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Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes

Nature Protocols volume 15, pages 2341–2386 (2020)Cite this article

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Abstract

Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.

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Fig. 1: Schematic overview of the complex-centric proteome-profiling workflow.
Fig. 2: Quality control plots for SEC fractionation and SWATH-MS data acquisition.
Fig. 3: Data pre-processing plots.
Fig. 4: Parameter selection and protein-centric analysis.
Fig. 5: Exemplary CCprofiler plots for complex-centric data analysis.

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Data availability

The MS data for the HEK293 SEC-SWATH-MS experiment16 is available at ProteomeXchange Consortium PXD007038 (http://proteomecentral.proteomexchange.org). The R-package CCprofiler is available on GitHub at https://github.com/CCprofiler/CCprofiler/.

Code availability

A detailed protocol on how to perform peptide-centric SEC-SWATH-MS data analysis is available on GitHub at https://github.com/CCprofiler/SECSWATH_PeptideCentricAnalysis. A detailed protocol on how to perform complex-centric SEC-SWATH-MS data analysis with the CCprofiler package as well as example data of our HEK293 experiment are available on GitHub at https://github.com/CCprofiler/SECSWATH_ComplexCentricAnalysis and in the Supplementary CCprofiler manual.

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Acknowledgements

The project was supported by the SystemsX.ch projects PhosphoNetX PPM and project TbX (to R.A.) and the European Research Council (ERC-20140AdG 670821 to R.A.). M.H. was supported by a grant from Institut Mérieux. I.B. was supported by the Swiss National Science Foundation (grant no. 31003A_166435). B.C.C. was supported by a Swiss National Science Foundation Ambizione grant (PZ00P3_161435). A.B.-E. was supported by the National Institutes of Health project Omics4TB Disease Progression (U19 AI106761).

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  1. These authors contributed equally: Isabell Bludau, Moritz Heusel.

Authors and Affiliations

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

    Isabell Bludau, Moritz Heusel, Max Frank, George Rosenberger, Robin Hafen, Amir Banaei-Esfahani, Audrey van Drogen, Ben C. Collins, Matthias Gstaiger & Ruedi Aebersold

  2. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

    Isabell Bludau

  3. Division of Infection Medicine (BMC), Department of Clinical Sciences, Lund University, Lund, Sweden

    Moritz Heusel

  4. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany

    Max Frank

  5. Columbia University, New York, NY, USA

    George Rosenberger

  6. School of Biological Sciences, Queen’s University of Belfast, Belfast, UK

    Ben C. Collins

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

    Ruedi Aebersold

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Contributions

I.B., M.H. and R.A. wrote the manuscript with input from all authors. I.B. and M.H. developed the presented workflow, implemented the analysis scripts and performed all analyses. M.H. developed and optimized the experimental protocol for SEC-SWATH-MS. G.R. and M.H. optimized the peptide-centric analysis for SEC-SWATH-MS applications. I.B., M.H., M.F., G.R., R.H. and A.B.-E. developed the CCprofiler software. A.V.D. performed validation experiments. R.A., M.G., B.C.C. and M.H. conceptualized the primary study. B.C.C., M.G. and R.A. supervised the study.

Corresponding authors

Correspondence to Matthias Gstaiger or Ruedi Aebersold.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Bruno Manadas, Leonard Foster and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Heusel, M. et al. Mol. Syst. Biol. 15, e8438 (2019): https://doi.org/10.15252/msb.20188438

Heusel, M. et al. Cell Syst. 10, 133–155.e6 (2020): https://doi.org/10.1016/j.cels.2020.01.001

Key data used in this protocol

Heusel, M. et al. Mol. Syst. Biol. 15, e8438 (2019): https://doi.org/10.15252/msb.20188438

Extended data

Extended Data Fig. 1 Overlap of proteins and protein complexes across the SEC fractionation dimension.

a, Heat-map representation of the percentage of detected proteins that are shared between each pair of SEC fractions (top). The percentage overlap is calculated as the number of shared proteins relative to the total set of proteins detected in any pair of SEC fractions, as a percentage. The bottom panel illustrates the average percentage of overlapping proteins at different distance thresholds between SEC fractions. b, Heat-map representation of the percentage of detected protein complexes that are shared between each pair of SEC fractions (top). The percentage overlap is calculated as the number of shared protein complexes relative to the total set of protein complexes detected in any pair of SEC fractions, as a percentage. The bottom panel illustrates the average percentage of overlapping protein complexes at different distance thresholds between SEC fractions.

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Supplementary CCprofiler Manual.

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Bludau, I., Heusel, M., Frank, M. et al. Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes. Nat Protoc 15, 2341–2386 (2020). https://doi.org/10.1038/s41596-020-0332-6

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