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Improved surfaceome coverage with a label-free nonaffinity-purified workflow.

ABSTRACT: The proteins of the cellular plasma membrane (PM) perform important functions relating to homeostasis and intercellular communication. Due to its overall low cellular abundance, amphipathic character, and low membrane-to-cytoplasm ratio, the PM proteome has been challenging to isolate and characterize, and is poorly represented in standard LC-MS/MS analyses. In this study, we employ sucrose gradient ultracentrifugation for the enrichment of the PM proteome, without chemical labeling and affinity purification, together with GeLCMS and use subsequent bioinformatics tools to select proteins associated with the PM/cell surface, herein referred to as the surfaceome. Using this methodology, we identify over 1900 cell surface associated proteins in a human acute myeloid leukemia cell line. These surface proteins comprise almost 50% of all detected cellular proteins, a number that substantially exceeds the depth of coverage in previously published studies describing the leukemia surfaceome.

SUBMITTER: Glisovic-Aplenc T

PROVIDER: S-EPMC5518111 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Improved surfaceome coverage with a label-free nonaffinity-purified workflow.

Glisovic-Aplenc Tina T Gill Saar S Spruce Lynn A LA Smith Ian R IR Fazelinia Hossein H Shestova Olga O Ding Hua H Tasian Sarah K SK Aplenc Richard R Seeholzer Steven H SH

Proteomics 20170306 7

The proteins of the cellular plasma membrane (PM) perform important functions relating to homeostasis and intercellular communication. Due to its overall low cellular abundance, amphipathic character, and low membrane-to-cytoplasm ratio, the PM proteome has been challenging to isolate and characterize, and is poorly represented in standard LC-MS/MS analyses. In this study, we employ sucrose gradient ultracentrifugation for the enrichment of the PM proteome, without chemical labeling and affinity ...[more]

PMID: 28116781

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Project description:Normalization is an important step in the analysis of quantitative proteomics data. If this step is ignored, systematic biases can lead to incorrect assumptions about regulation. Most statistical procedures for normalizing proteomics data have been borrowed from genomics where their development has focused on the removal of so-called 'batch effects.' In general, a typical normalization step in proteomics works under the assumption that most peptides/proteins do not change; scaling is then used to give a median log-ratio of 0. The focus of this work was to identify other factors, derived from knowledge of the variables in proteomics, which might be used to improve normalization. Here we have examined the multi-laboratory data sets from Phase I of the NCI's CPTAC program. Surprisingly, the most important bias variables affecting peptide intensities within labs were retention time and charge state. The magnitude of these observations was exaggerated in samples of unequal concentrations or "spike-in" levels, presumably because the average precursor charge for peptides with higher charge state potentials is lower at higher relative sample concentrations. These effects are consistent with reduced protonation during electrospray and demonstrate that the physical properties of the peptides themselves can serve as good reporters of systematic biases. Between labs, retention time, precursor m/z, and peptide length were most commonly the top-ranked bias variables, over the standardly used average intensity (A). A larger set of variables was then used to develop a stepwise normalization procedure. This statistical model was found to perform as well or better on the CPTAC mock biomarker data than other commonly used methods. Furthermore, the method described here does not require a priori knowledge of the systematic biases in a given data set. These improvements can be attributed to the inclusion of variables other than average intensity during normalization.

Dataset Information

Improved surfaceome coverage with a label-free nonaffinity-purified workflow.

Publications

Improved surfaceome coverage with a label-free nonaffinity-purified workflow.

Similar Datasets

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