Dataset Information

Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.

ABSTRACT: Liquid chromatography coupled to tandem mass spectrometry in combination with stable-isotope labeling is an established and widely spread method to measure gene expression on the protein level. However, it is often not considered that two opposing processes are responsible for the amount of a protein in a cell--the synthesis as well as the degradation. With this work, we provide an integrative, high-throughput method--from the experimental setup to the bioinformatics analysis--to measure synthesis and degradation rates of an organism's proteome. Applicability of the approach is demonstrated with an investigation of heat shock response, a well-understood regulatory mechanism in bacteria, on the biotechnologically relevant Corynebacterium glutamicum. Utilizing a multilabeling approach using both heavy stable nitrogen as well as carbon isotopes cells are metabolically labeled in a pulse-chase experiment to trace the labels' incorporation in newly synthesized proteins and its loss during protein degradation. Our work aims not only at the calculation of protein turnover rates but also at their statistical evaluation, including variance and hierarchical cluster analysis using the rich internet application QuPE.

SUBMITTER: Trotschel C

PROVIDER: S-EPMC3412979 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.

Trötschel Christian C Albaum Stefan P SP Wolff Daniel D Schröder Simon S Goesmann Alexander A Nattkemper Tim W TW Poetsch Ansgar A

Molecular & cellular proteomics : MCP 20120406 8

Liquid chromatography coupled to tandem mass spectrometry in combination with stable-isotope labeling is an established and widely spread method to measure gene expression on the protein level. However, it is often not considered that two opposing processes are responsible for the amount of a protein in a cell--the synthesis as well as the degradation. With this work, we provide an integrative, high-throughput method--from the experimental setup to the bioinformatics analysis--to measure synthes ...[more]

PMID: 22493176

Similar Datasets

Project description:Despite data-independent acquisition (DIA) has been increasingly used for relative protein quantification, DIA-based label-free absolute quantification method has not been fully established. Here we present a novel DIA method using the TPA algorithm (DIA-TPA) for the absolute quantification of protein expressions in human liver microsomal and S9 samples. To validate this method, both data-dependent acquisition (DDA) and DIA experiments were conducted on 36 individual human liver microsome and S9 samples. The MS2-based DIA-TPA was able to quantify approximately twice as many proteins as the MS1-based DDA-TPA method, whereas protein concentrations determined by the two approaches were comparable. To evaluate the accuracy of the DIA-TPA method, we absolutely quantified carboxylesterase 1 concentrations in human liver S9 fractions using an established SILAC internal standard-based proteomic assay; the SILAC results were consistent with those obtained from DIA-TPA analysis. Finally, we employed a unique algorithm in DIA-TPA to distribute the MS signals from shared peptides to individual proteins or isoforms and successfully applied the method to the absolute quantification of several drug-metabolizing enzymes in human liver microsomes. In sum, the DIA-TPA method not only can absolutely quantify entire proteomes and specific proteins, but also has the capability quantifying proteins with shared peptides. SIGNIFICANCE: Data independent acquisition (DIA) has emerged as a powerful approach for relative protein quantification at the whole proteome level. However, DIA-based label-free absolute protein quantification (APQ) method has not been fully established. In the present study, we present a novel DIA-based label-free APQ approach, named DIA-TPA, with the capability absolutely quantifying proteins with shared peptides. The method was validated by comparing the quantification results of DIA-TPA with that obtained from stable isotope-labeled internal standard-based proteomic assays.

Dataset Information

Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.

Publications

Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets