Abstract
Phosphoproteomics is a powerful platform for the unbiased profiling of kinase-driven signaling pathways. Quantitation of phosphorylation can be performed by means of either labeling or label-free mass spectrometry (MS) methods. Because of their simplicity and universality, label-free methodology is gaining acceptance and popularity in molecular biology research. Analytical workflows for label-free quantification of phosphorylation, however, need to overcome several hurdles for the technique to be accurate and precise. These include the use of biochemical extraction procedures that efficiently and reproducibly isolate phosphopeptides from complex peptide matrices and an analytical strategy that can cope with missing MS/MS phosphopeptide spectra in a subset of the samples being compared. Testing the accuracy of the developed workflows is an essential prerequisite in the analysis of small molecules by MS, and this is achieved by constructing calibration curves to demonstrate linearity of quantification for each analyte. This level of analytical rigor is rarely shown in large-scale quantification of proteins using either label-based or label-free techniques. In this chapter we show an approach to test linearity of quantification of each phosphopeptide quantified by liquid chromatography (LC)-MS without the need to synthesize standards or label proteins. We further describe the appropriate sample handling techniques required for the reproducible recovery of phosphopeptides and explore the essential algorithmic features that enable the handling of missing MS/MS spectra and thus make label-free data suitable for such analyses. The combined technology described in this chapter expands the applicability of phosphoproteomics to questions not previously tractable with other methodologies.
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This work was supported by grants from Barts and the London Charity and Cancer Research UK.
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Wilkes, E., Cutillas, P.R. (2017). Label-Free Phosphoproteomic Approach for Kinase Signaling Analysis. In: Tan, AC., Huang, P. (eds) Kinase Signaling Networks. Methods in Molecular Biology, vol 1636. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7154-1_13
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DOI: https://doi.org/10.1007/978-1-4939-7154-1_13
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