Improved intra-array and interarray normalization of peptide microarray phosphorylation for phosphorylome and kinome profiling by rational selection of relevant spots

Improved intra-array and interarray normalization of peptide microarray phosphorylation for phosphorylome and kinome profiling by rational selection of relevant spots

Scholma, Jetse (University of Twente)
Fuhler, G.M. (Erasmus MC)
Joore, Jos (Pepscope BV)
Hulsman, M. (TU Delft Pattern Recognition and Bioinformatics; Erasmus MC)
Schivo, Stefano (University of Twente)
List, Alan F. (Lee Moffitt Cancer Center and Research Institute)
Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics)
Peppelenbosch, M.P. (Erasmus MC)
Post, Janine N. (University of Twente)

2016-05-26

Massive parallel analysis using array technology has become the mainstay for analysis of genomes and transcriptomes. Analogously, the predominance of phosphorylation as a regulator of cellular metabolism has fostered the development of peptide arrays of kinase consensus substrates that allow the charting of cellular phosphorylation events (often called kinome profiling). However, whereas the bioinformatical framework for expression array analysis is well-developed, no advanced analysis tools are yet available for kinome profiling. Especially intra-array and interarray normalization of peptide array phosphorylation remain problematic, due to the absence of “housekeeping” kinases and the obvious fallacy of the assumption that different experimental conditions should exhibit equal amounts of kinase activity. Here we describe the development of analysis tools that reliably quantify phosphorylation of peptide arrays and that allow normalization of the signals obtained. We provide a method for intraslide gradient correction and spot quality control. We describe a novel interarray normalization procedure, named repetitive signal enhancement, RSE, which provides a mathematical approach to limit the false negative results occuring with the use of other normalization procedures. Using in silico and biological experiments we show that employing such protocols yields superior insight into cellular physiology as compared to classical analysis tools for kinome profiling

http://resolver.tudelft.nl/uuid:3687ec25-2594-4bf6-b1af-62ef7f19f806

https://doi.org/10.1038/srep26695

2045-2322

Scientific Reports, 1-13

Institutional Repository

journal article

© 2016 Jetse Scholma, G.M. Fuhler, Jos Joore, M. Hulsman, Stefano Schivo, Alan F. List, M.J.T. Reinders, M.P. Peppelenbosch, Janine N. Post