Assessment of current mass spectrometric workflows for the quantification of low abundant proteins and phosphorylation sites

• Published in: Data in brief 2015-12, Vol.5 (C), p.297-304
• Main Authors: Bauer, Manuel, Ahrné, Erik, Baron, Anna P., Glatter, Timo, Fava, Luca L., Santamaria, Anna, Nigg, Erich A., Schmidt, Alexander
• Format: Article
• Language: English
• Subjects: Data
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2015.08.015

The data described here provide a systematic performance evaluation of popular data-dependent (DDA) and independent (DIA) mass spectrometric (MS) workflows currently used in quantitative proteomics. We assessed the limits of identification, quantification and detection for each method by analyzing a dilution series of 20 unmodified and 10 phosphorylated synthetic heavy labeled reference peptides, respectively, covering six orders of magnitude in peptide concentration with and without a complex human cell digest background. We found that all methods performed very similarly in the absence of background proteins, however, when analyzing whole cell lysates, targeted methods were at least 5–10 times more sensitive than directed or DDA methods. In particular, higher stage fragmentation (MS3) of the neutral loss peak using a linear ion trap increased dynamic quantification range of some phosphopeptides up to 100-fold. We illustrate the power of this targeted MS3 approach for phosphopeptide monitoring by successfully quantifying 9 phosphorylation sites of the kinetochore and spindle assembly checkpoint component Mad1 over different cell cycle states from non-enriched pull-down samples. The data are associated to the research article ‘Evaluation of data-dependent and data-independent mass spectrometric workflows for sensitive quantification of proteins and phosphorylation sites׳ (Bauer et al., 2014) [1]. The mass spectrometry and the analysis dataset have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier http://www.ebi.ac.uk/pride/archive/projects/PXD000964.