Quantitative Phosphoproteomic Analysis of T-Cell Receptor Signaling

Part of the Methods in Molecular Biology book series (MIMB, volume 1584)


TCR signaling critically depends on protein phosphorylation across many proteins. Localization of each phosphorylation event relative to the T-cell receptor (TCR) and canonical T-cell signaling proteins will provide clues about the structure of TCR signaling networks. Quantitative phosphoproteomic analysis by mass spectrometry provides a wide-scale view of cellular phosphorylation networks. However, analysis of phosphorylation by mass spectrometry is still challenging due to the relative low abundance of phosphorylated proteins relative to all proteins and the extraordinary diversity of phosphorylation sites across the proteome. Highly selective enrichment of phosphorylated peptides is essential to provide the most comprehensive view of the phosphoproteome. Optimization of phosphopeptide enrichment methods coupled with highly sensitive mass spectrometry workflows significantly improves the sequencing depth of the phosphoproteome to over 10,000 unique phosphorylation sites from complex cell lysates. Here we describe a step-by-step method for phosphoproteomic analysis that has achieved widespread success for identification of serine, threonine, and tyrosine phosphorylation. Reproducible quantification of relative phosphopeptide abundance is provided by intensity-based label-free quantitation. An ideal set of mass spectrometry analysis parameters is also provided that optimize the yield of identified sites. We also provide guidelines for the bioinformatic analysis of this type of data to assess the quality of the data and to comply with proteomic data reporting requirements.

Key words

Immunoaffinity purification Label-free quantitation Phosphoproteomics T-Cell signaling Tyrosine phosphorylation Mass spectrometry 



The authors wish to acknowledge the financial support from NIH grant R01 AI083636 and NIH grant P30 GM110759. In addition, this research is based in part upon work conducted using the Rhode Island NSF/EPSCoR Proteomics Share Resource Facility, which is supported in part by the National Science Foundation EPSCoR Grant No. 1004057, National Institutes of Health Grant No. 1S10RR027027, a Rhode Island Science and Technology Advisory Council grant, and the Division of Biology and Medicine, Brown University.Conflict of Interest: The authors declare no conflict of interest.


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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Division of Biology and Medicine, Alpert Medical SchoolBrown UniversityProvidenceUSA
  2. 2.Center for Cancer Research and Development, Proteomics Core FacilityRhode Island HospitalProvidenceUSA
  3. 3.Department of Molecular Biology, Cell Biology, and BiochemistryBrown UniversityProvidenceUSA

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