Dataset Information

In silico instrumental response correction improves precision of label-free proteomics and accuracy of proteomics-based predictive models.

ABSTRACT: In the analysis of proteome changes arising during the early stages of a biological process (e.g. disease or drug treatment) or from the indirect influence of an important factor, the biological variations of interest are often small (?10%). The corresponding requirements for the precision of proteomics analysis are high, and this often poses a challenge, especially when employing label-free quantification. One of the main contributors to the inaccuracy of label-free proteomics experiments is the variability of the instrumental response during LC-MS/MS runs. Such variability might include fluctuations in the electrospray current, transmission efficiency from the air-vacuum interface to the detector, and detection sensitivity. We have developed an in silico post-processing method of reducing these variations, and have thus significantly improved the precision of label-free proteomics analysis. For abundant blood plasma proteins, a coefficient of variation of approximately 1% was achieved, which allowed for sex differentiation in pooled samples and ?90% accurate differentiation of individual samples by means of a single LC-MS/MS analysis. This method improves the precision of measurements and increases the accuracy of predictive models based on the measurements. The post-acquisition nature of the correction technique and its generality promise its widespread application in LC-MS/MS-based methods such as proteomics and metabolomics.

SUBMITTER: Lyutvinskiy Y

PROVIDER: S-EPMC3734588 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

In silico instrumental response correction improves precision of label-free proteomics and accuracy of proteomics-based predictive models.

Lyutvinskiy Yaroslav Y Yang Hongqian H Rutishauser Dorothea D Zubarev Roman A RA

Molecular & cellular proteomics : MCP 20130415 8

In the analysis of proteome changes arising during the early stages of a biological process (e.g. disease or drug treatment) or from the indirect influence of an important factor, the biological variations of interest are often small (∼10%). The corresponding requirements for the precision of proteomics analysis are high, and this often poses a challenge, especially when employing label-free quantification. One of the main contributors to the inaccuracy of label-free proteomics experiments is th ...[more]

PMID: 23589346

Similar Datasets

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.

Dataset Information

In silico instrumental response correction improves precision of label-free proteomics and accuracy of proteomics-based predictive models.

Publications

In silico instrumental response correction improves precision of label-free proteomics and accuracy of proteomics-based predictive models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets