Identification, quantification and bioinformatic analysis of RNA-dependent proteins by RNase treatment and density gradient ultracentrifugation using R-DeeP

Abstract

Analysis of RNA–protein complexes is central to understanding the molecular circuitry governing cellular processes. In recent years, several proteome-wide studies have been dedicated to the identification of RNA-binding proteins. Here, we describe in detail R-DeeP, an approach built on RNA dependence, defined as the ability of a protein to engage in protein complexes only in the presence of RNA, involving direct or indirect interaction with RNA. This approach provides—for the first time, to our knowledge—quantitative information on the fraction of a protein associated with RNA–protein complexes. R-DeeP is independent of any potentially biased purification procedures. It is based on cellular lysate fractionation by density gradient ultracentrifugation and subsequent analysis by proteome-wide mass spectrometry (MS) or individual western blotting. The comparison of lysates with and without previous RNase treatment enables the identification of differences in the apparent molecular weight and, hence, the size of the complexes. In combination with information from databases of protein–protein complexes, R-DeeP facilitates the computational reconstruction of protein complexes from proteins migrating in the same fraction. In addition, we developed a pipeline for the statistical analysis of the MS dataset to automatically identify RNA-dependent proteins (proteins whose interactome depends on RNA). With this protocol, the individual analysis of proteins of interest by western blotting can be completed within 1–2 weeks. For proteome-wide studies, additional time is needed for the integration of the proteomic and statistical analyses. In the future, R-DeeP can be extended to other fractionation techniques, such as chromatography.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Overview of the R-DeeP procedure.
Fig. 2: Gradient preparation procedure.
Fig. 3: RNase treatment of cleared lysates.
Fig. 4: Comparison of gradients from different rotors.
Fig. 5: Gradient fractionation procedure.
Fig. 6: TMT workflow for sucrose gradient fractions.
Fig. 7: Workflow of the statistical analysis.

Data availability

A test dataset (Mass_Spec_RawData_Sample.csv), a result summary file (MS_Analysis_Shifts.csv) and two examples of graphics (HNRPU_HUMAN_FIT.pdf and HNRPU_HUMAN_FIT.pdf) are available in the Supplementary Data.

Code availability

An analysis software pipeline is provided as MS_Statistical_Analysis.R in the Supplementary Data, along with an instruction file (README.txt). The whole analysis package, including the examples, can be directly downloaded from the documentation section of our database at http://R-DeeP.dkfz.de. The software is distributed as open-source software under a 3-clause BSD license. The code in this protocol has been peer-reviewed. The analysis of the whole HeLa cell line dataset is available online as a user-friendly database at http://R-DeeP.dkfz.de.

References

  1. 1.

    Kishore, S., Luber, S. & Zavolan, M. Deciphering the role of RNA-binding proteins in the post-transcriptional control of gene expression. Brief. Funct. Genomics 9, 391–404 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Cook, K. B., Kazan, H., Zuberi, K., Morris, Q. & Hughes, T. R. RBPDB: a database of RNA-binding specificities. Nucleic Acids Res. 39, D301–308 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Gerstberger, S., Hafner, M. & Tuschl, T. A census of human RNA-binding proteins. Nat. Rev. Genet. 15, 829–845 (2014).

    Article  CAS  Google Scholar 

  4. 4.

    Ray, D. et al. A compendium of RNA-binding motifs for decoding gene regulation. Nature 499, 172–177 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Baltz, A. G. et al. The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell 46, 674–690 (2012).

    Article  CAS  Google Scholar 

  6. 6.

    Castello, A. et al. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell 149, 1393–1406 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Kwon, S. C. et al. The RNA-binding protein repertoire of embryonic stem cells. Nat. Struct. Mol. Biol 20, 1122–1130 (2013).

    Article  CAS  Google Scholar 

  8. 8.

    Beckmann, B. M. et al. The RNA-binding proteomes from yeast to man harbour conserved enigmRBPs. Nat. Commun. 6, 10127 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Perez-Perri, J. I. et al. Discovery of RNA-binding proteins and characterization of their dynamic responses by enhanced RNA interactome capture. Nat. Commun. 9, 4408 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Conrad, T. et al. Serial interactome capture of the human cell nucleus. Nat. Commun. 7, 11212 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    He, C. et al. High-resolution mapping of RNA-binding regions in the nuclear proteome of embryonic stem cells. Mol. Cell 64, 416–430 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Bao, X. et al. Capturing the interactome of newly transcribed RNA. Nat. Methods 15, 213–220 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Huang, R., Han, M., Meng, L. & Chen, X. Capture and identification of RNA-binding proteins by using click chemistry-assisted RNA-interactome capture (CARIC) strategy. J Vis Exp, https://doi.org/10.3791/58580 (2018).

