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Minimizing back exchange in 18O/16O quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step.

ABSTRACT: Differential labeling of peptides via the use of the 18O-water proteolytic labeling method has been widely adopted for quantitative shotgun proteomics studies due to its simplicity and low reagent costs. In this report, the use of immobilized trypsin in the initial digestion step, in addition to the initial digestion step, is explored as a means to minimize postlabeling back exchange of 18O-labeled peptides into the 16O form when multidimensional peptide separation methods (here, isoelectric focusing of peptides) are incorporated into the sample workflow. Examples are shown with a mixture of standard proteins and a sample from an ongoing clinical proteomics study.

SUBMITTER: Sevinsky JR

PROVIDER: S-EPMC2796076 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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Minimizing back exchange in 18O/16O quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step.

Sevinsky Joel R JR Brown Kristy J KJ Cargile Benjamin J BJ Bundy Jonathan L JL Stephenson James L JL

Analytical chemistry 20070124 5

Differential labeling of peptides via the use of the 18O-water proteolytic labeling method has been widely adopted for quantitative shotgun proteomics studies due to its simplicity and low reagent costs. In this report, the use of immobilized trypsin in the initial digestion step, in addition to the initial digestion step, is explored as a means to minimize postlabeling back exchange of 18O-labeled peptides into the 16O form when multidimensional peptide separation methods (here, isoelectric foc ...[more]

PMID: 17249691

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Project description:BackgroundQuantitative measurements of specific protein phosphorylation sites, as presented here, can be used to investigate signal transduction pathways, which is an important aspect of cell dynamics. The presented method quantitatively compares peptide abundances from experiments using 18O/16O labeling starting from elaborated MS spectra. It was originally developed to study signaling cascades activated by amyloid-beta treatment of neurons used as a cellular model system with relevance to Alzheimer's disease, but is generally applicable.ResultsThe presented method assesses, in complete cell lysates, the degree of phosphorylation of specific peptide residues from MS spectra using 18O/16O labeling. The abundance of each observed phospho-peptide from two cell states was estimated from three overlapping isotope contours. The influence of peptide-specific labeling efficiency was removed by performing a label swapped experiment and assuming that the labeling efficiency was unchanged upon label swapping. Different degrees of phosphorylation were reported using the fold change measure which was extended with a confidence interval found to reflect the quality of the underlying spectra. Furthermore a new way of method assessment using simulated data is presented. Using simulated data generated in a manner mimicking real data it was possible to show the method's robustness both with increasing noise levels and with decreasing labeling efficiency.ConclusionThe fold change error assessable on simulated data was on average 0.16 (median 0.10) with an error-to-signal ratio and labeling efficiency distributions similar to the ones found in the experimentally observed spectra. Applied to experimentally observed spectra a very good match was found to the model (<10% error for 85% of spectra) with a high degree of robustness, as assessed by data removal. This new method can thus be used for quantitative signal cascade analysis of total cell extracts in a high throughput mode.

Dataset Information

Minimizing back exchange in 18O/16O quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step.

Publications

Minimizing back exchange in 18O/16O quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step.

Similar Datasets

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