Chapter title |
Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS
|
---|---|
Chapter number | 14 |
Book title |
Quantitative Proteomics by Mass Spectrometry
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3524-6_14 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3522-2, 978-1-4939-3524-6
|
Authors |
Gritsenko, Marina A, Xu, Zhe, Liu, Tao, Smith, Richard D, Marina A. Gritsenko, Zhe Xu, Tao Liu, Richard D. Smith |
Editors |
Salvatore Sechi |
Abstract |
Comprehensive, quantitative information on abundances of proteins and their posttranslational modifications (PTMs) can potentially provide novel biological insights into diseases pathogenesis and therapeutic intervention. Herein, we introduce a quantitative strategy utilizing isobaric stable isotope-labeling techniques combined with two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) for large-scale, deep quantitative proteome profiling of biological samples or clinical specimens such as tumor tissues. The workflow includes isobaric labeling of tryptic peptides for multiplexed and accurate quantitative analysis, basic reversed-phase LC fractionation and concatenation for reduced sample complexity, and nano-LC coupled to high resolution and high mass accuracy MS analysis for high confidence identification and quantification of proteins. This proteomic analysis strategy has been successfully applied for in-depth quantitative proteomic analysis of tumor samples and can also be used for integrated proteome and PTM characterization, as well as comprehensive quantitative proteomic analysis across samples from large clinical cohorts. |
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Mendeley readers
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Student > Bachelor | 2 | 15% |
Researcher | 2 | 15% |
Professor > Associate Professor | 2 | 15% |
Student > Ph. D. Student | 2 | 15% |
Unspecified | 1 | 8% |
Other | 2 | 15% |
Unknown | 2 | 15% |
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Neuroscience | 2 | 15% |
Engineering | 2 | 15% |
Chemistry | 2 | 15% |
Computer Science | 1 | 8% |
Other | 1 | 8% |
Unknown | 3 | 23% |