Chapter title |
A Method for Label-Free, Differential Top-Down Proteomics. - PubMed - NCBI
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Chapter number | 8 |
Book title |
Quantitative Proteomics by Mass Spectrometry
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Published in |
Methods in molecular biology, January 2016
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DOI | 10.1007/978-1-4939-3524-6_8 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3522-2, 978-1-4939-3524-6
|
Authors |
Ntai, Ioanna, Toby, Timothy K, LeDuc, Richard D, Kelleher, Neil L, Ioanna Ntai, Timothy K. Toby, Richard D. LeDuc, Neil L. Kelleher |
Editors |
Salvatore Sechi |
Abstract |
Biomarker discovery in the translational research has heavily relied on labeled and label-free quantitative bottom-up proteomics. Here, we describe a new approach to biomarker studies that utilizes high-throughput top-down proteomics and is the first to offer whole protein characterization and relative quantitation within the same experiment. Using yeast as a model, we report procedures for a label-free approach to quantify the relative abundance of intact proteins ranging from 0 to 30 kDa in two different states. In this chapter, we describe the integrated methodology for the large-scale profiling and quantitation of the intact proteome by liquid chromatography-mass spectrometry (LC-MS) without the need for metabolic or chemical labeling. This recent advance for quantitative top-down proteomics is best implemented with a robust and highly controlled sample preparation workflow before data acquisition on a high-resolution mass spectrometer, and the application of a hierarchical linear statistical model to account for the multiple levels of variance contained in quantitative proteomic comparisons of samples for basic and clinical research. |
X Demographics
Geographical breakdown
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France | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 28 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 6 | 21% |
Student > Bachelor | 5 | 18% |
Student > Master | 5 | 18% |
Researcher | 4 | 14% |
Professor | 2 | 7% |
Other | 4 | 14% |
Unknown | 2 | 7% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 10 | 36% |
Chemistry | 4 | 14% |
Chemical Engineering | 2 | 7% |
Engineering | 2 | 7% |
Agricultural and Biological Sciences | 2 | 7% |
Other | 7 | 25% |
Unknown | 1 | 4% |