Literature DB >> 15834404

Quantitative mouse brain proteomics using culture-derived isotope tags as internal standards.

Yasushi Ishihama, Toshitaka Sato, Tsuyoshi Tabata, Norimasa Miyamoto, Koji Sagane, Takeshi Nagasu, Yoshiya Oda.   

Abstract

An important challenge for proteomics is to be able to compare absolute protein levels across biological samples. Here we introduce an approach based on the use of culture-derived isotope tags (CDITs) for quantitative tissue proteome analysis. We cultured Neuro2A cells in a stable isotope-enriched medium and mixed them with mouse brain samples to serve as internal standards. Using CDITs, we identified and quantified a total of 1,000 proteins, 97-98% of which were expressed in both mouse whole brain and Neuro2A cells. CDITs also allow comprehensive and absolute protein quantification. Synthetic unlabeled peptides were used to quantify the corresponding proteins labeled with stable isotopes in Neuro2A cells, and the results were used to obtain the absolute amounts of 103 proteins in mouse whole brain. The expression levels correlated well with those in Neuro2A cells. Thus, the use of CDITs allows both relative and absolute quantitative proteome studies.

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Year:  2005        PMID: 15834404     DOI: 10.1038/nbt1086

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  59 in total

1.  15N-labeled brain enables quantification of proteome and phosphoproteome in cultured primary neurons.

Authors:  Lujian Liao; Richard C Sando; John B Farnum; Peter W Vanderklish; Anton Maximov; John R Yates
Journal:  J Proteome Res       Date:  2011-12-02       Impact factor: 4.466

2.  Super-SILAC for tumors and tissues.

Authors:  Thomas A Neubert; Paul Tempst
Journal:  Nat Methods       Date:  2010-05       Impact factor: 28.547

3.  Super-SILAC mix for quantitative proteomics of human tumor tissue.

Authors:  Tamar Geiger; Juergen Cox; Pawel Ostasiewicz; Jacek R Wisniewski; Matthias Mann
Journal:  Nat Methods       Date:  2010-04-04       Impact factor: 28.547

Review 4.  A Biologist's Field Guide to Multiplexed Quantitative Proteomics.

Authors:  Corey E Bakalarski; Donald S Kirkpatrick
Journal:  Mol Cell Proteomics       Date:  2016-02-12       Impact factor: 5.911

5.  15N metabolic labeling of mammalian tissue with slow protein turnover.

Authors:  Daniel B McClatchy; Meng-Qiu Dong; Christine C Wu; John D Venable; John R Yates
Journal:  J Proteome Res       Date:  2007-03-22       Impact factor: 4.466

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.  Analysis of a membrane-enriched proteome from postmortem human brain tissue in Alzheimer's disease.

Authors:  Laura E Donovan; Lenora Higginbotham; Eric B Dammer; Marla Gearing; Howard D Rees; Qiangwei Xia; Duc M Duong; Nicholas T Seyfried; James J Lah; Allan I Levey
Journal:  Proteomics Clin Appl       Date:  2012-04       Impact factor: 3.494

Review 8.  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

9.  Shotgun proteomics in neuroscience.

Authors:  Lujian Liao; Daniel B McClatchy; John R Yates
Journal:  Neuron       Date:  2009-07-16       Impact factor: 17.173

10.  Quantitative proteomic profiling of host-pathogen interactions: the macrophage response to Mycobacterium tuberculosis lipids.

Authors:  Wenqing Shui; Sarah A Gilmore; Leslie Sheu; Jun Liu; Jay D Keasling; Carolyn R Bertozzi
Journal:  J Proteome Res       Date:  2009-01       Impact factor: 4.466

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