A comparative study on the three quantitative methods frequently used in proteomics, 2D DIGE (difference gel electrophoresis), cICAT (cleavable isotope-coded affinity tags) and iTRAQ (isobaric tags for relative and absolute quantification), was carried out. DIGE and cICAT are familiar techniques used in gel- and LC-based quantitative proteomics, respectively. iTRAQ is a new LC-based technique which is gradually gaining in popularity. A systematic comparison among these quantitative methods has not been reported. In this study, we conducted well-designed comparisons using a six-protein mixture, a reconstituted protein mixture (BSA spiked into human plasma devoid of six abundant proteins), and complex HCT-116 cell lysates as the samples. All three techniques yielded quantitative results with reasonable accuracy when the six-protein or the reconstituted protein mixture was used. In DIGE, accurate quantification was sometimes compromised due to comigration or partial comigration of proteins. The iTRAQ method is more susceptible to errors in precursor ion isolation, which could be manifested with increasing sample complexity. The quantification sensitivity of each method was estimated by the number of peptides detected for each protein. In this regard, the global-tagging iTRAQ technique was more sensitive than the cysteine-specific cICAT method, which in turn was as sensitive as, if not more sensitive than, the DIGE technique. Protein profiling on HCT-116 and HCT-116 p53 -/- cell lysates displayed limited overlapping among proteins identified by the three methods, suggesting the complementary nature of these methods.

A critical step in protein biomarker discovery is the ability to contrast proteomes, a process referred generally as quantitative proteomics. While stable-isotope labeling (e.g., ICAT, 18O- or 15N-labeling, or AQUA) remains the core technology used in mass spectrometry-based proteomic quantification, increasing efforts have been directed to the label-free approach that relies on direct comparison of peptide peak areas between LC-MS runs. This latter approach is attractive to investigators for its simplicity as well as cost effectiveness. In the present study, the reproducibility and linearity of using a label-free approach to highly complex proteomes were evaluated. Various amounts of proteins from different proteomes were subjected to repeated LC-MS analyses using an ion trap or Fourier transform mass spectrometer. Highly reproducible data were obtained between replicated runs, as evidenced by nearly ideal Pearson's correlation coefficients (for ion's peak areas or retention time) and average peak area ratios. In general, more than 50% and nearly 90% of the peptide ion ratios deviated less than 10% and 20%, respectively, from the average in duplicate runs. In addition, the multiplicity ratios of the amounts of proteins used correlated nicely with the observed averaged ratios of peak areas calculated from detected peptides. Furthermore, the removal of abundant proteins from the samples led to an improvement in reproducibility and linearity. A computer program has been written to automate the processing of data sets from experiments with groups of multiple samples for statistical analysis. Algorithms for outlier-resistant mean estimation and for adjusting statistical significance threshold in multiplicity of testing were incorporated to minimize the rate of false positives. The program was applied to quantify changes in proteomes of parental and p53-deficient HCT-116 human cells and found to yield reproducible results. Overall, this study demonstrates an alternative approach that allows global quantification of differentially expressed proteins in complex proteomes. The utility of this method to biomarker discovery is likely to synergize with future improvements in the detecting sensitivity of mass spectrometers.