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Systematic evaluation of label-free and super-SILAC quantification for proteome expression analysis

MPG-Autoren
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Schaab,  Christoph
Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society;

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Zitation

Tebbe, A., Klammer, M., Sighart, S., Schaab, C., & Daub, H. (2015). Systematic evaluation of label-free and super-SILAC quantification for proteome expression analysis. RAPID COMMUNICATIONS IN MASS SPECTROMETRY, 29(9), 795-801. doi:10.1002/rcm.7160.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0027-A8C7-E
Zusammenfassung
RATIONALE: Advanced implementations of mass spectrometry (MS)-based proteomics allow for comprehensive proteome expression profiling across many biological samples. The outcome of such studies critically depends on accurate and precise quantification, which has to be ensured for high-coverage proteome analysis possible on fast and sensitive mass spectrometers such as quadrupole orbitrap instruments. METHODS: We conducted ultra-high-performance liquid chromatography (UHPLC)/MS experiments on a Q Exactive to systematically compare label-free proteome quantification across six human cancer cell lines with quantification against a shared reference mix generated by stable isotope labeling with amino acids in cell culture (super-SILAC). RESULTS: Single-shot experiments identified on average about 5000 proteins in the label-free compared to about 3500 in super-SILAC experiments. Label-free quantification was slightly less precise than super-SILAC in replicate measurements, verifying previous results obtained for lower proteome coverage. Due to the higher number of quantified proteins, more significant differences were detected in label-free cell line comparisons, whereas a higher percentage of quantified proteins was identified as differentially expressed in super-SILAC experiments. Additional label-free replicate analyses effectively compensated for lower precision of quantification. Finally, peptide fractionation by high pH reversed-phase chromatography prior to LC/MS analysis further increased the robustness and precision of label-free quantification in conjunction with higher proteome coverage. CONCLUSIONS: Our results benchmark and highlight the utility of label-free proteome quantification for applications such as target and biomarker discovery on state-of-the-art UHPLC/MS workflows. Copyright (C) 2015 John Wiley & Sons, Ltd.