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Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)

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Cover of 'Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Fifteen Years of Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)
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    Chapter 2 Stable isotope labeling by amino acids applied to bacterial cell culture.
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    Chapter 3 SILAC Labeling of Yeast for the Study of Membrane Protein Complexes
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    Chapter 4 Whole Proteome Analysis of the Protozoan Parasite Trypanosoma brucei Using Stable Isotope Labeling by Amino Acids in Cell Culture and Mass Spectrometry
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    Chapter 5 Stable Isotope Labeling by Amino Acids in Cultured Primary Neurons
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    Chapter 6 SILAC and Alternatives in Studying Cellular Proteomes of Plants
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    Chapter 7 In Vivo Stable Isotope Labeling by Amino Acids in Drosophila melanogaster.
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    Chapter 8 Stable Isotope Labeling for Proteomic Analysis of Tissues in Mouse
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    Chapter 9 Identification of Novel Protein Functions and Signaling Mechanisms by Genetics and Quantitative Phosphoproteomics in Caenorhabditis elegans
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    Chapter 10 SILAC-Based Temporal Phosphoproteomics.
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    Chapter 11 Global Ubiquitination Analysis by SILAC in Mammalian Cells
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    Chapter 12 Quantifying In Vivo, Site-Specific Changes in Protein Methylation with SILAC.
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    Chapter 13 Applying SILAC for the Differential Analysis of Protein Complexes.
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    Chapter 14 Defining Dynamic Protein Interactions Using SILAC-Based Quantitative Mass Spectrometry.
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    Chapter 15 Identifying Nuclear Protein–Protein Interactions Using GFP Affinity Purification and SILAC-Based Quantitative Mass Spectrometry
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    Chapter 16 Analyzing the Protein Assembly and Dynamics of the Human Spliceosome with SILAC
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    Chapter 17 Identification and Validation of Protein-Protein Interactions by Combining Co-immunoprecipitation, Antigen Competition, and Stable Isotope Labeling
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    Chapter 18 Protein Correlation Profiling-SILAC to Study Protein-Protein Interactions
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    Chapter 19 Autophagosomal Proteome Analysis by Protein Correlation Profiling-SILAC
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    Chapter 20 Design and Application of Super-SILAC for Proteome Quantification.
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    Chapter 21 Proteomics Meets Genetics: SILAC Labeling of Drosophila melanogaster Larvae and Cells for In Vivo Functional Studies.
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    Chapter 22 Analysis of Secreted Proteins Using SILAC.
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    Chapter 23 Identification of MicroRNA Targets by Pulsed SILAC
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    Chapter 24 MaxQuant for In-Depth Analysis of Large SILAC Datasets.
Attention for Chapter 12: Quantifying In Vivo, Site-Specific Changes in Protein Methylation with SILAC.
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Chapter title
Quantifying In Vivo, Site-Specific Changes in Protein Methylation with SILAC.
Chapter number 12
Book title
Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-4939-1142-4_12
Pubmed ID
Book ISBNs
978-1-4939-1141-7, 978-1-4939-1142-4
Authors

Lau HT, Lewis KA, Ong SE, Ho-Tak Lau, Karen A. Lewis, Shao-En Ong

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 38%
Student > Doctoral Student 3 19%
Student > Ph. D. Student 2 13%
Professor 1 6%
Unspecified 1 6%
Other 0 0%
Unknown 3 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 38%
Biochemistry, Genetics and Molecular Biology 4 25%
Unspecified 1 6%
Nursing and Health Professions 1 6%
Chemistry 1 6%
Other 0 0%
Unknown 3 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 March 2015.
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#20,267,098
of 22,797,621 outputs
Outputs from Methods in molecular biology
#9,903
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#264,988
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Outputs of similar age from Methods in molecular biology
#406
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