Skip to main content

SILAC and Alternatives in Studying Cellular Proteomes of Plants

  • Protocol
  • First Online:
Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1188))

Abstract

Quantitative proteomics by metabolic labeling has a high impact on the growing field of plant systems biology. SILAC has been pioneered and optimized for plant cell culture systems allowing for SILAC-based quantitative experiments in specialized experimental setups. In comparison to other model organisms, the application of SILAC to whole plants is challenging. As autotrophic organisms, plants under their natural growth conditions can hardly be fully labeled with stable isotope-coded amino acids. The metabolic labeling with inorganic nitrogen is therefore the method of choice for most whole-plant physiological questions. Plants can easily metabolize different inorganic nitrogen isotopes. The incorporation of the labeled inorganic nitrogen then results in proteins and metabolites with distinct molecular mass, which can be detected on a mass spectrometer. In comparative quantitative experiments, similarly as in SILAC experiments, treated and untreated samples are differentially labeled by nitrogen isotopes and jointly processed, thereby minimizing sample-to-sample variation. In recent years, heavy nitrogen labeling has become a widely used strategy in quantitative proteomics and novel approaches were developed for metabolite identification. Here we present a typical hydroponics setup, the workflow for processing of samples, mass spectrometry and data analysis for large-scale metabolic labeling experiments of whole plants.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ong SE, Blagoev B, Kratchmarova I et al (2002) 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

    Article  CAS  PubMed  Google Scholar 

  2. Bantscheff M, Schirle M, Sweetman G et al (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 389:1017–1031

    Article  CAS  PubMed  Google Scholar 

  3. Ong SE, Schenone M, Margolin AA et al (2009) Identifying the proteins to which small-molecule probes and drugs bind in cells. Proc Natl Acad Sci U S A 106:4617–4622

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Selbach M, Schwanhäusser B, Thierfelder N et al (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455:58–63

    Article  CAS  PubMed  Google Scholar 

  5. Schwannhäusser B, Gossen M, Dittmar G et al (2009) Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9:205–209

    Article  Google Scholar 

  6. Soufi B, Kumar C, Gnad F et al (2010) Stable isotope labeling by amino acids in cell culture (SILAC) applied to quantitative proteomics of Bacillus subtilis. J Proteome Res 9:3638–3646

    Article  CAS  PubMed  Google Scholar 

  7. Gruhler A, Olsen JV, Mohammed S et al (2005) Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol Cell Proteomics 4:310–327

    Article  CAS  PubMed  Google Scholar 

  8. Cuomo A, Bonaldi T (2010) Systems biology “on-the-fly”: SILAC-based quantitative proteomics and RNAi approach in Drosophila melanogaster. Methods Mol Biol 662:59–78

    Article  CAS  PubMed  Google Scholar 

  9. Larance M, Bailly AP, Pourkarimi E et al (2011) Stable-isotope labeling with amino acids in nematodes. Nat Methods 8:849–851

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Krüger M, Moser M, Ussar S et al (2008) SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134:353–364

    Article  PubMed  Google Scholar 

  11. Naumann B, Busch A, Allmer J et al (2007) Comparative quantitative proteomics to investigate the remodeling of bioenergetic pathways under iron deficiency in Chlamydomonas reinhardtii. Proteomics 7:3964–3979

    Article  CAS  PubMed  Google Scholar 

  12. Therashima M, Specht M, Naumann B et al (2010) Characterizing the anaerobic response of Chlamydomonas reinhardtii by quantitative proteomics. Mol Cell Proteomics 9:1514–1532

    Article  Google Scholar 

  13. Heide H, Nordhues A, Drepper F et al (2009) Application of quantitative immunoprecipitation combined with knockdown and cross-linking to Chlamydomonas reveals the presence of vesicle-inducing protein in plastids 1 in a common complex with chloroplast HSP90C. Proteomics 9:3079–3089

