Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps


Clinical specimens are each inherently unique, limited and nonrenewable. Small samples such as tissue biopsies are often completely consumed after a limited number of analyses. Here we present a method that enables fast and reproducible conversion of a small amount of tissue (approximating the quantity obtained by a biopsy) into a single, permanent digital file representing the mass spectrometry (MS)-measurable proteome of the sample. The method combines pressure cycling technology (PCT) and sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. The resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples. We used this method to process and convert 18 biopsy samples from nine patients with renal cell carcinoma into SWATH-MS fragment ion maps. From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples. The measured proteins clearly distinguished tumorous kidney tissues from healthy tissues and differentiated distinct histomorphological kidney cancer subtypes.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: PCT-SWATH method flow chart.
Figure 2: Yield and efficiency of PCT-assisted tissue lysis and protein digestion.
Figure 3: Reproducibility of PCT-SWATH.
Figure 4: Protein markers for RCC quantified in SWATH-MS maps.
Figure 5: Variation analysis of human kidney tissue proteomes by SWATH-MS.
Figure 6: Comparative proteomic analysis of clear cell RCC tissues.


  1. Liotta, L. & Petricoin, E. Molecular profiling of human cancer. Nat. Rev. Genet. 1, 48–56 (2000).

    Article  CAS  Google Scholar 

  2. van 't Veer, L.J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    Article  CAS  Google Scholar 

  3. Sotiriou, C. & Pusztai, L. Gene-expression signatures in breast cancer. N. Engl. J. Med. 360, 790–800 (2009).

    Article  CAS  Google Scholar 

  4. Barrett, T. et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 37, D885–D890 (2009).

    Article  CAS  Google Scholar 

  5. Forbes, S.A. et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015).

    Article  CAS  Google Scholar 

  6. Cancer Genome Atlas Research Network, Weinstein, J.N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article  Google Scholar 

  7. Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005).

    Article  CAS  Google Scholar 

  8. Sreekumar, A. et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910–914 (2009).

    Article  CAS  Google Scholar 

  9. Kononen, J. et al. Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4, 844–847 (1998).

    Article  CAS  Google Scholar 

  10. Haab, B.B. Antibody arrays in cancer research. Mol. Cell Proteomics 4, 377–383 (2005).

    Article  CAS  Google Scholar 

  11. Domon, B. & Aebersold, R. Options and considerations when selecting a quantitative proteomics strategy. Nat. Biotechnol. 28, 710–721 (2010).

    Article  CAS  Google Scholar 

  12. Gillet, L.C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).

    Article  Google Scholar 

  13. Liu, Y. et al. Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS. Proteomics 13, 1247–1256 (2013).

    Article  CAS  Google Scholar 

  14. Collins, B.C. et al. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14–3-3 system. Nat. Methods 10, 1246–1253 (2013).

    Article  CAS  Google Scholar 

  15. Powell, B.S., Lazarev, A.V., Carlson, G., Ivanov, A.R. & Rozak, D.A. Pressure cycling technology in systems biology. Methods Mol. Biol. 881, 27–62 (2012).

    Article  CAS  Google Scholar 

  16. López-Ferrer, D. et al. Application of pressurized solvents for ultrafast trypsin hydrolysis in proteomics: proteomics on the fly. J. Proteome Res. 7, 3276–3281 (2008).

    Article  Google Scholar 

  17. Glatter, T. et al. Large-scale quantitative assessment of different in-solution protein digestion protocols reveals superior cleavage efficiency of tandem Lys-C/trypsin proteolysis over trypsin digestion. J. Proteome Res. 11, 5145–5156 (2012).

    Article  CAS  Google Scholar 

  18. Röst, H. et al. OpenSWATH: Automated, targeted analysis of mass spectrometric data generated by data-independent acquisition. Nat. Biotechnol. 32, 219–223 (2014).

    Article  Google Scholar 

  19. Rosenberger, G., Ludwig, C., Rost, H.L., Aebersold, R. & Malmstrom, L. aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data. Bioinformatics 30, 2511–2513 (2014).

    Article  CAS  Google Scholar 

  20. Picotti, P., Bodenmiller, B., Mueller, L.N., Domon, B. & Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138, 795–806 (2009).

    Article  CAS  Google Scholar 

  21. Algaba, F. et al. Current pathology keys of renal cell carcinoma. Eur. Urol. 60, 634–643 (2011).

    Article  Google Scholar 

  22. Tan, P.H. et al. Renal tumors: diagnostic and prognostic biomarkers. Am. J. Surg. Pathol. 37, 1518–1531 (2013).

    Article  Google Scholar 

  23. Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell Biol. 6, 577–583 (2005).

    Article  CAS  Google Scholar 

  24. Beck, M. et al. The quantitative proteome of a human cell line. Mol. Syst. Biol. 7, 549 (2011).

    Article  Google Scholar 

  25. WiS´niewski, J.R. et al. Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma. Mol. Syst. Biol. 8, 611 (2012).

    Article  Google Scholar 

  26. Addona, T.A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).

    Article  CAS  Google Scholar 

  27. 't Hoen, P.A. et al. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat. Biotechnol. 31, 1015–1022 (2013).

    Article  CAS  Google Scholar 

  28. Sabidó, E. et al. Targeted proteomics reveals strain-specific changes in the mouse insulin and central metabolic pathways after a sustained high-fat diet. Mol. Syst. Biol. 16, 681 (2013).

    Article  Google Scholar 

  29. Escher, C. et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111–1121 (2012).

    Article  CAS  Google Scholar 

  30. Leitner, A. et al. Expanding the chemical cross-linking toolbox by the use of multiple proteases and enrichment by size exclusion chromatography. Mol. Cell. Proteomics 11, M111.014126 (2012).

    Article  Google Scholar 

  31. MacLean, B., Eng, J.K., Beavis, R.C. & McIntosh, M. General framework for developing and evaluating database scoring algorithms using the TANDEM search engine. Bioinformatics 22, 2830–2832 (2006).

    Article  CAS  Google Scholar 

  32. Geer, L.Y. et al. Open mass spectrometry search algorithm. J. Proteome Res. 3, 958–964 (2004).

    Article  CAS  Google Scholar 

  33. Deutsch, E.W. et al. A guided tour of the Trans-Proteomic Pipeline. Proteomics 10, 1150–1159 (2010).

    Article  CAS  Google Scholar 

  34. Kunszt, P. et al. iPortal: the Swiss Grid Proteomics Portal: requirements and new features based on experience and usability considerations. Concurrency and Computation: Practice and Experience 27, 433–445 (2015).

    Article  Google Scholar 

  35. Kessner, D., Chambers, M., Burke, R., Agus, D. & Mallick, P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534–2536 (2008).

    Article  CAS  Google Scholar 

  36. Sturm, M. et al. OpenMS: an open-source software framework for mass spectrometry. BMC Bioinformatics 9, 163 (2008).

    Article  Google Scholar 

  37. Vizcaíno, J.A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2013).

    Article  Google Scholar 

  38. Huber, W., von Heydebreck, A., Sultmann, H., Poustka, A. & Vingron, M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18 (suppl. 1), S96–S104 (2002).

    Article  Google Scholar 

  39. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  Google Scholar 

Download references


We thank N. Roesch and M. Stoffel for providing test mouse tissues, H.P. Schmid and D. Engeler for their help with human tissue collection, A. Leitner and K. Novy for help in Orbitrap analysis, A. Leitner for assistance in setting up the Barocycler, the ETH Brutus team for computational support, and O.L. Kon for critical reading of the manuscript. The work was supported by the project PhosphoNetX (to R.A.), the Swiss National Science Foundation (grant no. 3100A0-688 107679 to R.A.), and the European Research Council (grant no. ERC-2008-AdG 233226 to R.A.). P.K. was supported by a fellowship from the Finnish Cultural Foundation. We thank the PRIDE team for support in mass spectrometry data deposition.

Author information

Authors and Affiliations



R.A. conceived the idea. T.G. developed the method. S.G., M.J. and W.J. designed the clinical cohort and collected the clinical tissue samples. T.G., P.K. and C.C.K. performed the analysis of the tissues. L.C.G. and T.G. performed the MS measurements. T.G. performed the data analysis, with critical inputs from W.E.W., C.C.K., H.L.R., G.R., B.C.C. and L.C.B. T.G. and R.A. wrote the manuscript. C.C.K., M.J. and all the other authors contributed to the revision of the manuscript. R.A. supervised the project.

Corresponding author

Correspondence to Ruedi Aebersold.

Ethics declarations

Competing interests

R.A. holds shares of Biognosys AG, which operates in the field covered by the article. The research group of R.A. is supported by AB SCIEX, which provides access to prototype instrumentation, and Pressure Biosciences, which provides access to advanced sample preparation instrumentation.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3 (PDF 440 kb)

Supplementary Table 1

Protein quantification of 12 test kidney tissues (XLS 1002 kb)

Supplementary Table 2

Clinicopathological characteristics of renal cell carcinomas (XLS 25 kb)

Supplementary Table 3

Protein quantification of 9 paired kidney biopsies analyzed in duplicates (XLS 1351 kb)

Supplementary Table 4

Significantly regulated proteins between tumorous and nontumorous biopsies from six ccRCC patients (XLS 238 kb)

Supplementary Table 5

Significantly regulated proteins between tumorous ccRCC and pRCC (XLS 175 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guo, T., Kouvonen, P., Koh, C. et al. Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps. Nat Med 21, 407–413 (2015).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing