Skip to main content
SpringerLink
Log in

Label-free quantification of differentially expressed proteins in mouse liver cancer cells with high and low metastasis rates by a SWATH acquisition method

  • Articles
  • Published:
Science China Chemistry Aims and scope Submit manuscript

Abstract

Label-free quantification is a valuable tool for the analysis of differentially expressed proteins identified by mass spectrometry methods. Herein, we used a new strategy: data-dependent acquisition mode identification combined with label-free quantification by SWATH acquisition mode, to study the differentially expressed proteins in mouse liver cancer metastasis cells. A total of 1528 protein groups were identified, among which 1159 protein groups were quantified and 249 protein groups were observed as differentially expressed proteins (86 proteins up-regulated and 163 down-regulated). This method provides a commendable solution for the identification and quantification of differentially expressed proteins in biological samples.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wolters DA, Washburn MP, Yates JR. An automated multidimen-sional protein identification technology for shotgun proteomics. Anal Chem, 2001, 73: 5683–5690

    Article  CAS  Google Scholar 

  2. Tabb DL, McDonald WH, Yates JR. DTASelect and contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J Proteome Res, 2002, 1: 21–26

    Article  CAS  Google Scholar 

  3. Liu H, Sadygov RG, Yates J. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem, 2004, 76: 4193–201

    Article  CAS  Google Scholar 

  4. Anderson L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics, 2005, 5: 573–588

    Article  Google Scholar 

  5. Stahl-Zeng J, Lange V, Ossola R, Eckhardt K, Krek W, Aebersold R, Domon B. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics, 2007, 6: 1809–1817

    Article  CAS  Google Scholar 

  6. Kuzyk MA, Smith D, Yang J, Cross TJ, Jackson AM, Hardie DB, Anderson NL, Borchers CH. Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Mol Cell Proteomics, 2009, 8: 1860–1877

    Article  CAS  Google Scholar 

  7. Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods, 2004, 1: 39–45

    Article  CAS  Google Scholar 

  8. Purvine S, Eppel JT, Yi EC, Goodlett DR. Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer. Proteomics, 2003, 3: 847–850

    Article  CAS  Google Scholar 

  9. Plumb RS, Johnson KA, Rainville P, Smith BW, Wilson ID, Castro-Perez J, Nicholson J. UPLC/MS(E): a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom, 2006, 20: 1989–1994

    Article  CAS  Google Scholar 

  10. Silva JC, Gorenstein MV, Li GZ, Vissers JP, Geromanos SJ. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol Cell Proteomics, 2006, 5: 144–56

    Article  CAS  Google Scholar 

  11. Geiger T, Cox J, Mann M. Proteomics on an orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol Cell Proteomics, 2010, 9: 2252–2261

    Article  CAS  Google Scholar 

  12. Bern M, Finney G, Hoopmann MR, Merrihew G, Toth MJ, MacCoss MJ. Deconvolution of mixture spectra from ion-trap data-independent-acquisition tandem mass spectrometry. Anal Chem, 2010, 82: 833–841

    Article  CAS  Google Scholar 

  13. Carvalho P, Han XM, Xu T, Cociorva D, Carvalho M, Barbosa V, Yates J. XDIA: improving on the label-free data-independent analysis. Bioinformatics, 2010, 26: 847–848

    Article  CAS  Google Scholar 

  14. Panchaud A, Jung S, Shaffer SA, Aitchison JD, Goodlett DR. Faster, quantitative, and accurate precursor acquisition independent from ion count. Anal Chem, 2011, 83: 2250–2257

    Article  CAS  Google Scholar 

  15. Panchaud A, Scherl A, Shaffer SA, von Haller PD, Kulasekara H, Miller SI, Goodlett DR. Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal Chem, 2009, 81: 6481–6488

    Article  CAS  Google Scholar 

  16. Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, Bonner R, Aebersold R. 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, 2012, 11: O111 016717

    Article  Google Scholar 

  17. Liu Y, Huttenhain R, Surinova S, Gillet LC, Mouritsen J, Brunner R, Navarro P, Aebersold R. Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS. Proteomics, 2013, 13: 1247–1256

