Chapter title |
Proteomics Method to Identification of Protein Profiles in Exosomes
|
---|---|
Chapter number | 11 |
Book title |
Preeclampsia
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7498-6_11 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7497-9, 978-1-4939-7498-6
|
Authors |
Andrew Lai, Vyjayanthi Kinhal, Zarin Nuzhat, Ramkumar Menon, Gregory E. Rice, Carlos Salomon, Lai, Andrew, Kinhal, Vyjayanthi, Nuzhat, Zarin, Menon, Ramkumar, Rice, Gregory E., Salomon, Carlos |
Abstract |
Exosomes are membrane-bound nanovesicles that transport molecular signals (e.g., proteins) between cells and are released from a wide range of cells, including the human placenta. Interestingly, the levels of exosomes present in maternal circulation are higher in preeclamptic pregnancies and their protein content profile change in response to the microenvironment milieu. Through the discovery of candidate biomarkers, mass spectrometry (MS)-based proteomics may provide a better understanding of the pathophysiology underlying pregnancy-associated disorders. With advances in sample preparation techniques, computational methodologies, and bioinformatics, MS-based proteomics have addressed the challenge of identifying and quantifying thousands of proteins and peptides from a variety of complex biological samples. Despite increasing interest in biomarker diagnostics, the complex nature of biological matrices (e.g., plasma) poses a challenge for candidate biomarker discovery. Here we describe a workflow to prepare exosomes for proteomic analysis. |
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Geographical breakdown
Country | Count | As % |
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United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 32 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 7 | 22% |
Researcher | 5 | 16% |
Student > Doctoral Student | 3 | 9% |
Student > Master | 3 | 9% |
Student > Bachelor | 2 | 6% |
Other | 5 | 16% |
Unknown | 7 | 22% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 11 | 34% |
Medicine and Dentistry | 5 | 16% |
Agricultural and Biological Sciences | 4 | 13% |
Unspecified | 1 | 3% |
Computer Science | 1 | 3% |
Other | 3 | 9% |
Unknown | 7 | 22% |