| Chapter title |
Glycoproteins Enrichment and LC-MS/MS Glycoproteomics in Central Nervous System Applications
|
|---|---|
| Chapter number | 9 |
| Book title |
Neuroproteomics
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-6952-4_9 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6950-0, 978-1-4939-6952-4
|
| Authors |
Rui Zhu, Ehwang Song, Ahmed Hussein, Firas H. Kobeissy, Yehia Mechref, Zhu, Rui, Song, Ehwang, Hussein, Ahmed, Kobeissy, Firas H., Mechref, Yehia |
| Editors |
Firas H. Kobeissy, Stanley M. Stevens, Jr. |
| Abstract |
Proteins and glycoproteins play important biological roles in central nervous systems (CNS). Qualitative and quantitative evaluation of proteins and glycoproteins expression in CNS is critical to reveal the inherent biomolecular mechanism of CNS diseases. This chapter describes proteomic and glycoproteomic approaches based on liquid chromatography/tandem mass spectrometry (LC-MS or LC-MS/MS) for the qualitative and quantitative assessment of proteins and glycoproteins expressed in CNS. Proteins and glycoproteins, extracted by a mass spectrometry friendly surfactant from CNS samples, were subjected to enzymatic (tryptic) digestion and three down-stream analyses: (1) a nano LC system coupled with a high-resolution MS instrument to achieve qualitative proteomic profile, (2) a nano LC system combined with a triple quadrupole MS to quantify identified proteins, and (3) glycoprotein enrichment prior to LC-MS/MS analysis. Enrichment techniques can be applied to improve coverage of low abundant glycopeptides/glycoproteins. An example described in this chapter is hydrophilic interaction liquid chromatographic (HILIC) enrichment to capture glycopeptides, allowing efficient removal of peptides. The combination of three LC-MS/MS-based approaches is capable of the investigation of large-scale proteins and glycoproteins from CNS with an in-depth coverage, thus offering a full view of proteins and glycoproteins changes in CNS. |
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|---|---|---|
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| Unknown | 1 | 50% |
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|---|---|---|
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| Members of the public | 1 | 50% |
Mendeley readers
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| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
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| Other | 3 | 9% |
| Student > Doctoral Student | 3 | 9% |
| Researcher | 3 | 9% |
| Professor > Associate Professor | 3 | 9% |
| Other | 8 | 24% |
| Unknown | 8 | 24% |
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|---|---|---|
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| Medicine and Dentistry | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 11 | 33% |