Pulse SILAC Approaches to the Measurement of Cellular Dynamics

  • Justin Snider
  • Daifeng Wang
  • Daniel F. BogenhagenEmail author
  • John D. HaleyEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1140)


The global measurement of assembly and turnover of protein containing complexes within cells has advanced with the development of pulse stable isotope labelled amino acid approaches. Stable isotope labeling with amino acids in cell culture (SILAC) allows the incorporation of “light” 12-carbon amino acids or “heavy” 13-carbon amino acids into cells or organisms and the quantitation of proteins and peptides containing these amino acid tags using mass spectrometry. The use of these labels in pulse or pulse-chase scenarios has enabled measurements of macromolecular dynamics in cells, on time scales of several hours. Here we review advances with this approach and alternative or parallel strategies. We also examine the statistical considerations impacting datasets detailing mitochondrial assembly, to highlight key parameters in establishing significance and reproducibility.


Pulse SILAC Protein complex assembly Protein synthesis 


  1. 1.
    Gygi, S. P., Rochon, Y., Franza, B. R., & Aebersold, R. (1999). Correlation between protein and mRNA abundance in yeast. Molecular and Cellular Biology, 19, 1720–1730.CrossRefGoogle Scholar
  2. 2.
    Garlick, P. J., & Millward, D. J. (1972). An appraisal of techniques for the determination of protein turnover in vivo. The Proceedings of the Nutrition Society, 31, 249–255.CrossRefGoogle Scholar
  3. 3.
    Mann, M. (2006). Functional and quantitative proteomics using SILAC. Nature Reviews. Molecular Cell Biology, 7, 952–958. Scholar
  4. 4.
    Ong, S. E., Kratchmarova, I., & Mann, M. (2003). Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). Journal of Proteome Research, 2, 173–181.CrossRefGoogle Scholar
  5. 5.
    Ong S.E., Blagoev B., Kratchmarova I., Kristensen D.B., Steen H., Pandey A., Mann M. (2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular Proteomics, 1, 376–386.CrossRefGoogle Scholar
  6. 6.
    Boisvert, F. M., et al. (2012). A quantitative spatial proteomics analysis of proteome turnover in human cells. Molecular & Cellular Proteomics, 11, M111.011429. Scholar
  7. 7.
    Pratt, J. M., et al. (2002). Dynamics of protein turnover, a missing dimension in proteomics. Molecular & Cellular Proteomics, 1, 579–591.CrossRefGoogle Scholar
  8. 8.
    Doherty, M. K., Whitehead, C., McCormack, H., Gaskell, S. J., & Beynon, R. J. (2005). Proteome dynamics in complex organisms: using stable isotopes to monitor individual protein turnover rates. Proteomics, 5, 522–533. Scholar
  9. 9.
    Ahmad, Y., Boisvert, F. M., Lundberg, E., Uhlen, M., & Lamond, A. I. (2012). Systematic analysis of protein pools, isoforms, and modifications affecting turnover and subcellular localization. Molecular & Cellular Proteomics, 11, M111.013680. Scholar
  10. 10.
    Bogenhagen, D. F., Martin, D. W., & Koller, A. (2014). Initial steps in RNA processing and ribosome assembly occur at mitochondrial DNA nucleoids. Cell Metabolism, 19, 618–629. Scholar
  11. 11.
    Hinkson, I. V., & Elias, J. E. (2011). The dynamic state of protein turnover: it’s about time. Trends in Cell Biology, 21, 293–303. Scholar
  12. 12.
    Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 26, 1367–1372. Scholar
  13. 13.
    Geer, L. Y., et al. (2004). Open mass spectrometry search algorithm. Journal of Proteome Research, 3, 958–964. Scholar
  14. 14.
    Schubert, O. T., Rost, H. L., Collins, B. C., Rosenberger, G., & Aebersold, R. (2017). Quantitative proteomics: challenges and opportunities in basic and applied research. Nature Protocols, 12, 1289–1294. Scholar
  15. 15.
    Nesvizhskii, A. I., Vitek, O., & Aebersold, R. (2007). Analysis and validation of proteomic data generated by tandem mass spectrometry. Nature Methods, 4, 787–797. Scholar
  16. 16.
    Jayapal, K. P., et al. (2010). Multitagging proteomic strategy to estimate protein turnover rates in dynamic systems. Journal of Proteome Research, 9, 2087–2097. Scholar
  17. 17.
    Elias, J. E., Haas, W., Faherty, B. K., & Gygi, S. P. (2005). Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nature Methods, 2, 667–675. Scholar
  18. 18.
    Tabb, D. L., et al. (2010). Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. Journal of Proteome Research, 9, 761–776. Scholar
  19. 19.
    Milner, E., Barnea, E., Beer, I., & Admon, A. (2006). The turnover kinetics of major histocompatibility complex peptides of human cancer cells. Molecular & Cellular Proteomics, 5, 357–365. Scholar
  20. 20.
    Bogenhagen, D. F., Ostermeyer-Fay, A. G., Haley, J. D., & Garcia-Diaz, M. (2018). Kinetics and mechanism of mammalian mitochondrial ribosome assembly. Cell Reports, 22, 1935–1944. Scholar
  21. 21.
    Garlick, P. J., & Millward, D. J. (1972). An appraisal of techniques for the determination of protein turnover in vivo. The Biochemical Journal, 129, 1P.PubMedPubMedCentralGoogle Scholar
  22. 22.
    Lam, Y. W., Lamond, A. I., Mann, M., & Andersen, J. S. (2007). Analysis of nucleolar protein dynamics reveals the nuclear degradation of ribosomal proteins. Current Biology, 17, 749–760. Scholar
  23. 23.
    McShane, E., et al. (2016). Kinetic analysis of protein stability reveals age-dependent degradation. Cell, 167, 803–815.e21. Scholar
  24. 24.
    Wessel, D., & Flugge, U. I. (1984). A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Analytical Biochemistry, 138, 141–143.CrossRefGoogle Scholar
  25. 25.
    Shilov, I. V., et al. (2007). The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Molecular & Cellular Proteomics, 6, 1638–1655.CrossRefGoogle Scholar
  26. 26.
    Beavis, R. C. (2006). Using the global proteome machine for protein identification. Methods in Molecular Biology, 328, 217–228. Scholar
  27. 27.
    Calvo, S. E., Clauser, K. R., & Mootha, V. K. (2016). MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Research, 44, D1251–D1257. Scholar
  28. 28.
    MacLean, B., et al. (2010). Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics, 26, 966–968. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Biological Mass Spectrometry CenterStony Brook University School of MedicineStony BrookUSA
  2. 2.Department of Biomedical InformaticsStony Brook University School of MedicineStony BrookUSA
  3. 3.Stony Brook Cancer CenterStony Brook University School of MedicineStony BrookUSA
  4. 4.Department of PharmacologyStony Brook University School of MedicineStony BrookUSA
  5. 5.Department of PathologyStony Brook University School of MedicineStony BrookUSA

Personalised recommendations