Skip to main content

Thank you for visiting nature.com. 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.

  • Protocol
  • Published:

Quantitative analysis of newly synthesized proteins

Nature Protocols volume 13, pages 1744–1762 (2018)Cite this article

Subjects

Abstract

Measuring proteome response to perturbations is critical for understanding the underlying mechanisms involved. Traditional quantitative proteomic methods are limited by the large numbers of proteins in the proteome and the mass spectrometer’s dynamic range. A previous method uses the biorthogonal reagent azidohomoalanine (AHA), a methionine analog, for labeling, enrichment and detection of newly synthesized proteins (NSPs). Newly synthesized AHA proteins can be coupled to biotin via CuAAC-mediated click chemistry and enriched using avidin-based affinity purification. The combination of AHA-mediated NSP labeling with metabolic stable isotope labeling allows quantitation of low-abundant, newly secreted proteins by mass spectrometry (MS). However, the resulting multiplicity of labeling complicates NSP analysis. We developed a new NSP quantification strategy, called HILAQ (heavy isotope–labeled azidohomoalanine quantification), that uses a heavy isotope–labeled AHA molecule to enable NSP labeling, enrichment, identification and quantification. In addition, the AHA-peptide enrichment used in HILAQ improves both the identification and quantification of NSPs over AHA-protein enrichment. Here, we provide a description of the HILAQ method that includes procedures for (i) pulse-labeling and harvesting NSPs; (ii) addition of biotin by click reaction; (iii) protein precipitation; (iv) protein digestion; (v) enrichment of AHA-biotin peptides by NeutrAvidin beads and four-step elution; (vi) MS analysis; and (vii) data analysis for the identification and quantification of NSPs by ProLuCID and pQuant. We demonstrate our HILAQ approach by identifying NSPs from cell cultures, but we anticipate that it can be adapted for applications in animal models. The whole protocol takes ~6 d to complete.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Flowchart of the protocol, with procedure step numbers.
Fig. 2: Schematic diagram of the differences between the traditional quantitative proteomic strategy and HILAQ.
Fig. 3: HILAQ performed in an HT22 oxytosis model.
Fig. 4: Analysis of 108 cell death pathway–enriched proteins with twofold change after 1 h of labeling in HT22 cells as compared with HEK293T cells.

Similar content being viewed by others

References

  1. Vogt, P. K., Hart, J. R. & Yates, J. R. A butterfly effect in cancer. Mol. Cell. Oncol. 3, e1029063 (2016).

    Article  PubMed  CAS  Google Scholar 

  2. Oda, Y. et al. Accurate quantitation of protein expression and site-specific phosphorylation. Proc. Natl. Acad. Sci. USA 96, 6591–6596 (1999).

    Article  PubMed  CAS  Google Scholar 

  3. Ma, Y. et al. Activated cyclin-dependent kinase 5 promotes microglial phagocytosis of fibrillar beta-amyloid by up-regulating lipoprotein lipase expression. Mol. Cell. Proteomics 12, 2833–2844 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Ong, S. E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002).

    Article  PubMed  CAS  Google Scholar 

  5. Liao, L. et al. Quantitative proteomic analysis of primary neurons reveals diverse changes in synaptic protein content in fmr1 knockout mice. Proc. Natl. Acad. Sci. USA 105, 15281–15286 (2008).

    Article  PubMed  Google Scholar 

  6. Doherty, M. K. et al. Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC. J. Proteome Res. 8, 104–112 (2009).

    Article  PubMed  CAS  Google Scholar 

  7. Dieterich, D. C. et al. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc. Natl. Acad. Sci. USA 103, 9482–9487 (2006).

    Article  PubMed  CAS  Google Scholar 

  8. Dieterich, D. C. et al. Labeling, detection and identification of newly synthesized proteomes with bioorthogonal non-canonical amino-acid tagging. Nat. Protoc. 2, 532–540 (2007).

    Article  PubMed  Google Scholar 

  9. McClatchy, D. B. et al. Pulsed azidohomoalanine labeling in mammals (PALM) detects changes in liver-specific LKB1 knockout mice. J. Proteome Res. 14, 4815–4822 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Dieterich, D. C. et al. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc. Natl. Acad. Sci. USA 103, 9482–9487 (2006).

    Article  PubMed  CAS  Google Scholar 

  11. Dieterich, D. C. et al. Labeling, detection and identification of newly synthesized proteomes with bioorthogonal non-canonical amino-acid tagging. Nat. Protoc. 2, 532–540 (2007).

