Gorshkov et al. (2015) describe SuperQuant as a quantitative proteomics data processing tool that “uses complementary fragment ions to identify multiple co-isolated peptides during tandem mass spectrometric analysis.”1 The tool is implemented as a processing node on Thermo Scientific’s Proteome Discover software (revision 2.x), and the researchers demonstrate its inherent advantages through deep proteome characterization using the human cervical epithelial adenocarcinoma HeLa cell line.
With improvements in mass spectrometric–based proteomics instrumentation, researchers are generating increased amounts of data from experimental materials. However, one disadvantage of the standard shotgun proteomics approach is that it is a time-consuming method with potential for introducing errors. Problems arise from co-isolating precursor ions during experimental runs, leading to confusion when trying to identify peptide-spectrum matches (PSMs) from the resulting data. Additionally, the overall noise in the mixed spectra generally leads to discarding of significant amounts of the data generated.
In an effort to improve data yields and accuracy, researchers interested in quantitative proteomics analysis frequently use strategies such as isobaric tags for relative and absolute quantitation (iTRAQ) labeling or tandem mass tagging (TMT) approaches. Alternatively, they may select data from an additional MS3 run or optimize recovery from the MS1 run itself using stable isotope labeling by amino acids in cell culture (SILAC) or dimethyl labeling.
Using dimethyl labeling in HeLa cell extracts, Gorshkov et al. tried a different approach, optimizing collection during the MS1 scans and fine-tuning an existing algorithm to deconvolute the mixed spectral data obtained. The researchers compared results obtained by SuperQuant analysis with those obtained from standard ion trap–based MS2 data.
The researchers fine-tuned existing algorithms using C#. This helped define new features, including relative co-isolation window borders, selective extraction of primary and secondary spectra, defining of exclusion masses, and secondary mass spectra verification by survey MS1 scan. In this way, the boosted algorithm could exploit mass relationships between complementary ion fragments to deconvolute the mixed spectra arising during the MS1 scan. From this, the researchers could determine the individual and unique peptide parent masses, thus gaining quantitative information from the MS1 scan data.
The researchers labeled HeLa cells using a standard dimethyl labeling protocol. Following protease digestion, they examined the peptides by liquid chromatography–mass spectrometry (LC-MS) using a Dionex Ultimate 3000 Nano UPLC attached to an Orbitrap Fusion Tribrid mass spectrometer (both Thermo Scientific). They compared spectral data arising from the analysis with the Swiss-Prot database.
The research team compared data collected by each method and examined parameters such as identification performance, receiver operating characteristic (ROC) curve analysis and quantitative performance. They found that analysis by SuperQuant outperformed the traditional approach. The team obtained over 70% more PSMs, with 40% more peptide and 20% more protein identifications at 0.01 false discovery rate. When they made further adjustments in the software workflow, they saw an additional increase of 10–15% in data quality.
In conclusion, Gorshkov et al. state that SuperQuant is a reliable method for obtaining qualitative and quantitative data from shotgun proteomics and is applicable to data collected previously. The method performed better than the traditional approach, with similar reliability and consistency. In addition, they suggest that the reduction in prefractionation steps needed saves time, leading to more efficient use of instruments with reduced risk of error.
1. Gorshkov, V., et al. (2015) “SuperQuant: A data processing approach to increase quantitative proteome coverage,” Analytical Chemistry 87(12) (pp. 6319−27), doi: 10.1021/acs.analchem.5b01166.
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