Literature DB >> 20058174

iTRAQ-based shotgun neuroproteomics.

Tong Liu1, Jun Hu, Hong Li.   

Abstract

Shotgun proteomics involves the analysis of <span class="Chemical">peptides obtained by enzymatic digestions of proteins and subsequent identification via tandem mass spectrometry. This approach is an effective method for studying global protein expression in neuronal systems. The method described here is a quantitative shotgun neuroproteomics method using amine-specific isobaric tags for a relative and absolute quantitation (iTRAQ)-based workflow. We will provide the technical details for sample preparation, two-dimensional liquid chromatography, tandem mass spectrometry, database search, and statistical analysis to identify differentially expressed proteins. We will use a recent study on a rat model of multiple sclerosis, experimental autoimmune encephalomyelitis to illustrate the successful application of this method.

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Year:  2009        PMID: 20058174      PMCID: PMC4640694          DOI: 10.1007/978-1-59745-562-6_14

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  16 in total

Review 1.  Shotgun proteomics: tools for the analysis of complex biological systems.

Authors:  Christine C Wu; Michael J MacCoss
Journal:  Curr Opin Mol Ther       Date:  2002-06

2.  Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research.

Authors:  Sebastian Wiese; Kai A Reidegeld; Helmut E Meyer; Bettina Warscheid
Journal:  Proteomics       Date:  2007-02       Impact factor: 3.984

3.  Quantitative proteome analysis using isotope-coded affinity tags and mass spectrometry.

Authors:  Yuzuru Shiio; Ruedi Aebersold
Journal:  Nat Protoc       Date:  2006       Impact factor: 13.491

4.  Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome.

Authors:  Junmin Peng; Joshua E Elias; Carson C Thoreen; Larry J Licklider; Steven P Gygi
Journal:  J Proteome Res       Date:  2003 Jan-Feb       Impact factor: 4.466

5.  Identification and quantification of basic and acidic proteins using solution-based two-dimensional protein fractionation and label-free or 18O-labeling mass spectrometry.

Authors:  Wells W Wu; Guanghui Wang; Ming-Jiun Yu; Mark A Knepper; Rong-Fong Shen
Journal:  J Proteome Res       Date:  2007-05-17       Impact factor: 4.466

6.  Post-translational modifications in the rat lumbar spinal cord in experimental autoimmune encephalomyelitis.

Authors:  Jennifer E Grant; Jun Hu; Tong Liu; Mohit R Jain; Stella Elkabes; Hong Li
Journal:  J Proteome Res       Date:  2007-06-13       Impact factor: 4.466

7.  Optimized proteomic analysis of a mouse model of cerebellar dysfunction using amine-specific isobaric tags.

Authors:  Jun Hu; Jin Qian; Oleg Borisov; Sanqiang Pan; Yan Li; Tong Liu; Longwen Deng; Kenneth Wannemacher; Michael Kurnellas; Christa Patterson; Stella Elkabes; Hong Li
Journal:  Proteomics       Date:  2006-08       Impact factor: 3.984

8.  Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform.

Authors:  Kevin R Coombes; Spiridon Tsavachidis; Jeffrey S Morris; Keith A Baggerly; Mien-Chie Hung; Henry M Kuerer
Journal:  Proteomics       Date:  2005-11       Impact factor: 3.984

9.  iTRAQ is a useful method to screen for membrane-bound proteins differentially expressed in human natural killer cell types.

Authors:  Troy C Lund; Lorraine B Anderson; Valarie McCullar; Leeann Higgins; Gong H Yun; Bartek Grzywacz; Michael R Verneris; Jeffrey S Miller
Journal:  J Proteome Res       Date:  2007-02       Impact factor: 4.466

10.  i-Tracker: for quantitative proteomics using iTRAQ.

Authors:  Ian P Shadforth; Tom P J Dunkley; Kathryn S Lilley; Conrad Bessant
Journal:  BMC Genomics       Date:  2005-10-20       Impact factor: 3.969

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  7 in total

1.  Chronic exposure to light reverses the effect of maternal separation on proteins in the prefrontal cortex.

Authors:  J J Dimatelis; D J Stein; V A Russell
Journal:  J Mol Neurosci       Date:  2013-07-25       Impact factor: 3.444

Review 2.  Isobaric labeling-based relative quantification in shotgun proteomics.

Authors:  Navin Rauniyar; John R Yates
Journal:  J Proteome Res       Date:  2014-11-04       Impact factor: 4.466

3.  Quantitative proteomic analysis of sphere-forming stem-like oral cancer cells.

Authors:  Kaori Misuno; Xiaojun Liu; Sizhe Feng; Shen Hu
Journal:  Stem Cell Res Ther       Date:  2013       Impact factor: 6.832

4.  Neuroproteome changes after ischemia/reperfusion injury and tissue plasminogen activator administration in rats: a quantitative iTRAQ proteomics study.

Authors:  Zamir Merali; Meah MingYang Gao; Tim Bowes; Jian Chen; Kenneth Evans; Andrea Kassner
Journal:  PLoS One       Date:  2014-05-30       Impact factor: 3.240

5.  Quantitative Proteomic Analysis Reveals Molecular Adaptations in the Hippocampal Synaptic Active Zone of Chronic Mild Stress-Unsusceptible Rats.

Authors:  Jian Zhou; Zhao Liu; Jia Yu; Xin Han; Songhua Fan; Weihua Shao; Jianjun Chen; Rui Qiao; Peng Xie
Journal:  Int J Neuropsychopharmacol       Date:  2015-09-12       Impact factor: 5.176

6.  Plasma proteomics reveals coagulation, inflammation, and metabolic shifts in H-type hypertension patients with and without acute ischemic stroke.

Authors:  Feng Zhou; Lv Zhou; Tie Guo; Nianzhen Wang; Haizhen Hao; Yanhui Zhou; Dan Yu
Journal:  Oncotarget       Date:  2017-11-01

7.  iTRAQ-based quantitative proteomic analysis provides insight for molecular mechanism of neuroticism.

Authors:  Lei Tian; Hong-Zhao You; Hao Wu; Yu Wei; Min Zheng; Lei He; Jin-Ying Liu; Shu-Zhen Guo; Yan Zhao; Ren-Lai Zhou; Xingang Hu
Journal:  Clin Proteomics       Date:  2019-11-08       Impact factor: 3.988

  7 in total

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