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Super-SILAC mix for quantitative proteomics of human tumor tissue

Abstract

We describe a method to accurately quantify human tumor proteomes by combining a mixture of five stable-isotope labeling by amino acids in cell culture (SILAC)-labeled cell lines with human carcinoma tissue. This generated hundreds of thousands of isotopically labeled peptides in appropriate amounts to serve as internal standards for mass spectrometry–based analysis. By decoupling the labeling from the measurement, this super-SILAC method broadens the scope of SILAC-based proteomics.

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Figure 1: Quantification of tumor tissue with one SILAC-labeled cell type versus super-SILAC mix.
Figure 2: Cancer-related proteins analyzed with the super-SILAC mix as internal standard.

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Acknowledgements

We thank colleagues at the Department for Proteomics and Signal Transduction for assistance and fruitful discussion, and P. Ziolkowski (Department of Pathology, Wroclaw Medical University, Poland) for kindly providing us tumor tissues. T.G. is supported by a fellowship from the Humboldt Foundation. This work was partially funded by the European Commission's 7th Framework Program (grant agreement health-F4-2008-201648/PROSPECTS).

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Authors and Affiliations

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Contributions

T.G. designed and performed the experiments, analyzed the data and wrote the paper. J.C. developed analytical tools and assisted in data analysis. P.O. analyzed the tissue samples pathologically and assisted in data interpretation. J.R.W. developed experimental tools and provided tumor samples. M.M. conceived the super-SILAC idea, supervised the work and wrote the paper.

Corresponding author

Correspondence to Matthias Mann.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Tables 1–6 (PDF 2394 kb)

Supplementary Table 7

Complete lists of identified proteins (XLS 5842 kb)

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Geiger, T., Cox, J., Ostasiewicz, P. et al. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7, 383–385 (2010). https://doi.org/10.1038/nmeth.1446

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