  14. 14.

    Castello, A. et al. Comprehensive identification of RNA-binding domains in human cells. Mol. Cell 63, 696–710 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Mullari, M., Lyon, D., Jensen, L. J. & Nielsen, M. L. Specifying RNA-binding regions in proteins by peptide cross-linking and affinity purification. J. Proteome Res. 16, 2762–2772 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Trendel, J. et al. The human RNA-binding proteome and its dynamics during translational arrest. Cell 176, 391–403 e319 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Queiroz, R. M. L. et al. Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS). Nat. Biotechnol. 37, 169–178 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Urdaneta, E. C. et al. Purification of cross-linked RNA-protein complexes by phenol-toluol extraction. Nat. Commun. 10, 990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Chomczynski, P. & Sacchi, N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal. Biochem. 162, 156–159 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Brannan, K. W. et al. SONAR discovers RNA-binding proteins from analysis of large-scale protein–protein interactomes. Mol. Cell 64, 282–293 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Treiber, T. et al. A compendium of RNA-binding proteins that regulate microRNA biogenesis. Mol. Cell 66, 270–284 e213 (2017).

    Article  CAS  Google Scholar 

  22. 22.

    Sundararaman, B. et al. Resources for the comprehensive discovery of functional RNA Eeements. Mol. Cell 61, 903–913 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Hendrickson, D. G., Kelley, D. R., Tenen, D., Bernstein, B. & Rinn, J. L. Widespread RNA binding by chromatin-associated proteins. Genome Biol. 17, 28 (2016).

    Article  CAS  Google Scholar 

  24. 24.

    Caudron-Herger, M. et al. R-DeeP: proteome-wide and quantitative identification of RNA-dependent proteins by density gradient ultracentrifugation. Mol. Cell 75, 184–199 e10 (2019).

    Article  CAS  Google Scholar 

  25. 25.

    Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    Article  CAS  Google Scholar 

  26. 26.

    Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic Acids Re.s 38, D497–D501 (2010).

    Article  CAS  Google Scholar 

  27. 27.

    Kirkwood, K. J., Ahmad, Y., Larance, M. & Lamond, A. I. Characterization of native protein complexes and protein isoform variation using size-fractionation-based quantitative proteomics. Mol. Cell Proteomics 12, 3851–3873 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Hock, J. et al. Proteomic and functional analysis of Argonaute-containing mRNA-protein complexes in human cells. EMBO Rep. 8, 1052–1060 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Smirnov, A. et al. Grad-seq guides the discovery of ProQ as a major small RNA-binding protein. Proc. Natl Acad. Sci. USA 113, 11591–11596 (2016).

    Article  CAS  Google Scholar 

  30. 30.

    Fernandez-Martinez, J., LaCava, J. & Rout, M. P. Density gradient ultracentrifugation to isolate endogenous protein complexes after affinity capture. Cold Spring Harb. Protoc. 2016, https://doi.org/10.1101/pdb.prot087957 (2016).

  31. 31.

    Behal, R. H. & Cole, D. G. Analysis of interactions between intraflagellar transport proteins. Methods Enzymol. 524, 171–194 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Kalthoff, C., Alves, J., Urbanke, C., Knorr, R. & Ungewickell, E. J. Unusual structural organization of the endocytic proteins AP180 and epsin 1. J. Biol. Chem. 277, 8209–8216 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Gagnon, K. T., Li, L., Janowski, B. A. & Corey, D. R. Analysis of nuclear RNA interference in human cells by subcellular fractionation and Argonaute loading. Nat. Protoc. 9, 2045–2060 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Hafner, M. et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141, 129–141 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Sysoev, V. O. et al. Global changes of the RNA-bound proteome during the maternal-to-zygotic transition in Drosophila. Nat. Commun. 7, 12128 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Rinn, J. L. & Chang, H. Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).

    Article  CAS  Google Scholar 

  37. 37.