    Article  CAS  PubMed  Google Scholar 

  14. Gruhler A, Schulze WX, Matthiesen R et al (2005) Stable isotope labeling of Arabidopsis thaliana cells and quantitative proteomics by mass spectrometry. Mol Cell Proteomics 4:1697–1702

    Article  CAS  PubMed  Google Scholar 

  15. Schütz W, Hausmann N, Krug K et al (2011) Extending SILAC to proteomics of plant cell lines. Plant Cell 23:1701–1705

    Article  PubMed Central  PubMed  Google Scholar 

  16. Svennerstam H, Ganeteg U, Näsholm T (2008) Root uptake of cationic amino acids by Arabidopsis depends on functional expression of amino acid permease 5. New Phytol 180:620–630

    Article  CAS  PubMed  Google Scholar 

  17. Hirner A, Ladwig F, Stransky H et al (2006) Arabidopsis LHT1 is a high-affinity transporter for cellular amino acid uptake in both root epidermis and leaf mesophyll. Plant Cell 18:1931–1946

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Somerville CR, Ogren WL (1980) Photorespiration mutants of Arabidopsis thaliana deficient in serine-glyoxylate aminotransferase activity. Proc Natl Acad Sci U S A 77:2684–2687

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Forsum O, Svennerstam H, Ganeteg U et al (2008) Capacities and constraints of amino acid utilization in Arabidopsis. New Phytol 179:1058–1069

    CAS  PubMed  Google Scholar 

  20. Ippel JH, Pouvreau L, Kroef T et al (2004) In vivo uniform (15)N-isotope labelling of plants: using the greenhouse for structural proteomics. Proteomics 4:226–234

    Article  CAS  PubMed  Google Scholar 

  21. Arsova B, Kierszniowska S, Schulze WX (2012) The use of heavy nitrogen in quantitative proteomics experiments in plants. Trends Plant Sci 17:102–112

    Article  CAS  PubMed  Google Scholar 

  22. Gouw JW, Tops BBJ, Mortensen P et al (2008) Optimizing identification and quantitation of 15N-labeled proteins in comparative proteomics. Anal Chem 80:7796–7803

    Article  CAS  PubMed  Google Scholar 

  23. Marschner H (1998) Mineral nutrition of higher plants. Academic, London

    Google Scholar 

  24. Engelsberger WR, Erban A, Kopka J et al (2006) Metabolic labeling of plant cell cultures with K15NO3 as a tool for quantitative analysis of proteins and metabolites. Plant Methods 2:1–11

    Article  Google Scholar 

  25. Lanquar V, Kuhn L, Lelièvre F et al (2007) 15N-metabolic labeling for comparative plasma membrane proteomics in Arabidopsis cells. Proteomics 7:750–754

    Article  CAS  PubMed  Google Scholar 

  26. Bindschedler LV, Palmblad M, Cramer R (2008) Hydroponic isotope labelling of entire plants (HILEP) for quantitative plant proteomics; an oxidative stress case study. Phytochemistry 69:1962–1972

    Article  CAS  PubMed  Google Scholar 

  27. Laganowsky A, Gomez SM, Whitelegge JP et al (2009) Hydroponics on a chip: anaysis of the Fe deficient Arabidopsis thylakoid membrane proteome. J Proteomics 72:397–415

    Article  CAS  PubMed  Google Scholar 

  28. Nelson CJ, Huttlin EL, Hegeman AD et al (2007) Implications of 15N-metabolic labeling for automated peptide identification in Arabidopsis thaliana. Proteomics 7:1279–1292

    Article  CAS  PubMed  Google Scholar 

  29. Huttlin EL, Hegeman AD, Harms AC et al (2007) Comparison of full versus partial metabolic labeling for quantitative proteomic analysis in Arabidopsis thaliana. Mol Cell Proteomics 6:860–881

    Article  CAS  PubMed  Google Scholar 

  30. Kline KG, Barrett-Wilt GA, Sussman MR (2010) In planta changes in protein phosphorylation induced by the plant hormone abscisic acid. Proc Natl Acad Sci U S A 107:15986–15991

