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Ligand-based receptor identification on living cells and tissues using TRICEPS

Nature Protocols volume 8, pages 13211336 (2013) | Download Citation

Abstract

Physiological responses to ligands such as peptides, proteins, pharmaceutical drugs or whole pathogens are generally mediated through interactions with specific cell surface protein receptors. Here we describe the application of TRICEPS, a specifically designed chemoproteomic reagent that can be coupled to a ligand of interest for the subsequent ligand-based capture of corresponding receptors on living cells and tissues. This is achieved by three orthogonal functionalities in TRICEPS—one that enables conjugation to an amino group containing ligands, a second for the ligand-based capture of glycosylated receptors on gently oxidized living cells and a biotin tag for purifying receptor peptides for analysis by quantitative mass spectrometry (MS). Specific receptors for the ligand of interest are identified through quantitative comparison of the identified peptides with a sample generated by a control probe with known (e.g., insulin) or no binding preferences (e.g., TRICEPS quenched with glycine). In combination with powerful statistical models, this ligand-based receptor capture (LRC) technology enables the unbiased and sensitive identification of one or several specific receptors for a given ligand under near-physiological conditions and without the need for genetic manipulations. LRC has been designed for applications with proteins but can easily be adapted for ligands ranging from peptides to intact viruses. In experiments with small ligands that bind to receptors with comparatively large extracellular domains, LRC can also reveal approximate ligand-binding sites owing to the defined spacer length of TRICEPS. Provided that sufficient quantities of the ligand and target cells are available, LRC can be carried out within 1 week.

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Acknowledgements

We greatly acknowledge T. Clough and O. Vitek for help with the statistical data analysis pipeline. We are grateful to A. Hofmann, T. Bock, D. Bausch-Fluck, A. Jacobs and A. Leitner for suggestions and support at all stages of the project. This work was supported by funding from the National Center of Competence in Research Neural Plasticity and Repair (to B.W.), the Swiss National Science Foundation (to B.W.) and SystemsX.ch/ InfectX (to B.W.)

Author information

Affiliations

  1. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.

    • Andreas P Frei
    • , Hansjoerg Moest
    • , Karel Novy
    •  & Bernd Wollscheid
  2. Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.

    • Hansjoerg Moest
    •  & Bernd Wollscheid

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Contributions

A.P.F. and B.W. designed the project and wrote the paper. A.P.F. performed all the experiments and analyzed all the data. H.M. and K.N. contributed ideas. All authors discussed the results and implications and commented on the manuscript at all stages.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Andreas P Frei or Bernd Wollscheid.

Supplementary information

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  1. 1.

    Supplementary Methods

    R script for the statistical analysis of LRC datasets using MSstats

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DOI

https://doi.org/10.1038/nprot.2013.072

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