Joaquim Jaumot

Multivariate Curve Resolution (MCR). Solving the mixture analysis problem

This article is a tutorial that focuses on the main aspects to be considered when applying Multivariate Curve Resolution to analyze multicomponent systems, particularly when the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm is used. These aspects include general MCR comments on the potential fields of application and construction of data structures and details linked to each of the steps in the application workflow of the MCR-ALS algorithm (e.g., selection of initial estimates, choice and application of constraints, quality parameters of models and assessment of ambiguity,…). Two examples with downloadable data sets are shown for orientation on the practical use of this methodology.

Vibrational spectroscopic image analysis of biological material using multivariate curve resolution–alternating least squares (MCR-ALS)

Raman and Fourier transform IR (FTIR) microspectroscopic images of biological material (tissue sections) contain detailed information about their chemical composition. The challenge lies in identifying changes in chemical composition, as well as locating and assigning these changes to different conditions (pathology, anatomy, environmental or genetic factors). Multivariate data analysis techniques are ideal for decrypting such information from the data. This protocol provides a user-friendly pipeline and graphical user interface (GUI) for data pre-processing and unmixing of pixel spectra into their contributing pure components by multivariate curve resolution–alternating least squares (MCR-ALS) analysis. The analysis considers the full spectral profile in order to identify the chemical compounds and to visualize their distribution across the sample to categorize chemically distinct areas. Results are rapidly achieved (usually <30–60 min per image), and they are easy to interpret and evaluate both in terms of chemistry and biology, making the method generally more powerful than principal component analysis (PCA) or heat maps of single-band intensities. In addition, chemical and biological evaluation of the results by means of reference matching and segmentation maps (based on k-means clustering) is possible.

Experimental Methods for Studying the Interactions between G-Quadruplex Structures and Ligands

The present paper reviews the recent advances in and applications of experimental techniques used to study interactions between G-quadruplex structures and ligands that are potentially of pharmaceutical interest. Several instrumental techniques are used to study such interactions. The application of spectroscopic techniques such as molecular absorption, circular dichroism, molecular fluorescence, mass spectrometry and nuclear magnetic resonance are reviewed and we discuss the type of information (qualitative or quantitative) that can be obtained from the use of each technique. Additionally, the application of complementary techniques such as surface plasmon resonance, isothermal titration calorimetry and different methods based on biochemistry is considered. For each technique, the main applications are presented and they are classified according to the family of the ligand and the type of G-quadruplex forming sequence (human telomeric or promoter region of oncogenes) considered.

Resolution of a structural competition involving dimeric G-quadruplex and its C-rich complementary strand

The resolution of the dimeric intermolecular G-quadruplex/duplex competition of the telomeric DNA sequence 5′-TAG GGT TAG GGT-3′ and of its complementary 5′ ACC CTA ACC CTA-3′ is reported. To achieve this goal, melting experiments of both sequences and of the mixtures of these sequences were monitored by molecular absorption, molecular fluorescence and circular dichroism spectroscopies. Molecular fluorescence measurements were carried out using molecular beacons technology, in which the 5′-TAG GGT TAG GGT-3′ sequence was labelled with a fluorophore and a quencher at the ends of the strand. Mathematical analysis of experimental spectroscopic data was performed by means of multivariate curve resolution, allowing the calculation of concentration profiles and pure spectra of all resolved structures (dimeric antiparallel and parallel G-quadruplexes, Watson–Crick duplex and single strands) present in solution. Our results show that parallel G-quadruplex is more stable than antiparallel G-quadruplex. When the complementary C-rich strand is present, a mixture of both G-quadruplex structures and Watson–Crick duplex is observed, the duplex being the major species. In addition to melting temperatures, equilibrium constants for the parallel/antiparallel G-quadruplex equilibrium and for the G-quadruplex/duplex equilibrium were determined from the concentration profiles.

pH-Modulated Watson-Crick duplex-quadruplex equilibria of guanine-rich and cytosine-rich DNA sequences 140 base pairs upstream of the c-kit transcription initiation site.

Guanine-rich regions of DNA are sequences capable of forming G-quadruplex structures. The formation of a G-quadruplex structure in a region 140 base pairs (bp) upstream of the c-kit transcription initiation site was recently proposed (Fernando et al., Biochemistry, 2006, 45, 7854). In the present study, the acid-base equilibria and the thermally induced unfolding of the structures formed by a guanine-rich region and by its complementary cytosine-rich strand in c-kit were studied by means of circular dichroism and molecular absorption spectroscopies. In addition, competition between the Watson-Crick duplex and the isolated structures was studied as a function of pH value and temperature. Multivariate data analysis methods based on both hard and soft modeling were used to allow accurate quantification of the various acid-base species present in the mixtures. Results showed that the G-quadruplex and i-motif coexist with the Watson-Crick duplex over the pH range from 3.0 to 6.5, approximately, under the experimental conditions tested in this study. At pH 7.0, the duplex is practically the only species present.

Targeting the G-quadruplex-forming region near the P1 promoter in the human BCL-2 gene with the cationic porphyrin TMPyP4 and with the complementary C-rich strand.

