Abstract
In this study, we present a fully automated tool, called IDEAL-Q, for label-free quantitation analysis. It accepts raw data in the standard mzXML format as well as search results from major search engines, including Mascot, SEQUEST, and X!Tandem, as input data. To quantify as many identified peptides as possible, IDEAL-Q uses an efficient algorithm to predict the elution time of a peptide unidentified in a specific LC-MS/MS run but identified in other runs. Then, the predicted elution time is used to detect peak clusters of the assigned peptide. Detected peptide peaks are processed by statistical and computational methods and further validated by signal-to-noise ratio, charge state, and isotopic distribution criteria (SCI validation) to filter out noisy data. The performance of IDEAL-Q has been evaluated by several experiments. First, a serially diluted protein mixed with Escherichia coli lysate showed a high correlation with expected ratios and demonstrated good linearity (R2 = 0.996). Second, in a biological replicate experiment on the THP-1 cell lysate, IDEAL-Q quantified 87% (1,672 peptides) of all identified peptides, surpassing the 45.7% (909 peptides) achieved by the conventional identity-based approach, which only quantifies peptides identified in all LC-MS/MS runs. Manual validation on all 11,940 peptide ions in six replicate LC-MS/MS runs revealed that 97.8% of the peptide ions were correctly aligned, and 93.3% were correctly validated by SCI. Thus, the mean of the protein ratio, 1.00 ± 0.05, demonstrates the high accuracy of IDEAL-Q without human intervention. Finally, IDEAL-Q was applied again to the biological replicate experiment but with an additional SDS-PAGE step to show its compatibility for label-free experiments with fractionation. For flexible workflow design, IDEAL-Q supports different fractionation strategies and various normalization schemes, including multiple spiked internal standards. User-friendly interfaces are provided to facilitate convenient inspection, validation, and modification of quantitation results. In summary, IDEAL-Q is an efficient, user-friendly, and robust quantitation tool. It is available for download.
Highlights
In this study, we present a fully automated tool, called ID-based elution time prediction by fragmental regression (IDEAL)-Q, for label-free quantitation analysis
We conducted four experiments to evaluate the performance of IDEAL-Q in terms of the accuracy of elution time prediction, quantitation coverage, and quantitation accuracy
The workflow and the quantitation performance of IDEAL-Q were demonstrated on a serially diluted standard protein mixture spiked into E. coli cell lysate
Summary
Triethylammonium bicarbonate (TEABC) was purchased from Sigma-Aldrich. TFA, formic acid (FA), and HPLC grade ACN were purchased from Sigma-Aldrich. Modified, sequencing grade trypsin was purchased from Promega (Madison, WI). Raw MS/MS data were converted into peak lists using Distiller (Matrix Science, London, UK; version 2.0) with the default parameters. All MS/MS samples were analyzed using Mascot (Matrix Science; version 2.2.1). Mascot was set up to search the ipi_HUMAN_3.29 database (version 3.29; 68,161 entries) for the THP-1 cell line and the Swisssprot_Metazoa_Animals database (version 54.2; 17,170 entries) for standard proteins, assuming trypsin as the digestion enzyme. Mascot was set up to search with a fragment ion mass tolerance of 0.1 Da and a parent ion tolerance of 0.1 Da. Two missed cleavages were allowed during trypsin digestion. The unique MS/MS spectra and assignment of identified peptides are shown in the supplemental figures
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