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Comparison of a Label-Free Quantitative Proteomic Method Based on Peptide Ion Current Area to the Isotope Coded Affinity Tag Method

Authors :
Young Ah Goo
Scott A. Shaffer
Byron Gallis
Soyoung Ryu
Dragan Radulovic
David R. Goodlett
Source :
Cancer Informatics, Vol 6, Pp 243-255 (2008)
Publication Year :
2008
Publisher :
SAGE Publishing, 2008.

Abstract

Recently, several research groups have published methods for the determination of proteomic expression profiling by mass spectrometry without the use of exogenously added stable isotopes or stable isotope dilution theory. These so-called label-free, methods have the advantage of allowing data on each sample to be acquired independently from all other samples to which they can later be compared in silico for the purpose of measuring changes in protein expression between various biological states. We developed label free software based on direct measurement of peptide ion current area (PICA) and compared it to two other methods, a simpler label free method known as spectral counting and the isotope coded affinity tag (ICAT) method. Data analysis by these methods of a standard mixture containing proteins of known, but varying, concentrations showed that they performed similarly with a mean squared error of 0.09. Additionally, complex bacterial protein mixtures spiked with known concentrations of standard proteins were analyzed using the PICA label-free method. These results indicated that the PICA method detected all levels of standard spiked proteins at the 90% confidence level in this complex biological sample. This finding confirms that label-free methods, based on direct measurement of the area under a single ion current trace, performed as well as the standard ICAT method. Given the fact that the label-free methods provide ease in experimental design well beyond pair-wise comparison, label-free methods such as our PICA method are well suited for proteomic expression profiling of large numbers of samples as is needed in clinical analysis.

Details

Language :
English
ISSN :
11769351
Volume :
6
Database :
Directory of Open Access Journals
Journal :
Cancer Informatics
Publication Type :
Academic Journal
Accession number :
edsdoj.095a4cee0e394724a71ad6693505288f
Document Type :
article