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.
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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.
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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.
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Contributions
S.D. and M.C.-H. conceived and developed the R-DeeP procedure, including the statistical analysis pipeline. All authors wrote the manuscript.
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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.
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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.
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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.
Supplementary Table 1
Checklist for reproducible TCA precipitation
Supplementary Video 1
Gradient preparation.
Supplementary Video 2
Gradient fractionation.
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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
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DOI: https://doi.org/10.1038/s41596-019-0261-4
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