3. Protein Level Quantification

  1. Ingvar Eidhammer,
  2. Harald Barsnes,
  3. Geir Egil Eide and
  4. Lennart Martens

Published Online: 10 JAN 2013

DOI: 10.1002/9781118494042.ch3

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

Computational and Statistical Methods for Protein Quantification by Mass Spectrometry

How to Cite

Eidhammer, I., Barsnes, H., Eide, G. E. and Martens, L. (2013) Protein Level Quantification, in Computational and Statistical Methods for Protein Quantification by Mass Spectrometry, John Wiley & Sons Ltd, Oxford, UK. doi: 10.1002/9781118494042.ch3

Publication History

  1. Published Online: 10 JAN 2013
  2. Published Print: 4 JAN 2013

ISBN Information

Print ISBN: 9781119964001

Online ISBN: 9781118494042

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Keywords:

  • 2D SDS-PAGE gel;
  • differential in-gel electrophoresis (DIGE) approach;
  • enzyme-linked immunosorbent assay (ELISA);
  • protein arrays;
  • western blotting

Summary

This chapter describes the most common methods that do not rely on the combination of liquid chromatography and mass spectrometry. In general, 2D SDS-PAGE gel quantification is based on the signal intensity of the spot in which the protein has been found. A widely used technique to avoid the typical low reproducibility between different gels is the fluorescence-based differential in-gel electrophoresis (DIGE) approach. Another often-used method for discovery oriented protein quantification is provided by protein arrays. Where 2D gel electrophoresis and protein arrays provide discovery oriented methods used to detect and quantify as many proteins as possible in a sample,Western blotting provides a targeted means to quantify a single protein in a sample. Another popular means of targeted protein quantification is offered by enzyme-linked immunosorbent assay (ELISA). ELISA is often used in clinical assays, and the development of an ELISA is therefore often the endpoint of a biomarker discovery pipeline.

Controlled Vocabulary Terms

Computational statistics; Inferential statistics