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Quantitative label-free phosphoproteomics strategy for multifaceted experimental designs.

ABSTRACT: Protein phosphorylation is a critical regulator of signaling in nearly all eukaryotic cellular pathways and dysregulated phosphorylation has been implicated in an array of diseases. The majority of MS-based quantitative phosphorylation studies are currently performed from transformed cell lines because of the ability to generate large amounts of starting material with incorporated isotopically labeled amino acids during cell culture. Here we describe a general label-free quantitative phosphoproteomic strategy capable of directly analyzing relatively small amounts of virtually any biological matrix, including human tissue and biological fluids. The strategy utilizes a TiO(2) enrichment protocol in which the selectivity and recovery of phosphopeptides were optimized by assessing a twenty-point condition matrix of binding modifier concentrations and peptide-to-resin capacity ratios. The quantitative reproducibility of the TiO(2) enrichment was determined to be 16% RSD through replicate enrichments of a wild-type Danio rerio (zebrafish) lysate. Measured phosphopeptide fold-changes from alpha-casein spiked into wild-type zebrafish lysate backgrounds were within 5% of the theoretical value. Application to a morpholino induced knock-down of G protein-coupled receptor kinase 5 (GRK5) in zebrafish embryos resulted in the quantitation of 719 phosphorylated peptides corresponding to 449 phosphorylated proteins from 200 μg of zebrafish embryo lysates.

SUBMITTER: Soderblom EJ

PROVIDER: S-EPMC3093925 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

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Quantitative label-free phosphoproteomics strategy for multifaceted experimental designs.

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