    Article  PubMed  Google Scholar 

  12. Eichelbaum, K. et al. Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nat. Biotech. 30, 984–990 (2012).

    Article  CAS  Google Scholar 

  13. Howden, A. J. M. et al. QuaNCAT: quantitating proteome dynamics in primary cells. Nat. Methods 10, 343–346 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Ma, Y. et al. HILAQ: a novel strategy for newly synthesized protein quantification. J. Proteome Res. 16, 2213–2220 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Schiapparelli, L. M. et al. Direct detection of biotinylated proteins by mass spectrometry. J. Proteome Res. 13, 3966–3978 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Wang, J. et al. Quantitative chemical proteomics profiling of de novo protein synthesis during starvation-mediated autophagy. Autophagy 12, 1931–1944 (2016).

    Article  PubMed  PubMed Central  CAS  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  PubMed  CAS  Google Scholar 

  18. Kiick, K. L., Saxon, E., Tirrell, D. A. & Bertozzi, C. R. Incorporation of azides into recombinant proteins for chemoselective modification by the Staudinger ligation. Proc. Natl. Acad. Sci. USA 99, 19–24 (2002).

    Article  PubMed  CAS  Google Scholar 

  19. Chai, Q. et al. Study of the degradation of a multidrug transporter using a non-radioactive pulse chase method. Anal. Bioanal. Chem. 408, 7745–7751 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Kramer, G., Kasper, P. T., de Jong, L. & de Koster, C. G. Quantitation of newly synthesized proteins by pulse labeling with azidohomoalanine. Methods Mol. Biol. 753, 169–181 (2011).

    Article  PubMed  CAS  Google Scholar 

  21. Kervestin, S. & Amrani, N. Translational regulation of gene expression. Genome Biol. 5, 359–359 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Lu, B. et al. Identification of NUB1 as a suppressor of mutant Huntington toxicity via enhanced protein clearance. Nat. Neurosci. 16, 562–570 (2013).

    Article  PubMed  CAS  Google Scholar 

  23. McClatchy, D. B. et al. Pulsed azidohomoalanine labeling in mammals (PALM) detects changes in liver-specific LKB1 knockout mice. J. Proteome Res. 14, 4815–4822 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Speers, A. E. & Cravatt, B. F. Activity-based protein profiling (ABPP) and click chemistry (CC)-ABPP by MudPIT mass spectrometry. Curr. Protoc. Chem. Biol. 1, 29–41 (2009).

    PubMed  PubMed Central  Google Scholar 

  25. Chen, E. I., Cociorva, D., Norris, J. L. & Yates, J. R. Optimization of mass spectrometry-compatible surfactants for shotgun proteomics. J. Proteome Res. 6, 2529–2538 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Chen, E. I., McClatchy, D., Park, S. K. & Yates, J. R. 3rd Comparisons of mass spectrometry compatible surfactants for global analysis of the mammalian brain proteome. Anal. Chem. 80, 8694–8701 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Sun, S., Zhou, J. Y., Yang, W. & Zhang, H. Inhibition of protein carbamylation in urea solution using ammonium-containing buffers. Anal. Biochem. 446, 76–81 (2014).

    Article  PubMed  CAS  Google Scholar 

  28. Holmberg, A. et al. The biotin-streptavidin interaction can be reversibly broken using water at elevated temperatures. Electrophoresis 26, 501–510 (2005).

    Article  PubMed  CAS  Google Scholar 

  29. Wolters, D. A., Washburn, M. P. & Yates, J. R. 3rd An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 73, 5683–5690 (2001).

    Article  PubMed  CAS  Google Scholar 

  30. Howden, A. J. M. et al. QuaNCAT: quantitating proteome dynamics in primary cells. Nat. Methods 10, 343 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Magdeldin, S., Moresco, J. J., Yamamoto, T. & Yates, J. R. Off-line multidimensional liquid chromatography and auto sampling result in sample loss in LC/LC–MS/MS. J. Proteome Res. 13, 3826–3836 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Xu, T. et al. ProLuCID: an improved SEQUEST-like algorithm with enhanced sensitivity and specificity. J. Proteomics 129, 16–24 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Liu, C. et al. pQuant improves quantitation by keeping out interfering signals and evaluating the accuracy of calculated ratios. Anal. Chem. 86, 5286–5294 (2014).

