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High-throughput proteomic sample preparation using pressure cycling technology

Nature Protocols volume 17, pages 2307–2325 (2022)Cite this article

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Abstract

High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. Here we describe a detailed protocol of pressure cycling technology (PCT)-assisted sample preparation for proteomic analysis of biopsy tissues. A piece of fresh frozen or formalin-fixed paraffin-embedded tissue weighing ~0.1–2 mg is placed in a 150 μL pressure-resistant tube called a PCT-MicroTube with proper lysis buffer. After closing with a PCT-MicroPestle, a batch of 16 PCT-MicroTubes are placed in a Barocycler, which imposes oscillating pressure to the samples from one atmosphere to up to ~3,000 times atmospheric pressure. The pressure cycling schemes are optimized for tissue lysis and protein digestion, and can be programmed in the Barocycler to allow reproducible, robust and efficient protein extraction and proteolysis digestion for mass spectrometry-based proteomics. This method allows effective preparation of not only fresh frozen and formalin-fixed paraffin-embedded tissue, but also cells, feces and tear strips. It takes ~3 h to process 16 samples in one batch. The resulting peptides can be analyzed by various mass spectrometry-based proteomics methods. We demonstrate the applications of this protocol with mouse kidney tissue and eight types of human tumors.

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Fig. 1: PCT-assisted sample preparation equipment setup.
Fig. 2: Workflow and experimental design for PCT-assisted sample preparation.
Fig. 3: Demo analysis of mouse kidney tissue samples.
Fig. 4: Application of the PCT-assisted sample preparation protocol for human tumors.

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Data availability

The raw MS data of this study have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository40 with the dataset identifier PXD030645. Project accession of the mouse kidney data: IPX0003852001. Project accession of the cancer tissue data: IPX0003852002.

References

  1. Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Zhu, Y., Aebersold, R., Mann, M. & Guo, T. SnapShot: clinical proteomics. Cell 184, 4840–4840.e1 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Xiao, Q. et al. High-throughput proteomics and AI for cancer biomarker discovery. Adv. Drug Deliv. Rev. 176, 113844 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhu, Y. et al. Identification of protein abundance changes in hepatocellular carcinoma tissues using PCT-SWATH. Proteomics Clin. Appl. 13, 1700179 (2019).

    Article  Google Scholar 

  6. Eckert, M. A. et al. Proteomics reveals NNMT as a master metabolic regulator of cancer-associated fibroblasts. Nature 569, 723–728 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hood, B. L. et al. Proteomic analysis of formalin-fixed prostate cancer tissue. Mol. Cell Proteomics 4, 1741–1753 (2005).

    Article  CAS  PubMed  Google Scholar 

  8. Nie, X. et al. Multi-organ proteomic landscape of COVID-19 autopsies. Cell 184, 775–791.e14 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wisniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. Hwang, S. I. et al. Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues. Oncogene 26, 65–76 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Hughes, C. S. et al. Ultrasensitive proteome analysis using paramagnetic bead technology. Mol. Syst. Biol. 10, 757 (2014).

    Article  PubMed  Google Scholar 

  12. Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Fowler, C. B. et al. Elevated hydrostatic pressure promotes protein recovery from formalin-fixed, paraffin-embedded tissue surrogates. Lab Invest. 88, 185–195 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. 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  PubMed  Google Scholar 

  15. Shao, S. et al. Minimal sample requirement for highly multiplexed protein quantification in cell lines and tissues by PCT-SWATH mass spectrometry. Proteomics 15, 3711–3721 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Shao, S. et al. Reproducible tissue homogenization and protein extraction for quantitative proteomics using MicroPestle-assisted pressure-cycling technology. J. Proteome Res. 15, 1821–1829 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

    Article  CAS  PubMed  Google Scholar 

  18. Ross, P. L. et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell Proteomics 3, 1154–1169 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Anderson, L. & Hunter, C. L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell Proteomics 5, 573–588 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S. & Coon, J. J. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol. Cell Proteomics 11, 1475–1488 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Gallien, S. et al. Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol. Cell Proteomics 11, 1709–1723 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Venable, J. D., Dong, M. Q., Wohlschlegel, J., Dillin, A. & Yates, J. R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. Powell, K. Technology to watch in 2018. Nature 553, 531–534 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Zhang, F., Ge, W., Ruan, G., Cai, X. & Guo, T. Data-independent acquisition mass spectrometry-based proteomics and software tools: a glimpse in 2020. Proteomics 20, 1900276 (2021).

    Article  Google Scholar 

  25. Gao, H. et al. Accelerated lysis and proteolytic digestion of biopsy-level fresh-frozen and FFPE tissue samples using pressure cycling technology. J. Proteome Res. 19, 1982–1990 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Zhu, Y. & Guo, T. High-throughput proteomic analysis of fresh-frozen biopsy tissue samples using pressure cycling technology coupled with SWATH Mass spectrometry. Methods Mol. Biol. 1788, 279–287 (2018).

