Abstract
In many studies, particularly in the field of systems biology, it is essential that identical protein sets are precisely quantified in multiple samples such as those representing differentially perturbed cell states. The high degree of reproducibility required for such experiments has not been achieved by classical mass spectrometry-based proteomics methods. In this study we describe the implementation of a targeted quantitative approach by which predetermined protein sets are first identified and subsequently quantified at high sensitivity reliably in multiple samples. This approach consists of three steps. First, the proteome is extensively mapped out by multidimensional fractionation and tandem mass spectrometry, and the data generated are assembled in the PeptideAtlas database. Second, based on this proteome map, peptides uniquely identifying the proteins of interest, proteotypic peptides, are selected, and multiple reaction monitoring (MRM) transitions are established and validated by MS2 spectrum acquisition. This process of peptide selection, transition selection, and validation is supported by a suite of software tools, TIQAM (Targeted Identification for Quantitative Analysis by MRM), described in this study. Third, the selected target protein set is quantified in multiple samples by MRM. Applying this approach we were able to reliably quantify low abundance virulence factors from cultures of the human pathogen Streptococcus pyogenes exposed to increasing amounts of plasma. The resulting quantitative protein patterns enabled us to clearly define the subset of virulence proteins that is regulated upon plasma exposure.
Highlights
In many studies, in the field of systems biology, it is essential that identical protein sets are precisely quantified in multiple samples such as those representing differentially perturbed cell states
We developed a suite of software tools termed TIQAM (Targeted Identification for Quantitative Analysis by multiple reaction monitoring (MRM))
To support the novel aspects of this approach we developed TIQAM, an easy to use software suite with graphical user interface
Summary
The 7181 identified peptides map to a list of 964 non-redundant proteins of which 132 were only identified with one or more singly observed peptides (supplemental Data 1) The results of this analysis were loaded into the PeptideAtlas database as described previously [9] and are available for browsing and downloading. Results were imported into TIQAM, and the three transitions with the best signal-to-noise ratio were selected for quantitative analysis if the corresponding MS/MS spectrum was in accordance with the targeted peptide. 3) To correct for systematic errors from uneven total protein starting material in the samples, we calculated for every sample the median of all peptides from the housekeeping proteins (supplemental Fig. 3) and used the resulting vector for normalizing the data. The problem of differential protein abundance upon contact with different amounts of plasma can be formulated as H0: ␣1 ϭ . . . ϭ ␣5 ϭ 0 versus H1: ␣i 0 for at least two i
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