Literature DB >> 20013377

An overview of label-free quantitation methods in proteomics by mass spectrometry.

Jason W H Wong1, Gerard Cagney.   

Abstract

Protein quantification represents an important extension to identification proteomics, enabling the comparison of protein expression across different samples or treatments. Comparative protein quantification by mass spectrometry typically employs stable isotope incorporation, but recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this chapter, we provide an overview of the different approaches for extracting quantitative data from label-free LC(n)-MS experiments. The computational procedure for recovering the quantitative information is outlined. Examples of statistical tests used to evaluate the relevance of results are also provided.

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Year:  2010        PMID: 20013377     DOI: 10.1007/978-1-60761-444-9_18

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


  34 in total

Review 1.  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

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

Authors:  Borjana Arsova; Henrik Zauber; Waltraud X Schulze
Journal:  Mol Cell Proteomics       Date:  2012-05-05       Impact factor: 5.911

3.  Native SILAC: metabolic labeling of proteins in prototroph microorganisms based on lysine synthesis regulation.

Authors:  Florian Fröhlich; Romain Christiano; Tobias C Walther
Journal:  Mol Cell Proteomics       Date:  2013-04-16       Impact factor: 5.911

4.  Comparative label-free LC-MS/MS analysis of colorectal adenocarcinoma and metastatic cells treated with 5-fluorouracil.

Authors:  Kerry M Bauer; Paul A Lambert; Amanda B Hummon
Journal:  Proteomics       Date:  2012-06       Impact factor: 3.984

5.  A Synonymous Mutation Upstream of the Gene Encoding a Weak-Link Enzyme Causes an Ultrasensitive Response in Growth Rate.

Authors:  Jamie P Kershner; Sean Yu McLoughlin; Juhan Kim; Andrew Morgenthaler; Christopher C Ebmeier; William M Old; Shelley D Copley
Journal:  J Bacteriol       Date:  2016-09-22       Impact factor: 3.490

6.  A quantitative proteomics approach identifies ETV6 and IKZF1 as new regulators of an ERG-driven transcriptional network.

Authors:  Ashwin Unnikrishnan; Yi F Guan; Yizhou Huang; Dominik Beck; Julie A I Thoms; Sofie Peirs; Kathy Knezevic; Shiyong Ma; Inge V de Walle; Ineke de Jong; Zara Ali; Ling Zhong; Mark J Raftery; Tom Taghon; Jonas Larsson; Karen L MacKenzie; Pieter Van Vlierberghe; Jason W H Wong; John E Pimanda
Journal:  Nucleic Acids Res       Date:  2016-09-06       Impact factor: 16.971

7.  Biotin Proximity Labeling for Protein-Protein Interaction Discovery: The BioID Method.

Authors:  Jeff E Habel
Journal:  Methods Mol Biol       Date:  2021

8.  The host targeting motif in exported Plasmodium proteins is cleaved in the parasite endoplasmic reticulum.

Authors:  Andrew R Osborne; Kaye D Speicher; Pamela A Tamez; Souvik Bhattacharjee; David W Speicher; Kasturi Haldar
Journal:  Mol Biochem Parasitol       Date:  2010-02-01       Impact factor: 1.759

9.  Phosphoproteomics profiling of human skin fibroblast cells reveals pathways and proteins affected by low doses of ionizing radiation.

Authors:  Feng Yang; Katrina M Waters; John H Miller; Marina A Gritsenko; Rui Zhao; Xiuxia Du; Eric A Livesay; Samuel O Purvine; Matthew E Monroe; Yingchun Wang; David G Camp; Richard D Smith; David L Stenoien
Journal:  PLoS One       Date:  2010-11-30       Impact factor: 3.240

10.  Morpheus Spectral Counter: A computational tool for label-free quantitative mass spectrometry using the Morpheus search engine.

Authors:  David C Gemperline; Mark Scalf; Lloyd M Smith; Richard D Vierstra
Journal:  Proteomics       Date:  2016-03       Impact factor: 3.984

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