Estimating Influence of Cofragmentation on Peptide Quantification and Identification in iTRAQ Experiments by Simulating Multiplexed Spectra

Isobaric
tag-based quantification such as iTRAQ and TMT is a promising
approach to mass spectrometry-based quantification in proteomics as
it provides wide proteome coverage with greatly increased experimental
throughput. However, it is known to suffer from inaccurate quantification
and identification of a target peptide due to cofragmentation of multiple
peptides, which likely leads to under-estimation of differentially
expressed peptides (DEPs). A simple method of filtering out cofragmented
spectra with less than 100% precursor isolation purity (PIP) would
decrease the coverage of iTRAQ/TMT experiments. In order to estimate
the impact of cofragmentation on quantification and identification
of iTRAQ-labeled peptide samples, we generated multiplexed spectra
with varying degrees of PIP by mixing the two MS/MS spectra of 100%
PIP obtained in global proteome profiling experiments on gastric tumor–normal
tissue pair proteomes labeled by 4-plex iTRAQ. Despite cofragmentation,
the simulation experiments showed that more than 99% of multiplexed
spectra with PIP greater than 80% were correctly identified by three
different database search enginesMODa, MS-GF+, and Proteome
Discoverer. Using the multiplexed spectra that have been correctly
identified, we estimated the effect of cofragmentation on peptide
quantification. In 74% of the multiplexed spectra, however, the cancer-to-normal
expression ratio was compressed, and a fair number of spectra showed
the “ratio inflation” phenomenon. On the basis of the
estimated distribution of distortions on quantification, we were able
to calculate cutoff values for DEP detection from cofragmented spectra,
which were corrected according to a specific PIP and probability of
type I (or type II) error. When we applied these corrected cutoff
values to real cofragmented spectra with PIP larger than or equal
to 70%, we were able to identify reliable DEPs by removing about 25%
of DEPs, which are highly likely to be false positives. Our experimental
results provide useful insight into the effect of cofragmentation
on isobaric tag-based quantification methods. The simulation procedure
as well as the corrected cutoff calculation method could be adopted
for quantifying the effect of cofragmentation and reducing false positives
(or false negatives) in the DEP identification with general quantification
experiments based on isobaric labeling techniques.