During the last decade, research on plasma membrane focused increasingly on the analysis of so-called microdomains. It has been shown that function of many membrane-associated proteins involved in signaling and transport depends on their conditional segregation within sterol-enriched membrane domains. High throughput proteomic analysis of sterol-protein interactions are often based on analyzing detergent resistant membrane fraction enriched in sterols and associated proteins, which also contain proteins from these microdomain structures. Most studies so far focused exclusively on the characterization of detergent resistant membrane protein composition and abundances. This approach has received some criticism because of its unspecificity and many co-purifying proteins. In this study, by a label-free quantitation approach, we extended the characterization of membrane microdomains by particularly studying distributions of each protein between detergent resistant membrane and detergent-soluble fractions (DSF). This approach allows a more stringent definition of dynamic processes between different membrane phases and provides a means of identification of co-purifying proteins. We developed a random sampling algorithm, called Unicorn, allowing for robust statistical testing of alterations in the protein distribution ratios of the two different fractions. Unicorn was validated on proteomic data from methyl-â-cyclodextrin treated plasma membranes and the sterol biosynthesis mutant smt1. Both, chemical treatment and sterol-biosynthesis mutation affected similar protein classes in their membrane phase distribution and particularly proteins with signaling and transport functions.
Algorithms Arabidopsis