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

Bauer, Manuel and Ahrné, Erik and Baron, Anna P. and Glatter, Timo and Fava, Luca L. and Santamaria, Anna and Nigg, Erich A. and Schmidt, Alexander. (2015) Assessment of current mass spectrometric workflows for the quantification of low abundant proteins and phosphorylation sites. Data in brief, 5. pp. 297-304.

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Official URL: http://edoc.unibas.ch/39733/

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Abstract

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 PXD000964.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Former Organization Units Biozentrum > Cell Biology (Nigg)
UniBasel Contributors:Nigg, Erich A. and Ahrné, Erik and Baron, Anna and Schmidt, Alexander
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Elsevier
ISSN:2352-3409
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:30 Jun 2016 11:00
Deposited On:25 May 2016 07:16

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