    Article  PubMed  CAS  Google Scholar 

  34. Washburn, M. P., Wolters, D. & Yates, J. R. 3rd Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247 (2001).

    Article  PubMed  CAS  Google Scholar 

  35. McDonald, W. H. et al. MS1, MS2, and SQT-three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications. Rapid Commun. Mass Spectrom. 18, 2162–2168 (2004).

    Article  PubMed  CAS  Google Scholar 

  36. Cociorva, D., L. Tabb, D. & Yates, J. R. Validation of tandem mass spectrometry database search results using DTASelect. Curr. Protoc. Bioinformatics 16, 13.4.1–13.4.14 (2007).

    Google Scholar 

  37. Tabb, D. L., McDonald, W. H. & Yates, J. R. 3rd DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J. Proteome Res. 1, 21–26 (2002).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Ting, L. et al. Normalization and statistical analysis of quantitative proteomics data generated by metabolic labeling. Mol. Cell. Proteomics 8, 2227–2242 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Tan, S., Schubert, D. & Maher, P. Oxytosis: a novel form of programmed cell death. Curr. Top. Med. Chem. 1, 497–506 (2001).

    Article  PubMed  CAS  Google Scholar 

  40. Davis, J. B. & Maher, P. Protein kinase C activation inhibits glutamate-induced cytotoxicity in a neuronal cell line. Brain Res. 652, 169–173 (1994).

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We thank C. Liu from the Institute of Computing Technology, Chinese Academy of Sciences, for help with the use of pQuant. We thank C. Delahunty and X. Meng for critical reading of the manuscript. This work was supported by funding from the National Institutes of Health (P41 GM103533, R01 MH067880, R01 MH100175 to the Yates laboratory).

Author information

Authors and Affiliations

  1. Departments of Molecular Medicine and Neurobiology, The Scripps Research Institute, La Jolla, CA, USA

    Yuanhui Ma, Daniel B. McClatchy & John R. Yates III

  2. Cambridge Isotope Laboratories, Andover, MA, USA

    Salim Barkallah & William W. Wood

Authors
  1. Yuanhui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel B. McClatchy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salim Barkallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William W. Wood
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John R. Yates III
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.M., D.B.M. and J.R.Y. designed the research. Y.M. prepared samples, performed the MS analysis and analyzed the data. S.B. and W.W.W. contributed to hAHA synthesis. Y.M. wrote the manuscript.

Corresponding author

Correspondence to John R. Yates III.

Ethics declarations

Competing interests

J.R.Y. declares that he is a consultant for Cambridge Isotope Laboratories. W.W.W. and S.B. declare that they work for Cambridge Isotope Laboratories, which sells the hAHA. The remaining authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related link

Key reference using this protocol

1. Ma, Y. et al. J. Prot. Res. 16, 2213–2220 (2017) https://doi.org/10.1021/acs.jproteome.7b00005

Integrated supplementary information

Supplementary Figure 1 A typical MS1 spectrum of HILAQ samples.

Top: one MS1 spectrum with m/z range from 400 to 2000. Bottom: enlarged m/z range (560-660). The blue framed area indicated one pair of hAHA and AHA peptides (mass difference of 6)

Supplementary Figure 2 Box plot analysis on every replicate of HILAQ and QuaNCAT using Graphpad Prism (v5.01).

The box plot was created with Turkey whiskers. Adapted with permission from Ma et al.14, American Chemical Society

Supplementary Figure 3 Comparison of NSPs identified by HILAQ, QuaNCAT or QuanNCAT-pep.

Three independent biological replicates had been performed using each strategy. Error-bar represents “Mean with SD”

Supplementary information

Supplementary Text and Figures

Supplementary Figs. 1–3, Supplementary Table 1 and Supplementary Manuals 1 and 2

Reporting Summary

Supplementary Table 2

Identification results for HILAQ and QuaNCAT

Supplementary Table 3

List of proteins with twofold change

Supplementary Data 1

pQuant parameter file: element

Supplementary Data 2

pQuant parameter file: aa

Supplementary Data 3

pQuant parameter file: modification

Supplementary Data 4

Completed pQuant parameter setting file

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, Y., McClatchy, D.B., Barkallah, S. et al. Quantitative analysis of newly synthesized proteins. Nat Protoc 13, 1744–1762 (2018). https://doi.org/10.1038/s41596-018-0012-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-018-0012-y

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research