Mazet, F., Dunster, J. L.
ORCID: https://orcid.org/0000-0001-8986-4902, Jones, C. I.
ORCID: https://orcid.org/0000-0001-7537-1509, Vaiyapuri, S.
ORCID: https://orcid.org/0000-0002-6006-6517, Tindall, M. J., Fry, M. J. and Gibbins, J. M.
ORCID: https://orcid.org/0000-0002-0372-5352
(2015)
A high-density immunoblotting methodology for quantification of total protein levels and phosphorylation modifications.
Scientific Reports, 5.
16995.
ISSN 2045-2322
doi: 10.1038/srep16995
Abstract/Summary
The components of many signaling pathways have been identified and there is now a need to conduct quantitative data-rich temporal experiments for systems biology and modeling approaches to better understand pathway dynamics and regulation. Here we present a modified Western blotting method that allows the rapid and reproducible quantification and analysis of hundreds of data points per day on proteins and their phosphorylation state at individual sites. The approach is of particular use where samples show a high degree of sample-to-sample variability such as primary cells from multiple donors. We present a case study on the analysis of >800 phosphorylation data points from three phosphorylation sites in three signaling proteins over multiple time points from platelets isolated from ten donors, demonstrating the technique's potential to determine kinetic and regulatory information from limited cell numbers and to investigate signaling variation within a population. We envisage the approach being of use in the analysis of many cellular processes such as signaling pathway dynamics to identify regulatory feedback loops and the investigation of potential drug/inhibitor responses, using primary cells and tissues, to generate information about how a cell's physiological state changes over time.
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| Item Type | Article |
| URI | https://reading-clone.eprints-hosting.org/id/eprint/47625 |
| Identification Number/DOI | 10.1038/srep16995 |
| Refereed | Yes |
| Divisions | Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR) Life Sciences > School of Biological Sciences > Biomedical Sciences Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics |
| Publisher | Nature Publishing Group |
| Download/View statistics | View download statistics for this item |
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