    Castello, A. et al. System-wide identification of RNA-binding proteins by interactome capture. Nat. Protoc. 8, 491–500 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Castello, A. et al. Identification of RNA-binding domains of RNA-binding proteins in cultured cells on a system-wide scale with RBDmap. Nat. Protoc. 12, 2447–2464 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Pillai, R. S. et al. Inhibition of translational initiation by Let-7 microRNA in human cells. Science 309, 1573–1576 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Wu, Y., Li, Q. & Chen, X. Z. Detecting protein–protein interactions by far western blotting. Nat. Protoc. 2, 3278–3284 (2007).

    Article  CAS  Google Scholar 

  41. 41.

    Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry–based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    Article  CAS  Google Scholar 

  42. 42.

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

    Article  CAS  Google Scholar 

  43. 43.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was partially supported by a DKFZ NCT3.0 Integrative Project in Cancer Research grant (NCT3.0_2015.54 DysregPT to S.D.) and grants R35GM119455 and P20GM113132 from the National Institute of General Medicine (to A.N.K.). We thank the Loayza-Puch group (DKFZ, Heidelberg) and the Cathomen group (Medical Center, Freiburg) for allowing us to use their ultracentrifuges. We thank all members of the Diederichs and Kettenbach labs for their comments and suggestions on the method.

Author information

Affiliations

Authors

Contributions

S.D. and M.C.-H. conceived and developed the R-DeeP procedure, including the statistical analysis pipeline. All authors wrote the manuscript.

Corresponding authors

Correspondence to Maiwen Caudron-Herger or Sven Diederichs.

Ethics declarations

Competing interests

S.D. is co-owner of siTOOLs Biotech GmbH, Martinsried, Germany, which has no relation to this study or competing interests. The other authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Alfredo Castello, Markus Hafner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Related links

Key reference using this protocol

Caudron-Herger, M. et al., Mol. Cell 75, 184–199.e10 (2019): https://doi.org/10.1016/j.molcel.2019.04.018

Integrated supplementary information

Supplementary Figure 1 Analysis of samples treated with individual RNases.

Western blot analysis for hnRNP U (RNA-dependent protein) in fractions of control and individual RNase-treated samples as indicated (RNase A, RNase I, RNase T1, RNase III or RNase H) in HeLa cells (N = 1). For all western blots, the fractions were loaded onto two membranes (1: fractions 1 to 13 and 2: fractions 14 to 24).

Supplementary Figure 2 Effect of various centrifugation times.

Western blot analysis for hnRNP U (RNA-dependent protein) in fractions 12 to 25 of control (untreated) samples loaded onto 5% to 50% sucrose density gradients and centrifuged for 18 h, 6 h or 2 h, as indicated. One representative replicate out of two replicates is shown.

Supplementary Figure 3 Western blot analysis of individual proteins.

Western blot analysis for Nucleolin (NCL, RNA-dependent protein) in 25 fractions of representative control and RNase-treated samples in HeLa cells. b, Same as in a for MCM7 (RNA-independent protein). c, same as in a for hnRNP U (RNA-dependent protein) but in A549 cells. d, same as in a for ASNS (RNA-independent protein) but in A549 cells. For all western blots, fractions 1 to 25 were loaded onto two membranes (1: fractions 1 to 13 and 2: fractions 14 to 25).

Supplementary Figure 4 Schematic overview of western blot quantification.

The single steps for western blot quantification with the software ImageJ are shown (Steps 43–49). The generated intensity table can be copied and pasted to a program (e.g. Microsoft Excel) for further data processing and production of a graphical output. The intensities of each experiment are normalized as follows: \(normalized\,protein\,amount = \frac{{intensity\left( x \right)}}{{\mathop {\sum }\nolimits_{y = 1}^{25} intensity\left( y \right)}} \times 100\) for the fraction x.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4.

Reporting Summary

Supplementary Table 1

Checklist for reproducible TCA precipitation

Supplementary Data

Supplementary Video 1

Gradient preparation.

Supplementary Video 2

Gradient fractionation.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Caudron-Herger, M., Wassmer, E., Nasa, I. et al. Identification, quantification and bioinformatic analysis of RNA-dependent proteins by RNase treatment and density gradient ultracentrifugation using R-DeeP. Nat Protoc 15, 1338–1370 (2020). https://doi.org/10.1038/s41596-019-0261-4

Download citation

Further reading

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

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing