Biomedical subjects

Nicola Marchetti

Publications and source records attributed to Nicola Marchetti.

3 recordsLinked to original sources

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↗