Literature DB >> 17375949

15N metabolic labeling of mammalian tissue with slow protein turnover.

Daniel B McClatchy1, Meng-Qiu Dong, Christine C Wu, John D Venable, John R Yates.   

Abstract

We previously reported the metabolic 15N labeling of a rat where enrichment ranged from 94% to 74%. We report here an improved labeling strategy which generates 94% 15N enrichment throughout all tissues of the rat. A high 15N enrichment of the internal standard is necessary for accurate quantitation, and thus, this approach will allow quantitative mass spectrometry analysis of animal models of disease targeting any tissue.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17375949      PMCID: PMC2527585          DOI: 10.1021/pr060599n

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  32 in total

1.  Accurate quantitation of protein expression and site-specific phosphorylation.

Authors:  Y Oda; K Huang; F R Cross; D Cowburn; B T Chait
Journal:  Proc Natl Acad Sci U S A       Date:  1999-06-08       Impact factor: 11.205

2.  Amino acid residue specific stable isotope labeling for quantitative proteomics.

Authors:  Haining Zhu; Songqin Pan; Sheng Gu; E Morton Bradbury; Xian Chen
Journal:  Rapid Commun Mass Spectrom       Date:  2002       Impact factor: 2.419

3.  A correlation algorithm for the automated quantitative analysis of shotgun proteomics data.

Authors:  Michael J MacCoss; Christine C Wu; Hongbin Liu; Rovshan Sadygov; John R Yates
Journal:  Anal Chem       Date:  2003-12-15       Impact factor: 6.986

4.  Automatic quality assessment of peptide tandem mass spectra.

Authors:  Marshall Bern; David Goldberg; W Hayes McDonald; John R Yates
Journal:  Bioinformatics       Date:  2004-08-04       Impact factor: 6.937

5.  Differential stable isotope labeling of peptides for quantitation and de novo sequence derivation.

Authors:  D R Goodlett; A Keller; J D Watts; R Newitt; E C Yi; S Purvine; J K Eng; P von Haller ; R Aebersold; E Kolker
Journal:  Rapid Commun Mass Spectrom       Date:  2001       Impact factor: 2.419

6.  Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus.

Authors:  X Yao; A Freas; J Ramirez; P A Demirev; C Fenselau
Journal:  Anal Chem       Date:  2001-07-01       Impact factor: 6.986

7.  Decreased protein and puromycinyl-peptide degradation in livers of senescent mice.

Authors:  L Lavie; A Z Reznick; D Gershon
Journal:  Biochem J       Date:  1982-01-15       Impact factor: 3.857

8.  Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway.

Authors:  Albrecht Gruhler; Jesper V Olsen; Shabaz Mohammed; Peter Mortensen; Nils J Faergeman; Matthias Mann; Ole N Jensen
Journal:  Mol Cell Proteomics       Date:  2005-01-22       Impact factor: 5.911

9.  Global internal standard technology for comparative proteomics.

Authors:  Asish Chakraborty; Fred E Regnier
Journal:  J Chromatogr A       Date:  2002-03-08       Impact factor: 4.759

10.  Analysis of quantitative proteomic data generated via multidimensional protein identification technology.

Authors:  Michael P Washburn; Ryan Ulaszek; Cosmin Deciu; David M Schieltz; John R Yates
Journal:  Anal Chem       Date:  2002-04-01       Impact factor: 6.986

View more
  62 in total

1.  Mass spectrometry in high-throughput proteomics: ready for the big time.

Authors:  Tommy Nilsson; Matthias Mann; Ruedi Aebersold; John R Yates; Amos Bairoch; John J M Bergeron
Journal:  Nat Methods       Date:  2010-09       Impact factor: 28.547

2.  Analysis of proteome dynamics in the mouse brain.

Authors:  John C Price; Shenheng Guan; Alma Burlingame; Stanley B Prusiner; Sina Ghaemmaghami
Journal:  Proc Natl Acad Sci U S A       Date:  2010-08-10       Impact factor: 11.205

3.  The SILAC fly allows for accurate protein quantification in vivo.

Authors:  Matthias D Sury; Jia-Xuan Chen; Matthias Selbach
Journal:  Mol Cell Proteomics       Date:  2010-06-05       Impact factor: 5.911

4.  Extremely long-lived nuclear pore proteins in the rat brain.

Authors:  Jeffrey N Savas; Brandon H Toyama; Tao Xu; John R Yates; Martin W Hetzer
Journal:  Science       Date:  2012-02-02       Impact factor: 47.728

5.  Identification of long-lived proteins reveals exceptional stability of essential cellular structures.

Authors:  Brandon H Toyama; Jeffrey N Savas; Sung Kyu Park; Michael S Harris; Nicholas T Ingolia; John R Yates; Martin W Hetzer
Journal:  Cell       Date:  2013-08-29       Impact factor: 41.582

6.  Quantification of the synaptosomal proteome of the rat cerebellum during post-natal development.

Authors:  Daniel B McClatchy; Lujian Liao; Sung Kyu Park; John D Venable; John R Yates
Journal:  Genome Res       Date:  2007-08-03       Impact factor: 9.043

7.  Tracking brain palmitoylation change: predominance of glial change in a mouse model of Huntington's disease.

Authors:  Junmei Wan; Jeffrey N Savas; Amy F Roth; Shaun S Sanders; Roshni R Singaraja; Michael R Hayden; John R Yates; Nicholas G Davis
Journal:  Chem Biol       Date:  2013-11-07

Review 8.  Protein homeostasis: live long, won't prosper.

Authors:  Brandon H Toyama; Martin W Hetzer
Journal:  Nat Rev Mol Cell Biol       Date:  2013-01       Impact factor: 94.444

Review 9.  Quantification of histone modifications using ¹⁵N metabolic labeling.

Authors:  Chunchao Zhang; Yifan Liu; Philip C Andrews
Journal:  Methods       Date:  2013-02-27       Impact factor: 3.608

10.  Quantitative analysis of brain nuclear phosphoproteins identifies developmentally regulated phosphorylation events.

Authors:  Lujian Liao; Daniel B McClatchy; Sung Kyu Park; Tao Xu; Bingwen Lu; John R Yates
Journal:  J Proteome Res       Date:  2008-09-30       Impact factor: 4.466

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.