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Building high-quality assay libraries for targeted analysis of SWATH MS data

Nature Protocols volume 10, pages 426–441 (2015)Cite this article

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Abstract

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2–3 d to complete, depending on the extent of the library and the computational resources available.

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Figure 1: Workflow for SWATH assay library generation.
Figure 2: Splitting peptide identifications with distant elution times.

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Acknowledgements

We thank C. Ludwig and S. Bader for their discussions and feedback on the manuscript, L. Blum for implementation of iProphet support in MAYU, H. Röst for packaging msproteomicstools, J. Slagel for including the qtofpeakpicker and the new MAYU version in the TPP, and the PRIDE Team for maintaining the ProteomeXchange platform. This work has been financially supported by the Framework Programme 7 of the European Commission through SysteMTb (241587), UNICELLSYS (201142), PRIME-XS (262067) and ProteomeXchange (260558), a European Research Council advanced grant Proteomics v3.0 (233226), the Federal Ministry of Education and Research (e:Bio Express2Present, 0316179C) and the Forschungszentrum Immunologie of the University Medical Center Mainz.

Author information

Author notes

  1. Olga T Schubert, Ludovic C Gillet and Ben C Collins: These authors contributed equally to this work.

Authors and Affiliations

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

    Olga T Schubert, Ludovic C Gillet, Ben C Collins, George Rosenberger, Witold E Wolski & Ruedi Aebersold

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

    Olga T Schubert & George Rosenberger

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

    Pedro Navarro

  4. SystemsX.ch Biology IT (SyBIT), SystemsX.ch, Zurich, Switzerland

    Witold E Wolski

  5. Division of Biomedical Engineering and Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China

    Henry Lam

  6. Department of Radiology, Stanford University School of Medicine, Stanford, California, USA

    Dario Amodei & Parag Mallick

  7. Department of Genome Sciences, University of Washington, Seattle, Washington, USA

    Brendan MacLean

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

    Ruedi Aebersold

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  1. Olga T Schubert
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Contributions

O.T.S., L.C.G. and B.C.C. developed the workflow and wrote the manuscript; P.N. developed the tools spectrast2tsv.py and spectrast_cluster.py; G.R. and H.L. developed and implemented the retention time normalization and iRT calibration in SpectraST; W.E.W. developed the qtofpeakpicker; D.A., P.M. and B.M. implemented automated SWATH library import into Skyline; and R.A. directed the project and contributed to writing the manuscript.

Corresponding author

Correspondence to Ruedi Aebersold.

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Competing interests

R.A. holds shares of Biognosys AG, which operates in the field covered by the article (products are Spectronaut software and iRT-kit).

Integrated supplementary information

Supplementary Figure 1 Comparison of converters.

(a) Violin plots show the intrinsic fragment ion spectrum variability of three yeast sample injections converted with three DDA centroiding algorithms.

(b) Violin plots show how well the relative intensities of the six most intense fragment ions obtained by a certain centroiding algorithm compare to the relative intensities of the same six fragments in SWATH MS data.

The spectrum similarity was determined using the normalised spectral contrast angle as described by Toprak and co-workers41. N indicates the number of pairwise comparisons of fragment ion spectra for the same peptide precursors between each data file and S indicates the estimated level of dissimilarity obtained by comparing the experimental score distribution to the score distribution of a perturbation benchmark dataset (the lower S, the more similar the spectra).

(c) Depending on the centroiding algorithm implemented in the different peak pickers, the number of peptide and protein identifications from a database search vary slightly (after filtering for protein-level FDR=1%). The size of the circles is proportional to the number of identifications.

Supplementary Figure 2 MAYU-estimated FDRs with respect to iProphet probability thresholds.

The software MAYU estimates the FDRs on PSM (mFDR), peptide (pepFDR), and protein (protFDR) level with respect to the applied iProphet probability threshold. The data is based on the case study described in the protocol, which consists of three whole cell lysates of yeast. As comparison the protein FDR of a very large data set of 331 runs of different human cells and tissues is shown (Pan Human Library as described by Rosenberger and colleagues23).

Supplementary Figure 3 Comparison of consensus and best replicate spectral libraries.

a) Violin plots show the intrinsic fragment ion spectrum variability of three injections of a yeast sample converted with the qtofpeakpicker and summarised using either the best replicate fragment ion spectrum or consensus algorithm implemented in SpectraST11.

(b) Violin plots show how well the relative intensities of the six most intense fragment ions compare to the relative intensities of the same six fragments in SWATH MS data.

The spectrum similarity was determined using the normalised spectral contrast angle as described by Toprak and co-workers41. N indicates the number of pairwise comparisons of fragment ion spectra for the same peptide precursors between each data file and S indicates the estimated level of dissimilarity obtained by comparing the experimental score distribution to the score distribution of a perturbation benchmark dataset (the lower S, the more similar the spectra). Common neutral losses were included in the comparison.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Notes 1–5, Supplementary Tables 1–6 and Supplementary Tutorial (PDF 6958 kb)

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Schubert, O., Gillet, L., Collins, B. et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc 10, 426–441 (2015). https://doi.org/10.1038/nprot.2015.015

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