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Guo G, Li N (2011) Relative and accurate measurement of protein abundance using (15)N stable isotope labeling in Arabidopsis (SILIA). Phytochemistry 72:1028–1039

    Article  CAS  PubMed  Google Scholar 

  32. Hebeler R, Oeljeklaus S, Reidegeld KA et al (2008) Study of early leaf senescence in Arabidopsis thaliana by quantitative proteomics using reciprocal 14N/15N labeling and difference gel electrophoresis. Mol Cell Proteomics 7:108–120

    Article  CAS  PubMed  Google Scholar 

  33. Skirycz A, Memmi S, De Bodt S et al (2011) A reciprocal 15N-labeling proteomic analysis of expanding Arabidopsis leaves subjected to osmotic stress indicates importance of mitochondria in preserving plastid functions. J Proteome Res 10:1018–1029

    Article  CAS  PubMed  Google Scholar 

  34. Schaff JE, Mbeunkui F, Blackburn K et al (2008) SILIP: a novel stable isotope labeling method for in planta quantitative proteomic analysis. Plant J 56:840–854

    Article  CAS  PubMed  Google Scholar 

  35. Gruhler A, Kratchmarova I (2008) Stable isotope labeling by amino acids in cell culture (SILAC). Methods Mol Biol 424:101–111

    Article  CAS  PubMed  Google Scholar 

  36. Benschop JJ, Mohammed S, O’Flaherty M, Heck AJ, Slijper M, Menke FL (2007) Quantitative phospho-proteomics of early elicitor signalling in Arabidopsis. Mol Cell Proteomics 6:1705–1713

    Article  Google Scholar 

  37. Kierszniowska S, Seiwert B, Schulze WX (2009) Definition of Arabidopsis sterol-rich membrane microdomains by differential treatment with methyl-ß-cyclodextrin and quantitative proteomics. Mol Cell Proteomics 8:612–623

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Kierszniowska S, Walther D, Schulze WX (2009) Ratio-dependent significance thresholds in reciprocal 15N-labeling experiments as a robust tool in detection candidate proteins responding to biological treatment. Proteomics 9:1916–1924

    Article  CAS  PubMed  Google Scholar 

  39. Keinath NF, Kierszniowska S, Lorek J et al (2010) PAMP-induced changes in plasma membrane compartmentalization reveal novel components of plant immunity. J Biol Chem 285:39140–39149

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Mühlhaus T, Weiss J, Hemme D et al (2011) Quantitative shotgun proteomics using a uniform 15N-labeled standard to monitor proteome dynamics in time course experiments reveals new insights into the heat stress response of Chlamydomonas reinhardtii. Mol Cell Proteomics 10:M110.004739

    Article  PubMed Central  PubMed  Google Scholar 

  41. Palmblad M, Mills DJ, Bindschedler LV (2008) Heat-shock response in Arabidopsis thaliana explored by multiplexed quantitative proteomics using differential metabolic labeling. J Proteome Res 7:780–785

    Article  CAS  PubMed  Google Scholar 

  42. Nowaczyk MM, Hebeler R, Schlodder E et al (2006) Psb27, a cyanobacterial lipoprotein, is involved in the repair cycle of photosystem II. Plant Cell 18:3121–3131

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Zhang Y, Reckow S, Webhofer C et al (2011) Proteome scale turnover analysis in live animals using stable isotope metabolic labeling. Anal Chem 83:1665–1672

    Article  CAS  PubMed  Google Scholar 

  44. Martin SF, Munagapati VS, Salvo-Chirnside E et al (2012) Proteome turnover in the green alga Ostreococcus tauri by time course 15N metabolic labeling mass spectrometry. J Proteome Res 11:476–486

    Article  CAS  PubMed  Google Scholar 

  45. Li L, Nelson CJ, Solheim C et al (2012) Determining degradation and synthesis rates of Arabidopsis proteins using the kinetics of progressive 15N labeling of two-dimensional gel-separated protein spots. Mol Cell Proteomics 11:M111.010025

