Biomedical subjects

Maria Chiara Pietrogrande

Publications and source records attributed to Maria Chiara Pietrogrande.

9 recordsLinked to original sources

PubMed2006

Identification and quantification of homologous series of compound in complex mixtures: autocovariance study of GC/MS chromatograms.

The paper describes a method for determining homologous classes of compounds in a multicomponent complex chromatogram obtained under programming elution conditions. The method is based on the computation of the autocovariance function of the experimental chromatogram (EACVF). The EACVF plot, if properly interpreted, can be regarded as a "class chromatogram" i.e., a virtual chromatogram formed by peaks whose positions and heights allow identification and quantification of the different homologous series, even if they are embedded in a random complex chromatogram. Theoretical models were developed to describe complex chromatograms displaying random retention pattern, ordered sequences or a combination of them. On the basis of theoretical autocovariance function, the properties of the chromatogram can be experimentally evaluated, under well-defined conditions: in particular, the two components of the chromatogram, ordered and random, can be identified. Moreover, the total number of single components (SCs) and the separated number of the SCs belonging to the random and ordered components can be determined, when the two components display the same concentration. If the mixture contains several homologous series with common frequency and different phase values, the number and identity of the different homologous series as well as the number of SCs belonging to each of them can be evaluated. Moreover, the power of the EACVF method can be magnified by applying it to the single ion monitoring (SIM) signals to selectively detect specific compound classes in order to identify the different homologous series. By this way, a full "decoding" of the complex multicomponent chromatogram is achieved. The method was validated on synthetic mixtures containing known amount of SCs belonging to homologous series of hydrocarbon, alcohols, ketones, and aromatic compounds in addition to other not structurally related SCs. The method was applied to both the total ion monitoring (TIC) and the SIM signals, to describe step by step the essence of the procedure. Moreover, the systematic use of both SIM and TIC can simplify the decoding procedure of complex chromatograms by singling out only specific compound classes or by confirming the identification of the different homologous series. The method was further applied to a sample containing unknown number of compounds and homologous series (a petroleum benzin, bp 140-160 degrees C): the results obtained were meaningful in terms of both the identified number of components and identified homologous series.

Journal Article↗

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

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↗

PubMed2005

Spot overlapping in two-dimensional maps: a serious problem ignored for much too long.

In the analysis of a neuroblastoma xenograft implanted in mice using two-dimensional maps, some 85 proteins were found to be up- or down-regulated (out of a total of 264 detected by a medium-sensitivity colloidal Coomassie stain). When these spots were eluted and analysed by mass spectrometry in a quadrupole time of flight mass spectrometer, a number of spots were found to be envelopes of different polypeptide chains. Out of a total of 74 proteins identified, 52 (71%) were found to be singlets, 14 (19%) were doublets, 6 (8%) were triplets, 1 was a quadruplet and 1 a quintuplet. Analysis of the DeltapI and DeltaMr of all species contained in a single gel segment eluted helped point out potential errors in protein identification. This was a unique case, in that very minute bioptic sample loads were applied to the gel. In normal cases, where sample loads of ca. 1 mg of total protein are applied and typically at least 1000 spots are visualised, the singlets will be the minority, rarely exceeding 30% of all spots analysed. The experimental data on the abundance of overlapping spots were in excellent agreement with theoretical data calculated on the basis of the statistical theory of spot overlapping, originally proposed by Davis and further developed by some of the authors. Ways and means for minimizing spot overlap and visualising a greater number of spots in a two-dimensional map are discussed.

Adrenal Gland Neoplasms↗

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

Decoding of complex isothermal chromatograms recovered from space missions. Identification of molecular structure.

A chemometric approach, based on the study of the autocovariance function, is described to study isothermal GC chromatograms of multicomponent mixtures: isothermal GC analysis is the method of choice in space missions since it is, to date, the only method compatible with flight constraints. Isothermal GC chromatograms look inhomogeneous and disordered with peak density decreasing at higher retention times: a time axis transformation is proposed to make retention an homogeneous process so that CH2 addition in terms of an homologous series yields a constant retention increment. The time axis is transformed into a new scale based on the retention times of n-alkanes, as they are the basis of the universal Kovats indices procedure. The order introduced into the chromatogram by retention time linearization can be simply singled out by the experimental autocorrelation function (EACF) plot: if constant inter-distances are repeated in different regions of the chromatogram, well-shaped peaks are evident in the EACF plot. By comparison, with a standard mixture it is possible to identify peaks diagnostic of specific molecular structures: study of the EACF plot provides information on sample chemical composition. The procedure was applied to standard mixtures containing compounds representative of the planetary atmospheres that will be investigated in the near future: in particular, those related to Titan's atmosphere (Cassini-Huygens mission) and cometary's nucleus (Rosetta mission). The employed experimental conditions simulated those applied to GC instruments installed on space probes and landers in space missions. The method was applied to two specific investigations related to space research, i.e., a comparison of retention selectivity of different GC columns and identification of the chemical composition of an unknown mixture.

Chromatography, Gas↗

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↗

PubMedDate not supplied

GC-MS in space research: decoding complex isothermal chromatograms recovered from space missions.

An analytical procedure is described to study GC-MS isothermal chromatograms simulating those recovered from space missions: in fact GC plays a predominant role in space missions devoted to characterizing the chemical composition of extra-terrestrial atmospheres. SIM (selected ion monitoring) detection was used for monitoring selected chemical classes: a simplified chromatogram can be obtained giving information on the chemical composition of the complex mixture. Since only isothermal GC chromatograms are allowed by flight constraints, a time axis transformation is required to make them homogeneous: i.e., constant retention increments for CH2 additions in terms of a homologous series. The order in the linearized chromatogram can be simply singled out with a chemometric approach based on the study of the Autocovariance Function (ACVF) computed on the digitized chromatogram: the plot of the experimental autocorrelation function (EACF) shows well-shaped peaks if constant interdistances are repeated in different regions of the chromatogram. The method was applied to standard mixtures representative of planetary atmospheres--hydrocarbons, nitriles and oxygenated compounds with between 3 and 12 carbon atoms--analyzed in flight simulating conditions. The coupling of the selectivity of SIM detection with the interpretation power of the EACF procedure proves to be a powerful tool for interpreting data recovered from space missions: the chemical composition of the mixture can be identified by handling the raw SIM chromatograms.

Automation↗