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Quantitative proteomics: a review of different methodologies Pier Giorgio Righetti,a,* Natascia Campostrini,a Jennifer Pascali,a Mahmoud Hamdanb and Hubert
Astnerb aDepartment of Agricultural and Industrial Biotechnologies, University of Verona, Strada Le Grazie No. 15, 37134 Verona, Italy. E-mail:
righetti@sci.univr.it bComputational, Analytical & Structural Sciences, GlaxoSmithKline, Via Fleming 4, 37134 Verona, Italy
ABSTRACT:
The present review attempts to
cover the vast array of methods which have appeared in the last few years for performing quantitative proteome analysis. These methods are divided into two classes: those
applicable to conventional two-dimensional map analysis, coupling orthogonally a charge-based step (isoelectric focusing) to a size-based separation [sodium dodecylsulfate
(SDS)-electrophoresis] and those applicable to two-dimensional chromatographic protocols. The first method, although being by and large the most popular approach, can offer
differential display of paired samples with relatively few methods, the oldest one being based on statistical analysis performed on sets of gels via powerful software packages, such
as the MELANIE, PDQuest, Z3 and Z4000, Phoretix and Progenesis. Recent developments comprise analysis performed on a single gel containing mixed samples differentially
labeled, either with fluorophors (Cy3 and Cy5) or with d0 /d3 acrylamide. Conversely, chromatographic approaches, which mostly rely on analysis not of
intact proteins but of their tryptic digests, offer a panoply of differential labeling protocols, most of which rely on stable isotope tagging. Essentially, all possible reactions have been
described, such as those involving Lys, Asp, Glu, Cys residues, as well as a number of methods exploiting differential derivatization of amine and carboxyl groups generated
during proteolysis. All such methods are described and evaluated.
Keywords:
quantitative proteomics, differential in-gel electrophoresis (DIGE), isotope-coded affinity tags (ICAT), global
internal standard strategy (GIST), mass-coded tags
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