (presence/absence) and quantitative (equivalent to PCA) similarity measures can be computed. PCO and the Kolomorogov-Smirnoff (KS) rank test are applied to these similarity matrices. It is shown that for this data set there is a reproducible individual fingerprint, which is best represented using the qualitative similarity measure as assessed both by the Hotelling [t.sup.2] statistic as applied to PCO scores and the probabilities associated with the KS rank test.

Procedures were developed for the estimation of orthogonality in two-dimensional (2D) separations. The parameters evaluated include peak spreading angle, retention correlation, and practical peak capacity. Solute retention parameters, such as retention times and capacity factors on both dimensions, were used to establish a correlation matrix, from which a peak spreading angle matrix was calculated using a geometric approach to factor analysis. The orthogonality is defined by the correlation matrix with correlation coefficients that vary from 0 (orthogonal) to 1 (perfect correlation). Equations were derived for the calculation of practical peak capacity in 2D separations. The calculations are based on the peak capacities obtained on each dimension and the peak spreading angle in an orthogonal, 2D retention space. The equations and the procedures can be used to evaluate the performance of a comprehensive 2D separation. Using experimental data from a 2D GC separation, it is demonstrated that the equations are very useful for the comparison, evaluation, and optimization of 2D separations.

A simulated data matrix was used to investigate the effects of white noise upon the interpretation of an abstract factor model. An a priori knowledge of the relationships among variables that exist from theory was assumed in the study. Factor loadings along eigenvalues of extracted principal components were analyzed in relation to aqueous chemical equilibria. The methodwas found to be applicable to any noisy data matrix involving abstract factor analysis.

In HPLC-UV the generalized rank annihilation factor analysis (GRAFA), iterative target transformation factor analysis (IT-TFA), and residual bilinearization (RBL) can be used to quantify an analyte of interest in the presence of one or more unknown compounds. Until now simulated data that were used for evaluating these techniques always had a perfect bilinear structure. In this paper GRAFA, ITTFA, and RBL are systematically evaluated and compared by simulating more realistic peak systems, by including model deviations that are present in real data. The effect of the presence of interferents, the peak resolution, the relative peak heights, the chromatographic reproducibility, and the background absorbances on the quality of the obtained solutions is demonstrated. The ITTFA algorithm has been modified in order to improve quantitative results.

Bilinear data matrices may be resolved into abstract factors by factor analysis. The underlying chemical processes that generated the data may be deduced from the abstract factors by hard (model fitting) or soft (model-free) analyses. We propose a novel approach that combines the advantages of both hard and soft methods, in that only a few parameters have to be fitted, but the assumptions made about the system are very general and common to a range of possible models: The true chemical factors span the same space as the abstract factors and may be mapped onto the abstract factors by a transformation matrix T, since they are a linear combination of the abstract factors. The difference between the true factors and any other linear combination of the abstract factors is that the true factors conform to known chemical constraints (for instance, nonnegativity of absorbance spectra or monomodality of chromatographic peaks). Thus, by excluding linear combinations of the abstract factors that are not physically possible (assuming a unique solution), we can find the true chemical factors. This is achieved by passing the elements of a transformation matrix to a nonlinear optimization routine, to find the best estimate oft that fits the criteria. The optimization routine usually converges to the correct minimum with random starting parameters, but more difficult problems require starting parameters based on some other method, for instance EFA. We call the new method resolving factor analysis (RFA). The use of RFA is demonstrated with computer-generated kinetic and chromatographic data and with real chromatographic (HPLC) data. RFA produces correct solutions with data sets that are refractory to other methods, for instance, data with an embedded nonconstant baseline.