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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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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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).
The authors declare no competing interests.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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 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.
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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