Literature DB >> 20364148

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

Tamar Geiger1, Juergen Cox, Pawel Ostasiewicz, Jacek R Wisniewski, Matthias Mann.   

Abstract

We describe a method to accurately quantify human tumor proteomes by combining a mixture of five stable-isotope labeling by amino acids in cell culture (SILAC)-labeled cell lines with human carcinoma tissue. This generated hundreds of thousands of isotopically labeled peptides in appropriate amounts to serve as internal standards for mass spectrometry-based analysis. By decoupling the labeling from the measurement, this super-SILAC method broadens the scope of SILAC-based proteomics.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20364148     DOI: 10.1038/nmeth.1446

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  18 in total

1.  Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap.

Authors:  Jesper V Olsen; Lyris M F de Godoy; Guoqing Li; Boris Macek; Peter Mortensen; Reinhold Pesch; Alexander Makarov; Oliver Lange; Stevan Horning; Matthias Mann
Journal:  Mol Cell Proteomics       Date:  2005-10-24       Impact factor: 5.911

2.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Authors:  Jürgen Cox; Matthias Mann
Journal:  Nat Biotechnol       Date:  2008-11-30       Impact factor: 54.908

Review 3.  The biological impact of mass-spectrometry-based proteomics.

Authors:  Benjamin F Cravatt; Gabriel M Simon; John R Yates
Journal:  Nature       Date:  2007-12-13       Impact factor: 49.962

4.  Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome.

Authors:  Jacek R Wiśniewski; Alexandre Zougman; Matthias Mann
Journal:  J Proteome Res       Date:  2009-12       Impact factor: 4.466

5.  Glucose metabolism of breast cancer assessed by 18F-FDG PET: histologic and immunohistochemical tissue analysis.

Authors:  N Avril; M Menzel; J Dose; M Schelling; W Weber; F Jänicke; W Nathrath; M Schwaiger
Journal:  J Nucl Med       Date:  2001-01       Impact factor: 10.057

6.  Breast carcinomas fulfill the Warburg hypothesis and provide metabolic markers of cancer prognosis.

Authors:  Antonio Isidoro; Enrique Casado; Andrés Redondo; Paloma Acebo; Enrique Espinosa; Andrés M Alonso; Paloma Cejas; David Hardisson; Juan A Fresno Vara; Cristobal Belda-Iniesta; Manuel González-Barón; José M Cuezva
Journal:  Carcinogenesis       Date:  2005-07-20       Impact factor: 4.944

7.  Correlation of E-cadherin expression with differentiation grade and histological type in breast carcinoma.

Authors:  C Gamallo; J Palacios; A Suarez; A Pizarro; P Navarro; M Quintanilla; A Cano
Journal:  Am J Pathol       Date:  1993-04       Impact factor: 4.307

8.  SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function.

Authors:  Marcus Krüger; Markus Moser; Siegfried Ussar; Ingo Thievessen; Christian A Luber; Francesca Forner; Sarah Schmidt; Sara Zanivan; Reinhard Fässler; Matthias Mann
Journal:  Cell       Date:  2008-07-25       Impact factor: 41.582

Review 9.  A literature review of molecular markers predictive of clinical response to cytotoxic chemotherapy in patients with breast cancer.

Authors:  Ikuo Sekine; Chikako Shimizu; Kazuto Nishio; Nagahiro Saijo; Tomohide Tamura
Journal:  Int J Clin Oncol       Date:  2009-04-24       Impact factor: 3.402

10.  Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

Authors:  Shao-En Ong; Blagoy Blagoev; Irina Kratchmarova; Dan Bach Kristensen; Hanno Steen; Akhilesh Pandey; Matthias Mann
Journal:  Mol Cell Proteomics       Date:  2002-05       Impact factor: 5.911

View more
  190 in total

Review 1.  The grand challenge to decipher the cancer proteome.

Authors:  Samir Hanash; Ayumu Taguchi
Journal:  Nat Rev Cancer       Date:  2010-09       Impact factor: 60.716

2.  Super-SILAC for tumors and tissues.

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

Review 3.  Decoding signalling networks by mass spectrometry-based proteomics.

Authors:  Chunaram Choudhary; Matthias Mann
Journal:  Nat Rev Mol Cell Biol       Date:  2010-05-12       Impact factor: 94.444

4.  A quantitative proteomics design for systematic identification of protease cleavage events.

Authors:  Francis Impens; Niklaas Colaert; Kenny Helsens; Bart Ghesquière; Evy Timmerman; Pieter-Jan De Bock; Benjamin M Chain; Joël Vandekerckhove; Kris Gevaert
Journal:  Mol Cell Proteomics       Date:  2010-07-13       Impact factor: 5.911

5.  SILACtor: software to enable dynamic SILAC studies.

Authors:  Michael R Hoopmann; Juan D Chavez; James E Bruce
Journal:  Anal Chem       Date:  2011-10-27       Impact factor: 6.986

6.  QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues.

Authors:  Harsha P Gunawardena; Jonathon O'Brien; John A Wrobel; Ling Xie; Sherri R Davies; Shunqiang Li; Matthew J Ellis; Bahjat F Qaqish; Xian Chen
Journal:  Mol Cell Proteomics       Date:  2015-11-23       Impact factor: 5.911

Review 7.  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

8.  Quantitative proteomic study of arsenic treated mouse liver sinusoidal endothelial cells using a reverse super-SILAC method.

Authors:  Wenbo Li; Jiyang Zhang; Yongzhuang Lv; Nader Sheibani
Journal:  Biochem Biophys Res Commun       Date:  2019-05-02       Impact factor: 3.575

9.  Deciphering Phosphotyrosine-Dependent Signaling Networks in Cancer by SH2 Profiling.

Authors:  Kazuya Machida; Malik Khenkhar; Peter Nollau
Journal:  Genes Cancer       Date:  2012-05

10.  Calibration Using a Single-Point External Reference Material Harmonizes Quantitative Mass Spectrometry Proteomics Data between Platforms and Laboratories.

Authors:  Lindsay K Pino; Brian C Searle; Eric L Huang; William Stafford Noble; Andrew N Hoofnagle; Michael J MacCoss
Journal:  Anal Chem       Date:  2018-10-23       Impact factor: 6.986

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.