Dataset Information

MS approach assessment and Mad1 phosphopeptide analysis

ABSTRACT: We evaluated the performance of different popular DDA and DIA MS approaches and applied the most sensitive apporaches to monitor phosphoryation events in Mad1.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell, Cell Culture

SUBMITTER: Alexander Schmidt

LAB HEAD: Alexander Schmidt

PROVIDER: PXD000964 | Pride | 2014-10-31

REPOSITORIES: Pride

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Publications

Evaluation of data-dependent and -independent mass spectrometric workflows for sensitive quantification of proteins and phosphorylation sites.

Bauer Manuel M Ahrné Erik E Baron Anna P AP Glatter Timo T Fava Luca L LL Santamaria Anna A Nigg Erich A EA Schmidt Alexander A

Journal of proteome research 20141113 12

In recent years, directed and, particularly, targeted mass spectrometric workflows have gained momentum as alternative techniques to conventional data-dependent acquisition (DDA) LC-MS/MS approaches. By focusing on specific peptide species, these methods allow hypothesis-driven analysis of selected proteins of interest, and they have been shown to be suited to monitor low-abundance proteins within complex mixtures. Despite their growing popularity, no study has systematically evaluated these var ...[more]

PMID: 25330945

Similar Datasets

Project description:There is a great interest in reliable ways to obtain absolute protein abundances at a proteome-wide scale. To this end, label-free LC-MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number of accurately quantified reference proteins, we evaluate five popular low cost and easily implemented quantification methods (APEX, emPAI, iBAQ, Top3 and MeanInt). Our results demonstrate considerably improved abundance estimates upon implementing accurately quantified reference proteins; i.e. using spiked in SIS peptides or a standard protein mix, to generate a properly calibrated quantification model. We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements. Additionally, we demonstrate that spectral count based quantification methods are associated with higher errors than MS1 peak intensity based methods. Furthermore, we investigate the impact of mis-cleaved, modified and shared peptides as well as protein size and the number of employed reference proteins on quantification accuracy. The .raw data submitted to PRIDE correspond to replicate DDA LC-MS/MS analysis of the UPS2 mix (Universal Proteomics Standard, UPS2, Sigma-Aldrich), as well as triplicate DDA LC-MS/MS analysis of the UPS2 mix spiked into samples from the following organisms M. pneumoniae, L. interrogans and D. melanogaster. The accompanying R-script called calculatePQIs.R was used to calculate the different Quantification Indices, provided that the peptide XICs or Spectral Counts have been loaded into a data.frame R object (which can be produced by SafeQuant).

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