The B-cell lymphoma-2 (bcl-2) gene contains a region that has been implicated in the regulation of bcl-2 gene expression. This region can form G-quadruplex structures in solution [J.X. Dai, T.S. Dexheimer, D. Chen, M. Carver, A. Ambrus, R.A. Jones, D.Z. Yang, An intramolecular G-quadruplex structure with mixed parallel/antiparallel G-strands formed in the human BCL-2 promoter region in solution, J. Am. Chem. Soc. 128 (2006) 1096-1098.]. Here, we examined the acid-base and conformational equilibria of this G-quadruplex-forming region (BCL2G), as well as its interaction with both the porphyrin TMPyP4 and with the complementary C-rich strand. We used molecular absorption and circular dichroism techniques, in tandem with multivariate analysis tools. The results revealed the formation of an interaction complex BCL2G:TMPyP4 with a stoichiometry of 1:2 and an equilibrium constant equal to 5.0 (+/-2.3) x 10(13) M(-2). Addition of the complementary C-rich strand to BCL2G induces the predominant formation of the Watson-Crick double-helix with an equilibrium constant equal to 10(7.7) M(-1) (at pH 7.1). Finally, the pH-induced formation of quadruplex structures from the Watson-Crick double-helix is characterized.

Lipidomic data analysis: Tutorial, practical guidelines and applications

Lipids are a broad group of biomolecules involved in diverse critical biological roles such as cellular membrane structure, energy storage or cell signaling and homeostasis. Lipidomics is the -omics science that pursues the comprehensive characterization of lipids present in a biological sample. Different analytical strategies such as nuclear magnetic resonance or mass spectrometry with or without previous chromatographic separation are currently used to analyze the lipid composition of a sample. However, current analytical techniques provide a vast amount of data which complicates the interpretation of results without the use of advanced data analysis tools. The choice of the appropriate chemometric method is essential to extract valuable information from the crude data as well as to interpret the lipidomic results in the biological context studied. The present work summarizes the diverse methods of analysis than can be used to study lipidomic data, from statistical inference tests to more sophisticated multivariate analysis methods. In addition to the theoretical description of the methods, application of various methods to a particular lipidomic data set as well as literature examples are presented.

Classification of nucleic acids structures by means of the chemometric analysis of circular dichroism spectra

DNA can adopt structures in solution apart from the well-known Watson-Crick double helix, ranging from disordered single strands to high-order structures such as triplexes or quadruplexes. Moreover, different topologies can be adopted depending on the polarity of the DNA strands. The elucidation of the structure and topology adopted by a DNA sequence is usually carried out by means of spectroscopic techniques, such as circular dichroism. In this work, the ability of several chemometric methods to efficiently classify DNA structures from circular dichroism data is tested. With this objective in mind, a dataset including 50 experimental spectra corresponding to different DNA structures (random coil, duplex, hairpin, reversed and normal triplex, parallel and antiparallel G-quadruplex, and i-motif) has been analyzed by means of unsupervised hierarchical clustering analysis, principal component analysis and partial least squares discriminant analysis. The results have shown than those methods allow efficiently the classification of DNA structures from circular dichroism spectra. Moreover, these classification methods also provided the most characteristic wavelengths used in the classification procedures.

Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies

AbstractThe comprehensive analysis of untargeted metabolomics data acquired using LC-MS is still a major challenge. Different data analysis tools have been developed in recent years such as XCMS (various forms (X) of chromatography mass spectrometry) and multivariate curve resolution alternating least squares (MCR-ALS)-based strategies. In this work, metabolites extracted from rice tissues cultivated in an environmental test chamber were subjected to untargeted full-scan LC-MS analysis, and the obtained data sets were analyzed using XCMS and MCR-ALS. These approaches were compared in the investigation of the effects of copper and cadmium exposure on rice tissue (roots and aerial parts) samples. Both methods give, as a result of their application, the whole set of resolved elution and spectra profiles of the extracted metabolites in control and metal-treated samples, as well as the values of their corresponding chromatographic peak areas. The effects caused by the two considered metals on rice samples were assessed by further chemometric analysis and statistical evaluation of these peak area values. Results showed that there was a statistically significant interaction between the considered factors (type of metal of treatment and tissue). Also, the discrimination of the samples according to both factors was possible. A tentative identification of the most discriminant metabolites (biomarkers) was assessed. It is finally concluded that both XCMS- and MCR-ALS-based strategies provided similar results in all the considered cases despite the completely different approaches used by these two methods in the chromatographic peak resolution and detection strategies. Finally, advantages and disadvantages of using these two methods are discussed. Graphical AbstractSummary of the workflow for untargeted metabolomics using the compared approaches

Chemical equilibria studies using multivariate analysis methods

AbstractChemical multiequilibria systems can be monitored efficiently with the aid of spectroscopic techniques. Both hard- and soft-modeling are effective and powerful tools to extract chemical information from spectroscopic data. Recently, hybrid approaches that combine the flexibility of soft-modeling with the precise solutions provided by hard-modeling have been proposed. Here, we tested the performance of these three chemometric approaches for the analysis of several simulated data sets. In addition, experimental data recorded during the study of the acid–base equilibria of two DNA structures (G-quadruplex and i-motif) corresponding to two short sequences of the k-ras oncogene were studied. Finally, we also analyzed the interaction of the two DNA sequences with the model ligand TMPyP4. The results obtained from the analysis of these data sets may be useful to determine the most appropriate use of each approach. Whenever the presence of optically active interferences or unknown drifts can be neglected and a chemical model can easily be proposed and fitted, the hard-modeling method shows the best performance. If any of these conditions is not fulfilled, a hybrid-modeling approach may be a better option because all the contributions (chemical and unknown) can be modeled and the ambiguities inherent to soft-modeling methods show minor effects. FigureSchematic representation of the application of multivariate modeling methods to the analysis of spectroscopic data