Brief Communication | Published:

Targeted protein quantification using sparse reference labeling

Nature Methods volume 11, pages 301304 (2014) | Download Citation

Abstract

Targeted proteomics is a method of choice for accurate and high-throughput quantification of predefined sets of proteins. Many workflows use isotope-labeled reference peptides for every target protein, which is time consuming and costly. We report a statistical approach for quantifying full protein panels with a reduced set of reference peptides. This label-sparse approach achieves accurate quantification while reducing experimental cost and time. It is implemented in the software tool SparseQuant.

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Acknowledgements

The work was supported in part by the LiverX program of the Swiss Initiative for Systems Biology (SystemsX) to E.S., by the European Research Council (ERC) via grant “Proteomics v3.0” (grant 233226) and the Swiss National Science Foundation (grant 31-147086) to R.A., and by the US National Science Foundation CAREER award DBI-1054826 to O.V. We thank M. Choi for assistance with the manuscript.

Author information

Author notes

    • Ching-Yun Chang
    •  & Eduard Sabidó

    These authors contributed equally to this work.

Affiliations

  1. Department of Statistics, Purdue University, West Lafayette, Indiana, USA.

    • Ching-Yun Chang
    •  & Olga Vitek
  2. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.

    • Eduard Sabidó
    •  & Ruedi Aebersold
  3. Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, Zurich, Switzerland.

    • Ruedi Aebersold
  4. Faculty of Science, University of Zurich, Zurich, Switzerland.

    • Ruedi Aebersold
  5. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA.

    • Olga Vitek

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Contributions

C.-Y.C. developed and implemented the statistical analysis framework, analyzed the data sets and wrote the manuscript. E.S. designed and conducted the mouse liver study and wrote the manuscript. R.A. supervised the experimental aspects of the work and wrote the manuscript. O.V. designed and supervised the statistical aspects of the work and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Olga Vitek.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Data 1 and Supplementary Notes 1–3

Excel files

  1. 1.

    Supplementary Data 2

    Identified and quantified SRM transitions in the mouse liver study. These data are the input to the label-sparse quantification approach.

Zip files

  1. 1.

    Supplementary Data 3

    The raw data in the Skyline format, generated by the initial screening experiment. These data are evidence that some transitions lack their reference counterpart, and require the label-sparse quantification approach.

  2. 2.

    Supplementary Data 4

    The raw data in the Skyline format, generated by the actual experiment. These data can be used to assess the quality of the individual transitions. These transitions were identified and quantified, and the result is stored in Supplementary Data 2.

  3. 3.

    Supplementary Software

    SparseQuant

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DOI

https://doi.org/10.1038/nmeth.2806

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