| Chapter title |
Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation
|
|---|---|
| Chapter number | 1 |
| Book title |
Serum/Plasma Proteomics
|
| Published in |
Methods in molecular biology, July 2017
|
| DOI | 10.1007/978-1-4939-7057-5_1 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7056-8, 978-1-4939-7057-5
|
| Authors |
Viviana Greco, Cristian Piras, Luisa Pieroni, Andrea Urbani Ph.D., Andrea Urbani |
| Editors |
David W. Greening, Richard J. Simpson |
| Abstract |
Blood proteome analysis for biomarker discovery represents one of the most challenging tasks to be achieved through clinical proteomics due to the sample complexity, such as the extreme heterogeneity of proteins in very dynamic concentrations, and to the observation of proper sampling and storage conditions. Quantitative and qualitative proteomics profiling of plasma and serum could be useful both for the early detection of diseases and for the evaluation of pathological status. Two main sources of variability can affect the precision and accuracy of the quantitative experiments designed for biomarker discovery and validation. These sources are divided into two categories, pre-analytical and analytical, and are often ignored; however, they can contribute to consistent errors and misunderstanding in biomarker research. In this chapter, we review critical pre-analytical and analytical variables that can influence quantitative proteomics. According to guidelines accepted by proteomics community, we propose some recommendations and strategies for a proper proteomics analysis addressed to biomarker studies. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 17% |
| Researcher | 5 | 17% |
| Student > Ph. D. Student | 3 | 10% |
| Student > Doctoral Student | 1 | 3% |
| Student > Bachelor | 1 | 3% |
| Other | 4 | 13% |
| Unknown | 11 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 13% |
| Biochemistry, Genetics and Molecular Biology | 4 | 13% |
| Medicine and Dentistry | 3 | 10% |
| Chemistry | 2 | 7% |
| Environmental Science | 1 | 3% |
| Other | 4 | 13% |
| Unknown | 12 | 40% |