Biomedical subjects

Nicola Marchetti

Publications and source records attributed to Nicola Marchetti.

7 recordsLinked to original sources

PubMed2006

Decoding 2D-PAGE complex maps: relevance to proteomics.

This review describes two mathematical approaches useful for decoding the complex signal of 2D-PAGE maps of protein mixtures. These methods are helpful for interpreting the large amount of data of each 2D-PAGE map by extracting all the analytical information hidden therein by spot overlapping. Here the basic theory and application to 2D-PAGE maps are reviewed: the means for extracting information from the experimental data and their relevance to proteomics are discussed. One method is based on the quantitative theory of statistical model of peak overlapping (SMO) using the spot experimental data (intensity and spatial coordinates). The second method is based on the study of the 2D-autocovariance function (2D-ACVF) computed on the experimental digitised map. They are two independent methods that are able to extract equal and complementary information from the 2D-PAGE map. Both methods permit to obtain fundamental information on the sample complexity and the separation performance and to single out ordered patterns present in spot positions: the availability of two independent procedures to compute the same separation parameters is a powerful tool to estimate the reliability of the obtained results. The SMO procedure is an unique tool to quantitatively estimate the degree of spot overlapping present in the map, while the 2D-ACVF method is particularly powerful in simply singling out the presence of order in the spot position from the complexity of the whole 2D map, i.e., spot trains. The procedures were validated by extensive numerical computation on computer-generated maps describing experimental 2D-PAGE gels of protein mixtures. Their applicability to real samples was tested on reference maps obtained from literature sources. The review describes the most relevant information for proteomics: sample complexity, separation performance, overlapping extent, identification of spot trains related to post-translational modifications (PTMs).

Electrophoresis, Gel, Two-Dimensional↗

PubMed2005

Modeling of overloaded gradient elution of nociceptin/orphanin FQ in reversed-phase liquid chromatography.

The Reversed-phase (RP) gradient elution chromatography of nociceptin/orphanin FQ (N/OFQ), a neuropeptide with many biological effects, has been modeled under linear and non-linear conditions. In order to do this, the chromatographic behavior has been studied under both linear and nonliner conditions under isocratic mode at different mobile phase compositions--ranging from 16 to 19% (v/v) acetonitrile (ACN) in aqueous trifluoracetic acid (TFA) 0.1% (v/v)-on a C-8 column. Although the range of mobile phase compositions investigated was quite narrow, the retention factor of this relatively small polypeptide (N/OFQ is a heptadecapeptide) has been found to change by more than 400%. In these conditions, gradient operation resulted thus to be the optimum approach for non-linear elution. As the available amount of N/OFQ was extremely reduced (only a few milligrams), the adsorption isotherms of the peptide, at the different mobile phase compositions examined, have been measured through the so-called inverse method (IM) on a 5 cm long column. The adsorption data at different mobile phase compositions have been fitted to several models of adsorption. The dependence of the isotherm parameters on the mobile phase composition was modeled by using the linear solvent strength (LSS) model and a generalized Langmuir isotherm that includes the mobile phase composition dependence. The overloaded gradient separation of N/OFQ has been modeled by numerically solving the equilibrium-dispersive (ED) model of chromatography under a selected gradient elution mode, on the basis of the previously determined generalized Langmuir isotherm. The agreement between theoretical calculations and experimental overloaded band profiles appeared reasonably accurate.

Chromatography, Liquid↗

PubMed2005

Separation of peptides from myoglobin enzymatic digests by RPLC. Influence of the mobile-phase composition and the pressure on the retention and separation.

The influence of the mobile-phase composition and the pressure on the chromatographic separation of the peptides from the enzymatic digest of myoglobin was studied under linear conditions. The retention behavior of these tryptic peptides was measured under isocratic conditions with different mobile-phase compositions, ranging from 9 to 28% (v/v) acetonitrile in 0.1% (v/v) aqueous trifluoroacetic acid. The effect of the pressure was studied by analyzing the separation of the tryptic peptides under different average column pressures between 14 and 220 bar, at 13, 20, and 26% (v/v) acetonitrile. The differences between the partial molar volumes of these peptides in the stationary and mobile phases were derived from these results. All the measurements were performed on a 10-cm-long C18-bonded, end-capped monolithic column. The results obtained illustrate the highly complicated behavior of the complex peptide mixtures afforded by tryptic digestion. The capacity factors of the analyzed peptides do not depend linearly on the acetonitrile concentration but follow exactly a quadratic relationship. The adsorption changes of partial molar volumes are in good agreement with other literature data. The consequences of the influence of the average column pressure (hence of the flow rate) on the column phase ratio and on the retention factors of the peptides are discussed. The retention pattern of the complex mixture is affected by both the mobile-phase composition and the pressure, and the resolution of certain peptide pairs is so much affected by the pressure that inversions in the elution order of some pairs are observed.

Adsorption↗

PubMed2005

Decoding two-dimensional polyacrylamide gel electrophoresis complex maps by autocovariance function: a simplified approach useful for proteomics.

This paper describes a mathematical approach applied for decoding the complex signal of two-dimensional polyacrylamide gel electrophoresis maps of protein mixtures. The method is helpful in extracting analytical information since separation of all the proteins present in the sample is still far from being achieved and co-migrating proteins are generally present in the same spot. The simplified method described is based on the study of the 2-D autocovariance function (2D-ACVF) computed on an experimental digitized map. The first part of the 2D-ACVF allows for the estimation of the number of proteins present in the sample (2D-ACVF computed at the origin) and of the separation performance (mean spot size). Moreover, the 2D-ACVF plot is a powerful tool in identifying order in the spot position, and singling it out from the complex separation pattern. This method was validated on synthetic maps obtained by computer simulation to describe 2-D PAGE real maps and reference maps retrieved from the SWISS-2DPAGE database. The results obtained are discussed by focusing on specific information relevant in proteomics: sample complexity, separation performance, and identification of spot trains related to post-translational modifications.

Analysis of Variance↗

PubMed2004

Decoding two-dimensional complex multicomponent separations by autocovariance function.

A new method for decoding two-dimensional (2D) multicomponent separations based on the use of the 2D Autocovariance function (2D-ACVF) has been developed. Theoretical models of single component (SC) spot distributions in 2D separations, both random and structured, are developed as the basis for a nonlinear estimation of both sample and separation system parameters from experimental 2D separations. The number of SCs, the average spot size, the spot capacity, and the saturation factor can be evaluated in the case of random SC spot patterns. The procedure was validated by extensive numerical simulation under conditions close to those usually found in GC x GC or 2D-polyacrylamid gel electrophoresis of proteins. The worse precision degree was no greater than 10% in the case of maximum spot density. This imprecision was fully accounted for, and it seems acceptable owing to the intrinsic statistical character of the estimation method. Structured multicomponent 2D separations, where SCs are linked by linear relationships, give rise to specific structured patterns in 2D-ACVF plots from which the parameters (phase and frequency) of the structured SC sequences can be evaluated: the study of 2D-ACVF makes it possible to decode multicomponent 2D separation, that is, to determine the number, relative abundance, and structural similarities of the single components. Pertinent expressions of the theoretical 2D-ACVF were derived for simple cases, and a procedure for decoding cases of structured 2D separations was developed and applied. It was shown that 2D separations containing both random and structured patterns of SC spots give rise to 2D-EACVF, which is the superimposition of the two component parts. This feature allows one, in principle, to decode the two components. The relevance of these results for Giddings sample dimensionality and separation dimensionality and their effective experimental evaluation is discussed.

Journal Article↗

PubMed2003

Spot overlapping in two-dimensional polyacrylamide gel electrophoresis maps: relevance to proteomics.

Proteomics requires a large-scale, simultaneous separation of proteins from a mixture, assessment of the relative abundance of these molecules, and identification and characterization of each component. In 2-D PAGE separations, the best method of choice for protein analysis, separation of all the proteins present in the sample is still far to be achieved and comigrating proteins in the same spot are in general present. A statistical estimation of the degree of spot overlapping present in a 2-D PAGE separation is here described: for different conditions of spot overcrowding in the map, the degree of overlapping can be quantified in terms of purity degree of each spot or percentage of proteins that will appear in the map as a single spot. A computer simulation approach is described: it is based on the protein separation pattern present in the experimental maps. The results thus obtained are compared to a theoretical model (statistical degree of peak overlapping model) based on random spot position. The described procedures were applied to an experimental reference map of human plasma. The severity of spot overlapping in 2-D PAGE maps is estimated and the influence of different experimental conditions (strip dimension, detector system performance, pI range) is discussed. These informations are useful to quantitatively estimate the degree of error associated with identification and quantitation of each protein and to set-up experimental conditions which will increase resolution and greatly decrease the probability of spot overlapping.

Computer Simulation↗

PubMed2002

Spot overlapping in two-dimensional polyacrylamide gel electrophoresis separations: a statistical study of complex protein maps.

A statistical approach able to extract the information contained in a two-dimenisional polyacrylamide gel electrophoresis (2-D PAGE) separation is here reported. The method is based on the quantitative theory of peak overlapping, a procedure previously developed by the authors and here extended to 2-D separations. The whole map is divided into many strips in order to obtain 1-D separations on which the statistic procedure is applied: the developed algorithms, on the basis of spot experimental data (intensity and spatial coordinates) permit to estimate the intrinsic number of components and to single out the specific order present in spot positions. The procedure was validated on computer-simulated maps. Its applicability to real samples was tested on maps obtained from literature sources. The following important information on protein mixtures can be extracted: (i) the number of proteins can be accurately estimated, on the basis of the spatial coordinates and intensities of spots detected in the 2-D PAGE map; (ii) the model describing distribution of interdistance between adjacent spots can be identified in both the separation dimensions; (iii) the presence of repeated interdistances in spot positions in the maps can be easily singled out: these regularities suggest specific protein modifications.

Computer Simulation↗