Dataset Information

O18Quant: a semiautomatic strategy for quantitative analysis of high-resolution 16O/18O labeled data.

ABSTRACT: Proteolytic (18)O-labeling has been widely used in quantitative proteomics since it can uniformly label all peptides from different kinds of proteins. There have been multiple algorithms and tools developed over the last few years to analyze high-resolution proteolytic (16)O/(18)O labeled mass spectra. We have developed a software package, O18Quant, which addresses two major issues in the previously developed algorithms. First, O18Quant uses a robust linear model (RLM) for peptide-to-protein ratio estimation. RLM can minimize the effect of outliers instead of iteratively removing them which is a common practice in other approaches. Second, the existing algorithms lack applicable implementation. We address this by implementing O18Quant using C# under Microsoft.net framework and R. O18Quant automatically calculates the peptide/protein relative ratio and provides a friendly graphical user interface (GUI) which allows the user to manually validate the quantification results at scan, peptide, and protein levels. The intuitive GUI of O18Quant can greatly enhance the user's visualization and understanding of the data analysis. O18Quant can be downloaded for free as part of the software suite ProteomicsTools.

SUBMITTER: Guo Y

PROVIDER: S-EPMC4037588 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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O18Quant: a semiautomatic strategy for quantitative analysis of high-resolution 16O/18O labeled data.

Guo Yan Y Miyagi Masaru M Zeng Rong R Sheng Quanhu Q

BioMed research international 20140511

Proteolytic (18)O-labeling has been widely used in quantitative proteomics since it can uniformly label all peptides from different kinds of proteins. There have been multiple algorithms and tools developed over the last few years to analyze high-resolution proteolytic (16)O/(18)O labeled mass spectra. We have developed a software package, O18Quant, which addresses two major issues in the previously developed algorithms. First, O18Quant uses a robust linear model (RLM) for peptide-to-protein rat ...[more]

PMID: 24901003

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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.

Project description:Oral squamous cell carcinoma (OSCC) remains one of the most common cancers worldwide, and the mortality rate of this disease has increased in recent years. No molecular markers are available to assist with the early detection and therapeutic evaluation of OSCC; thus, identification of differentially expressed proteins may assist with the detection of potential disease markers and shed light on the molecular mechanisms of OSCC pathogenesis. We performed a multidimensional (16)O/(18)O proteomics analysis using an integrated ESI-ion trap and MALDI-TOF/TOF MS system and a computational data analysis pipeline to identify proteins that are differentially expressed in microdissected OSCC tumor cells relative to adjacent non-tumor epithelia. We identified 1233 unique proteins in microdissected oral squamous epithelia obtained from three pairs of OSCC specimens with a false discovery rate of <3%. Among these, 977 proteins were quantified between tumor and non-tumor cells. Our data revealed 80 dysregulated proteins (53 up-regulated and 27 down-regulated) when a 2.5-fold change was used as the threshold. Immunohistochemical staining and Western blot analyses were performed to confirm the overexpression of 12 up-regulated proteins in OSCC tissues. When the biological roles of 80 differentially expressed proteins were assessed via MetaCore analysis, the interferon (IFN) signaling pathway emerged as one of the most significantly altered pathways in OSCC. As many as 20% (10 of 53) of the up-regulated proteins belonged to the IFN-stimulated gene (ISG) family, including ubiquitin cross-reactive protein (UCRP)/ISG15. Using head-and-neck cancer tissue microarrays, we determined that UCRP is overexpressed in the majority of cheek and tongue cancers and in several cases of larynx cancer. In addition, we found that IFN-beta stimulates UCRP expression in oral cancer cells and enhances their motility in vitro. Our findings shed new light on OSCC pathogenesis and provide a basis for the future development of novel biomarkers.

Project description:Statistical models for the analysis of protein expression changes by stable isotope labeling are still poorly developed, particularly for data obtained by 16O/18O labeling. Besides large scale test experiments to validate the null hypothesis are lacking. Although the study of mechanisms underlying biological actions promoted by vascular endothelial growth factor (VEGF) on endothelial cells is of considerable interest, quantitative proteomics studies on this subject are scarce and have been performed after exposing cells to the factor for long periods of time. In this work we present the largest quantitative proteomics study to date on the short term effects of VEGF on human umbilical vein endothelial cells by 18O/16O labeling. Current statistical models based on normality and variance homogeneity were found unsuitable to describe the null hypothesis in a large scale test experiment performed on these cells, producing false expression changes. A random effects model was developed including four different sources of variance at the spectrum-fitting, scan, peptide, and protein levels. With the new model the number of outliers at scan and peptide levels was negligible in three large scale experiments, and only one false protein expression change was observed in the test experiment among more than 1000 proteins. The new model allowed the detection of significant protein expression changes upon VEGF stimulation for 4 and 8 h. The consistency of the changes observed at 4 h was confirmed by a replica at a smaller scale and further validated by Western blot analysis of some proteins. Most of the observed changes have not been described previously and are consistent with a pattern of protein expression that dynamically changes over time following the evolution of the angiogenic response. With this statistical model the 18O labeling approach emerges as a very promising and robust alternative to perform quantitative proteomics studies at a depth of several thousand proteins.

Dataset Information

O18Quant: a semiautomatic strategy for quantitative analysis of high-resolution 16O/18O labeled data.

Publications

O18Quant: a semiautomatic strategy for quantitative analysis of high-resolution 16O/18O labeled data.

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