Chapter title |
Quantitative Proteomics of Secreted Proteins
|
---|---|
Chapter number | 14 |
Book title |
Innate Immune Activation
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7519-8_14 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7518-1, 978-1-4939-7519-8
|
Authors |
Annika Frauenstein, Felix Meissner |
Abstract |
Secreted proteins such as cytokines, interleukins, growth factors, and hormones have pleiotropic functions and facilitate intercellular communication in organisms. Quantification of these proteins conventionally relies on antibody-based methods, i.e., enzyme-linked immunosorbent assays (ELISA), whose large-scale use is limited by availability, specificity, and affordability.Here, we describe an experimental and bioinformatics workflow to comprehensively quantify cellular protein secretion by mass spectrometry. Secreted proteins are collected in vitro or ex vivo, digested with proteases and the resulting peptide mixtures are analyzed in single liquid chromatography-mass spectrometry (LC-MS/MS) runs. Label-free quantification and bioinformatics analysis is conducted in the MaxQuant and Perseus computational environment. Our workflow allows the quantification of thousands of secreted proteins spanning a concentration range of four orders of magnitude and permits the systems-level characterization of secretory programs as well as the discovery of proteins with unexpected extracellular functions. |
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Unknown | 2 | 100% |
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 | 42 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 7 | 17% |
Student > Master | 7 | 17% |
Student > Ph. D. Student | 6 | 14% |
Student > Bachelor | 4 | 10% |
Professor > Associate Professor | 3 | 7% |
Other | 3 | 7% |
Unknown | 12 | 29% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 14 | 33% |
Agricultural and Biological Sciences | 3 | 7% |
Immunology and Microbiology | 3 | 7% |
Chemistry | 2 | 5% |
Neuroscience | 2 | 5% |
Other | 4 | 10% |
Unknown | 14 | 33% |