    Article  CAS  PubMed  Google Scholar 

  27. Zhu, Y. et al. High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification. Mol. Oncol. 13, 2305–2328 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhu, T. et al. DPHL: a DIA pan-human protein mass spectrometry library for robust biomarker discovery. Genomics Proteomics Bioinformatics 18, 104–119 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ge, W. et al. Computational optimization of spectral library size improves DIA-MS proteome coverage and applications to 15 tumors. J. Proteome Res. 20, 5392–5401 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. Cheng, Y., Chen, Y. & Yu, C. Fast and efficient non-reduced Lys-C digest using pressure cycling technology for antibody disulfide mapping by LC–MS. J. Pharm. Biomed. Anal. 129, 203–209 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Nie, S., Greer, T., Huang, X., Zheng, X. & Li, N. Development of a simple non-reduced peptide mapping method that prevents disulfide scrambling of mAbs without affecting tryptic enzyme activity. J. Pharm. Biomed. Anal. 209, 114541 (2022).

    Article  CAS  PubMed  Google Scholar 

  32. Huang, Y., Burchmore, R., Jonsson, N. N., Johnson, P. C. D. & Eckersall, P. D. Technical report: In-gel sample preparation prior to proteomic analysis of bovine faeces increases protein identifications by removal of high molecular weight glycoproteins. J. Proteomics 261, 104573 (2022).

    Article  CAS  PubMed  Google Scholar 

  33. Wu, C. et al. Coupling suspension trapping-based sample preparation and data-independent acquisition mass spectrometry for sensitive exosomal proteomic analysis. Anal. Bioanal. Chem. 414, 2585–2595 (2022).

    Article  CAS  PubMed  Google Scholar 

  34. Xuan, Y. et al. Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies. Nat. Commun. 11, 5248 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hunt, A. L. et al. Extensive three-dimensional intratumor proteomic heterogeneity revealed by multiregion sampling in high-grade serous ovarian tumor specimens. iScience 24, 102757 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lee, S. et al. Molecular analysis of clinically defined subsets of high-grade serous ovarian cancer. Cell Rep. 31, 107502 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Li, D. et al. pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry. Bioinformatics 21, 3049–3050 (2005).

    Article  CAS  PubMed  Google Scholar 

  38. Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. Cai, X. et al. PulseDIA: data-independent acquisition mass spectrometry using multi-injection pulsed gas-phase fractionation. J. Proteome Res. 20, 279–288 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Ma, J. et al. iProX: an integrated proteome resource. Nucleic Acids Res. 47, D1211–D1217 (2019).

    Article  PubMed  Google Scholar 

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Acknowledgements

This work is supported by grants from the National Key R&D Program of China (2021YFA1301601 and 2020YFE0202200), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107).

Author information

Authors and Affiliations

  1. Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China

    Xue Cai, Zhangzhi Xue, Rui Sun, Liujia Qian, Liang Yue, Yi Zhu & Tiannan Guo

  2. Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China

    Xue Cai, Zhangzhi Xue, Rui Sun, Liujia Qian, Liang Yue, Yi Zhu & Tiannan Guo

  3. Westlake Omics (Hangzhou) Biotechnology Co., Ltd. No.1, Hangzhou, China

    Chunlong Wu, Weigang Ge, Xiao Yi, Wei Liu, Chen Chen, Huanhuan Gao, Jing Yu & Luang Xu

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Contributions

T.G. and X.C. designed the studies. X.C., X.Y., W.L., C.C., H.G., J.Y. and L.X. participated in the development of this protocol. X.C., C.L., R.S, L.Q. and L.Y. performed the PCT-based sample preparation. X.C performed the LC–MS/MS analysis. X.C., Z.X. and W.G. analyzed the data. X.C., T.G. and Y.Z. wrote the manuscript with input from all co-authors. T.G. and Y.Z. supervised the project.

Corresponding authors

Correspondence to Yi Zhu or Tiannan Guo.

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Competing interests

Y.Z. and T.G. are shareholders of Westlake Omics Inc. C.W., W.G., X.Y., W.L., C.C., H.G., J.Y. and L.X. are employees of Westlake Omics Inc. The other authors declare no competing interests.

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Nature Protocols thanks Louise Bundgaard and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Guo, T. et al. Nat. Med. 21, 407–413 (2015): https://doi.org/10.1038/nm.3807

Zhu, Y. et al. Mol. Oncol. 13, 2305–2328 (2019): https://doi.org/10.1002/1878-0261.12570

Shao, W. et al. Nat. Commun. 10, 2524 (2019): https://doi.org/10.1038/s41467-019-10513-5

Charmpi, K. et al. Genome Biol. 21, 302 (2020): https://doi.org/10.1186/s13059-020-02188-9

Nie, X. et al. Cell 184, 775–791.e714 (2021): https://doi.org/10.1016/j.cell.2021.01.004

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Supplementary Data 1

Experimental data from Fig. 2. Proteomic data of mouse kidney samples in the form of FF, FFPE punch and FFPE slice.

Supplementary Data 2

Experimental data from Fig. 3. Proteomic data of 32 independent FFPE tissue samples (benign and tumor pairs) from eight types of cancer tissues.

Supplementary Data 3

PulseDIA-PASEF MS isolation window: part 1 setting.

Supplementary Data 4

PulseDIA-PASEF MS isolation window: part 2 setting.

Supplementary Video

The procedures for handling the PCT-MicroTubes, MicroCaps, MicroPestles with the PCT tool.

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Cai, X., Xue, Z., Wu, C. et al. High-throughput proteomic sample preparation using pressure cycling technology. Nat Protoc 17, 2307–2325 (2022). https://doi.org/10.1038/s41596-022-00727-1

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