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TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics

Nature Methods volume 13, pages 777–783 (2016)Cite this article

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Abstract

Next-generation mass spectrometric (MS) techniques such as SWATH-MS have substantially increased the throughput and reproducibility of proteomic analysis, but ensuring consistent quantification of thousands of peptide analytes across multiple liquid chromatography–tandem MS (LC-MS/MS) runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we developed TRIC (http://proteomics.ethz.ch/tric/), a software tool that utilizes fragment-ion data to perform cross-run alignment, consistent peak-picking and quantification for high-throughput targeted proteomics. TRIC reduced the identification error compared to a state-of-the-art SWATH-MS analysis without alignment by more than threefold at constant recall while correcting for highly nonlinear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups. Thus, TRIC fills a gap in the pipeline for automated analysis of massively parallel targeted proteomics data sets.

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Figure 1: TRIC: alignment algorithm for targeted proteomics data.
Figure 2: Identification and alignment accuracy of TRIC on a validation data set of more than 7,000 manually annotated peak groups.
Figure 3: Analysis of a data set of 12 runs of S. pyogenes exposed to human plasma using TRIC.
Figure 4: Pulsed-SILAC experiment on human iPSCs.
Figure 5: Protein turnover rates in human iPSCs.

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Acknowledgements

We would like to thank the SyBIT project of SystemsX.ch for support and maintenance of the lab-internal computing infrastructure. L. Blum (SyBIT) contributed substantially to making this software available to the lab and provided critical feedback. This work was supported by ETH Zurich (grant ETH-30 11-2 to H.R. and R.A.), the Swiss National Science Foundation (SNSF grants P2EZP3_162268 to H.R. and 31003A_166435 to R.A.), ERC Proteomics v3.0 (AdvG grant 233226 to R.A.), ERC Proteomics4D (AdvG grant 670821 to R.A.), the PhosphonetX project of SystemsX.ch, the ERC DISEASEAVATARS (grant 616441 to R.A. and G.T.), the Telethon Foundation (GGP14265 to G.T.), the Regione Lombardia (Grant Ricerca Indipendente 2012 to G.T.) and the Fondazione Umberto Veronesi (G.D.).

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Authors and Affiliations

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

    Hannes L Röst, Yansheng Liu, Pedro Navarro, George Rosenberger, Ben C Collins, Ludovic Gillet, Lars Malmström & Ruedi Aebersold

  2. Department of Genetics, Stanford University, Stanford, California, USA

    Hannes L Röst

  3. Department of Experimental Oncology, European Institute of Oncology, Milan, Italy

    Giuseppe D'Agostino, Matteo Zanella & Giuseppe Testa

  4. Institute for Immunology, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany

    Pedro Navarro

  5. PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland

    George Rosenberger

  6. Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy

    Giuseppe Testa

  7. S3IT, University of Zurich, Zurich, Switzerland

    Lars Malmström

  8. Faculty of Science, University of Zurich, Zurich, Switzerland

    Ruedi Aebersold

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Contributions

H.L.R. designed and wrote code, performed data analysis and produced the figures. Y.L., G.D. and M.Z. performed iPSC experiments and acquired MS data. P.N. and G.R. contributed to code and provided an initial prototype of the implementation. B.C.C. and L.G. acquired MS data and gave critical input. G.T., L.M. and R.A. designed and supervised the study. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Ruedi Aebersold.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–7 (PDF 2736 kb)

Supplementary Software

msproteomicstools 0.4.3 (ZIP 999 kb)

Supplementary Table 1

Result table describing the manually picked peptides with retention times and peak boundaries. (CSV 5750 kb)

Supplementary Table 2

Significant proteins from the S. pyogenes analysis without any alignment. (CSV 137 kb)

Supplementary Table 3

Significant proteins from the S. pyogenes analysis with TRIC alignment. (CSV 162 kb)

Supplementary Table 4

Degradation rates for the iPSC as determined by SWATH-MS analysis on the peptide level. (CSV 556 kb)

Supplementary Table 5

Degradation rates for the iPSCs as determined by SWATH-MS analysis on the protein level. (CSV 87 kb)

Supplementary Table 6

GO-enrichment analysis using Gorilla. (XLS 32 kb)

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Röst, H., Liu, Y., D'Agostino, G. et al. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat Methods 13, 777–783 (2016). https://doi.org/10.1038/nmeth.3954

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