Chapter title |
Quantitative Proteomics Using SILAC
|
---|---|
Chapter number | 13 |
Book title |
Proteomics
|
Published in |
Methods in molecular biology, February 2017
|
DOI | 10.1007/978-1-4939-6747-6_13 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6745-2, 978-1-4939-6747-6
|
Authors |
Kian Kani |
Editors |
Lucio Comai, Jonathan E. Katz, Parag Mallick |
Abstract |
The ability to enumerate all of the proteins in a cell is quickly becoming a reality. Quantitative proteomics adds an extra dimension to proteome-wide discovery experiments by enabling differential measurements of protein concentrations, characterization of protein turnover, increased stringency of co-immunoprecipitation reactions, as well as many other intriguing applications. One of the most widely used techniques that enable relative protein quantitation is stable isotope labeling by amino acids in cell culture (SILAC) (Ong et al., Mol Cell Proteomics 1(5):376-386, 2002). Over the past decade, SILAC has become the preferred approach for proteome-wide quantitation by mass spectrometry. This approach relies on the metabolic incorporation of isotopically enriched amino acids into the proteome of cells-the proteome of "light" ((1)H, (12)C, (14)N) cells can then be compared to "heavy" ((2)H, (13)C, (15)N) cells as the isotopically labeled proteins and peptides are easily distinguished in a mass spectrometer. Since cellular uptake and response to isotopically different amino acid(s) is naïve, it is without impact on cell physiology. We provide a detailed step-by-step procedure for performing SILAC-based experiment for proteome-wide quantitation in this chapter. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 24 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 33% |
Student > Master | 4 | 17% |
Researcher | 3 | 13% |
Student > Bachelor | 3 | 13% |
Professor > Associate Professor | 2 | 8% |
Other | 2 | 8% |
Unknown | 2 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 7 | 29% |
Agricultural and Biological Sciences | 4 | 17% |
Medicine and Dentistry | 3 | 13% |
Chemistry | 3 | 13% |
Immunology and Microbiology | 2 | 8% |
Other | 3 | 13% |
Unknown | 2 | 8% |