    Article  CAS  Google Scholar 

  18. Liu S, Sun MZ, Tang JW, Wang Z, Sun C, Greenaway F. High-performance liquid chromatography/nano-electrospray ionization tandem mass spectrometry, two-dimensional difference in-gel electrophoresis and gene microarray identification of lymphatic metastasis-associated biomarkers. Rapid Commun Mass Spectrom, 2008, 22: 3172–3178

    Article  CAS  Google Scholar 

  19. Sun MZ, Liu S, Tang J, Wang Z, Gong X, Sun C, Greenaway F. Proteomics analysis of two mice hepatocarcinoma ascites syngeneic cell lines with high and low lymph node metastasis rates provide potential protein markers for tumor malignancy attributes to lymphatic metastasis. Proteomics, 2009, 9: 3285–3302

    Article  CAS  Google Scholar 

  20. Song B, Tang JW, Wang B, Cui XN, Hou L, Sun L, Mao LM, Zhou CH, Du Y, Wang LH, Wang HX, Zheng RS, Sun L. Identify lymphatic metastasis-associated genes in mouse hepatocarcinoma cell lines using gene chip. World J Gastroenterol, 2005, 11: 1463–1472

    CAS  Google Scholar 

  21. Shen Z, Ye Y, Kauttu T, Seppanen H, Vainionpaa S, Wang S, Mustonen H, Puolakkainen P. Novel focal adhesion protein kindlin-2 promotes the invasion of gastric cancer cells through phosphorylation of integrin beta1 and beta3. J Surg Oncol, 2013, 108: 106–112

    Article  CAS  Google Scholar 

  22. Sarto C, Marocchi A, Sanchez JC, Giannone D, Frutiger S, Golaz O, Wilkins M, Doro G, Cappellano F, Hughes G, Hochstrasser D, Mocarelli P. Renal cell carcinoma and normal kidney protein expression. Electrophoresis, 1997, 18: 599–604

    Article  CAS  Google Scholar 

  23. Wu L, Peng CW, Hou JX, Zhang YH, Chen C, Chen LD, Li Y. Coronin-1C is a novel biomarker for hepatocellular carcinoma invasive progression identified by proteomics analysis and clinical validation. J Exp Clin Cancer Res, 2010, 29: 17

    Article  Google Scholar 

  24. Craven RJ. PGRMC1: A new biomarker for the estrogen receptor in breast cancer. Breast Cancer Res, 2008, 10: 113

    Article  Google Scholar 

  25. Huang Y, Jin Y, Yan CH, Yu Y, Bai J, Chen F, Zhao YZ, Fu SB. Involvement of Annexin A2 in p53 induced apoptosis in lung cancer. Mol Cell Biochem, 2008, 309: 117–123

    Article  CAS  Google Scholar 

  26. Peng B, Guo C, Guan H, Liu S, Sun MZ. Annexin A5 as a potential marker in tumors. Clin Chim Acta, 2014, 427: 42–48

    Article  CAS  Google Scholar 

  27. Ruiz C, Holz DR, Oeggerli M, Schneider S, Gonzales IM, Kiefer JM, Zellweger T, Bachmann A, Koivisto PA, Helin HJ, Mousses S, Barrett MT, Azorsa D, Bubendorf L. Amplification and overexpression of vinculin are associated with increased tumour cell proliferation and progression in advanced prostate cancer. J Pathol, 2011, 223: 543–552

    Article  CAS  Google Scholar 

  28. Pols MS, Klumperman J. Trafficking and function of the tetraspanin CD63. Exp Cell Res, 2009, 315: 1584–1592

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China

    ZiQi Yan, Yuan Zhou, YiChu Shan, Qi Wu, Shen Zhang, Zhen Liang, LiHua Zhang & YuKui Zhang

Authors
  1. ZiQi Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YiChu Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhen Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. LiHua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. YuKui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to LiHua Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, Z., Zhou, Y., Shan, Y. et al. Label-free quantification of differentially expressed proteins in mouse liver cancer cells with high and low metastasis rates by a SWATH acquisition method. Sci. China Chem. 57, 718–722 (2014). https://doi.org/10.1007/s11426-014-5093-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11426-014-5093-z

Keywords

Search

Navigation