Literature DB >> 22562867

Precision, proteome coverage, and dynamic range of Arabidopsis proteome profiling using (15)N metabolic labeling and label-free approaches.

Borjana Arsova1, Henrik Zauber, Waltraud X Schulze.   

Abstract

This study reports the comprehensive comparison of (15)N metabolic labeling and label free proteomic strategies for quantitation, with particular focus on plant proteomics. Our investigation of proteome coverage, dynamic range and quantitative precision for a wide range of mixing ratios and protein loadings aim to aid the investigators in the decision making process during experimental design. One of the main characteristics of the label free strategy is the applicability to all starting material, which is a limitation to the metabolic labeling. However, particularly at mixing ratios up to 10-fold the (15)N metabolic labeling proved to be more precise. Contrary to usual practice based on the results from this study, we suggest that nonequal mixing ratios in metabolic labeling could further increase the proteome coverage for quantitation. On the other hand, the label free strategy, in combination with low protein loading allows the extension of the dynamic range for quantitation and it is more precise at very high ratios, which could be important for certain types of experiments.

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Year:  2012        PMID: 22562867      PMCID: PMC3434778          DOI: 10.1074/mcp.M112.017178

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  39 in total

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4.  Quantitative proteomics of mouse brain and specific protein-interaction studies using stable isotope labeling.

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Journal:  Methods Mol Biol       Date:  2007

5.  Ratio-dependent significance thresholds in reciprocal 15N-labeling experiments as a robust tool in detection of candidate proteins responding to biological treatment.

Authors:  Sylwia Kierszniowska; Dirk Walther; Waltraud X Schulze
Journal:  Proteomics       Date:  2009-04       Impact factor: 3.984

6.  Quantitative proteomics by metabolic labeling of model organisms.

Authors:  Joost W Gouw; Jeroen Krijgsveld; Albert J R Heck
Journal:  Mol Cell Proteomics       Date:  2009-11-19       Impact factor: 5.911

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Authors:  Waltraud X Schulze; Björn Usadel
Journal:  Annu Rev Plant Biol       Date:  2010       Impact factor: 26.379

9.  Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation.

Authors:  Suman S Thakur; Tamar Geiger; Bhaswati Chatterjee; Peter Bandilla; Florian Fröhlich; Juergen Cox; Matthias Mann
Journal:  Mol Cell Proteomics       Date:  2011-05-17       Impact factor: 5.911

10.  Metabolic labeling of plant cell cultures with K(15)NO3 as a tool for quantitative analysis of proteins and metabolites.

Authors:  Wolfgang R Engelsberger; Alexander Erban; Joachim Kopka; Waltraud X Schulze
Journal:  Plant Methods       Date:  2006-09-04       Impact factor: 4.993

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  7 in total

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Journal:  Mol Cell Proteomics       Date:  2016-07-01       Impact factor: 5.911

3.  Harmonization of quality metrics and power calculation in multi-omic studies.

Authors:  Sonia Tarazona; Leandro Balzano-Nogueira; David Gómez-Cabrero; Andreas Schmidt; Axel Imhof; Thomas Hankemeier; Jesper Tegnér; Johan A Westerhuis; Ana Conesa
Journal:  Nat Commun       Date:  2020-06-18       Impact factor: 14.919

4.  PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis.

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Journal:  Bioinformatics       Date:  2019-03-01       Impact factor: 6.937

5.  Systematic evaluation of reference protein normalization in proteomic experiments.

Authors:  Henrik Zauber; Vivian Schüler; Waltraud Schulze
Journal:  Front Plant Sci       Date:  2013-02-27       Impact factor: 5.753

6.  Quantitation of Vacuolar Sugar Transporter Abundance Changes Using QconCAT Synthtetic Peptides.

Authors:  Heidi Pertl-Obermeyer; Oliver Trentmann; Kerstin Duscha; H Ekkehard Neuhaus; Waltraud X Schulze
Journal:  Front Plant Sci       Date:  2016-04-12       Impact factor: 5.753

7.  Improved Quantitative Plant Proteomics via the Combination of Targeted and Untargeted Data Acquisition.

Authors:  Gene Hart-Smith; Rodrigo S Reis; Peter M Waterhouse; Marc R Wilkins
Journal:  Front Plant Sci       Date:  2017-09-27       Impact factor: 5.753

  7 in total

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