    Article  PubMed Central  PubMed  Google Scholar 

  46. Loqué D, Tillard P, Gojon A et al (2003) Gene expression of the NO3- transporter NRT1.1 and the nitrate reductase NIA1 is repressed in Arabidopsis roots by NO2-, the product of NO3- reduction. Plant Physiol 132:958–967

    Article  PubMed Central  PubMed  Google Scholar 

  47. Carroll AJ, Heazlewood JL, Ito J et al (2008) Analysis of the Arabidopsis cytosolic ribosome proteome provides detailed insights into its components and their post-translational modification. Mol Cell Proteomics 7:347–369

    Article  CAS  PubMed  Google Scholar 

  48. Marmagne A, Salvi D, Rolland N et al (2006) Purification and fractionation of membranes for proteomic analyses. Methods Mol Biol 323:403–420

    CAS  PubMed  Google Scholar 

  49. Lilley KS, Dupree P (2007) Plant organelle proteomics. Curr Opin Plant Biol 10:594–599

    Article  CAS  PubMed  Google Scholar 

  50. Glatter T, Ludwig C, Ahrné E et al (2012) 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

    Article  CAS  PubMed  Google Scholar 

  51. Rappsilber J, Ishihama Y, Mann M (2003) Stop And Go Extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal Chem 75:663–670

    Article  CAS  PubMed  Google Scholar 

  52. Mortensen P, Gouw JW, Olsen JV et al (2010) MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res 9:393–403

    Article  CAS  PubMed  Google Scholar 

  53. Gouw JW, Krijgsveld J, Heck AJ (2010) Quantitative proteomics by metabolic labeling of model organisms. Mol Cell Proteomics 9:11–24

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Ong SE, Mann M (2005) Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1:252–262

    Article  CAS  PubMed  Google Scholar 

  55. van Breukelen B, van den Toorn HW, Drugan MM et al (2009) StatQuant: a post-quantification analysis toolbox for improving quantitative mass spectrometry. Bioinformatics 25:1472–1473

    Article  PubMed  Google Scholar 

  56. Zauber H, Schulze WX (2012) Proteomics wants cRacker: Automated standardized data analysis of LC/MS derived proteomic data. J Proteome Res 11:5548–5555

    Article  CAS  PubMed  Google Scholar 

  57. Li XJ, Zhang H, Ranish JR et al (2003) Automated statistical analysis of protein abundance ratios from data generated by stable isotope dilution and tandem mass spectrometry. Anal Chem 75:6648–6657

    Article  CAS  PubMed  Google Scholar 

  58. Park SK, Venable JD, Xu T et al (2008) A quantitative analysis software tool for mass spectrometry-based proteomics. Nat Methods 5:319–322

    CAS  PubMed Central  PubMed  Google Scholar 

  59. Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372

    Article  CAS  PubMed  Google Scholar 

  60. Cox J, Matic I, Hilger M et al (2009) A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat Protoc 4:698–705

    Article  CAS  PubMed  Google Scholar 

  61. Cox J, Neuhauser N, Michalski A et al (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10:1794–1805

    Article  CAS  PubMed  Google Scholar 

  62. Specht M, Kuhlgert S, Fufezan C et al (2011) Proteomics to go: Proteomatic enables the user-friendly creation of versatile MS/MS data evaluation workflows. Bioinformatics 27:1183–1184

    Article  CAS  PubMed  Google Scholar 

  63. Han DK, Eng J, Zhou H et al (2001) Quantitative profiling of differentiation-induced microsomal proteins using isotope- coded affinity tags and mass spectrometry. Nat Biotechnol 19:946–951

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Waltraud X. Schulze .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this protocol

Cite this protocol

Matthes, A., Köhl, K., Schulze, W.X. (2014). SILAC and Alternatives in Studying Cellular Proteomes of Plants. In: Warscheid, B. (eds) Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC). Methods in Molecular Biology, vol 1188. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1142-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-1142-4_6

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1141-7

  • Online ISBN: 978-1-4939-1142-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics