Transcriptome-wide interrogation of RNA secondary structure in living cells with icSHAPE

Journal name:
Nature Protocols
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Published online


icSHAPE (in vivo click selective 2-hydroxyl acylation and profiling experiment) captures RNA secondary structure at a transcriptome-wide level by measuring nucleotide flexibility at base resolution. Living cells are treated with the icSHAPE chemical NAI-N3 followed by selective chemical enrichment of NAI-N3–modified RNA, which provides an improved signal-to-noise ratio compared with similar methods leveraging deep sequencing. Purified RNA is then reverse-transcribed to produce cDNA, with SHAPE-modified bases leading to truncated cDNA. After deep sequencing of cDNA, computational analysis yields flexibility scores for every base across the starting RNA population. The entire experimental procedure can be completed in ~5 d, and the sequencing and bioinformatics data analysis take an additional 4–5 d with no extensive computational skills required. Comparing in vivo and in vitro icSHAPE measurements can reveal in vivo RNA-binding protein imprints or facilitate the dissection of RNA post-transcriptional modifications. icSHAPE reactivities can additionally be used to constrain and improve RNA secondary structure prediction models.

At a glance


  1. Schematic overview of icSHAPE.
    Figure 1: Schematic overview of icSHAPE.

    (a) Stepwise scheme for acylating flexible RNA nucleobases with the SHAPE reagent NAI-N3 and subsequent 'click' reaction of DIBO-biotin specifically to icSHAPE-modified nucleotides20. (b) Experimental and computational steps in the icSHAPE procedure. Living cells are first treated with NAI-N3 to record the in vivo flexibility of RNA nucleotides across the transcriptome. Modified (and mock-modified) RNA is isolated, and 'click' reactions add DIBO-biotin specifically to modified RNAs. RNA fragmentation and RT 'reads out' the NAI-N3 modification, after which only NAI-N3–modified molecules are selectively isolated and sequenced. Deep-sequencing reads are separated for individual experiments, removed of PCR artifacts and mapped to the appropriate transcriptome build. RT stop sites and abundances are calculated and biological replicate experiments are merged to ensure reproducibility. RT stops are normalized for each transcript, and the resulting data are visualized in the UCSC genome browser or used for downstream analysis.

  2. Experimental data from representative icSHAPE experiments.
    Figure 2: Experimental data from representative icSHAPE experiments.

    (a) Urea PAGE analysis of fragmented and 3′-end–ligated RNA generated in Step 31. The free RNA linkers are labeled (black arrows) and the region of the gel cut for subsequent steps is shown within a dashed red box for one sample. (b) Urea PAGE analysis of cDNAs generated in Step 55. Carry-over RNA linkers and unextended RT primers are labeled (black arrows), and the red box (dashed red line) denotes the region cut for subsequent steps. (c) Native PAGE analysis of icSHAPE library DNA generated in Step 64. Un-incorporated PCR primers and PCR primer-dimer products are labeled (black arrows). The final library DNA, above the PCR primer dimer, is highlighted (dashed red box) and subsequently cut from the gel and analyzed on an Illumina deep-sequencing platform.

  3. icSHAPE reactivities of Nanog (Nanog homeobox) mRNA.
    Figure 3: icSHAPE reactivities of Nanog (Nanog homeobox) mRNA.

    A 7-kb region centered on the Nanog locus is shown with two isoforms of the Nanog mRNA in the mm10 build of the mouse genome. In vivo (red) and in vitro icSHAPE experimental data are represented as tracks with values ranging from 0 to 1. The vivovitro difference (VTD) score for each base is shown in gray.


  1. Cooper, T.A., Wan, L. & Dreyfuss, G. RNA and disease. Cell 136, 777793 (2009).
  2. Cech, T.R. Structural biology. The ribosome is a ribozyme. Science 289, 878879 (2000).
  3. Guerrier-Takada, C., Gardiner, K., Marsh, T., Pace, N. & Altman, S. The RNA moiety of ribonuclease P is the catalytic subunit of the enzyme. Cell 35, 849857 (1983).
  4. Rana, T.M. Illuminating the silence: understanding the structure and function of small RNAs. Nat. Rev. Mol. Cell Biol. 8, 2336 (2007).
  5. Weeks, K.M. Advances in RNA structure analysis by chemical probing. Curr. Opin. Struct. Biol. 20, 295304 (2010).
  6. Goodman, D.B., Church, G.M. & Kosuri, S. Causes and effects of N-terminal codon bias in bacterial genes. Science. 342, 475479 (2013).
  7. Wan, Y., Qu, K., Ouyang, Z. & Chang, H.Y. Genome-wide mapping of RNA structure using nuclease digestion and high-throughput sequencing. Nat. Protoc. 8, 849869 (2013).
  8. Tijerina, P., Mohr, S. & Russell, R. DMS footprinting of structured RNAs and RNA-protein complexes. Nat. Protoc. 2, 26082623 (2007).
  9. Ziehler, W.A. & Engelke, D.R. Probing RNA structure with chemical reagents and enzymes. Curr. Protoc. Nucleic Acid Chem. Chapter 6, Unit 6.1 (2001).
  10. Ingle, S., Azad, R.N., Jain, S.S. & Tullius, T.D. Chemical probing of RNA with the hydroxyl radical at single-atom resolution. Nucleic Acids Res. 42, 1275812767 (2014).
  11. Merino, E.J., Wilkinson, K.A., Coughlan, J.L. & Weeks, K.M. RNA structure analysis at single nucleotide resolution by selective 2′-hydroxyl acylation and primer extension (SHAPE). J. Am. Chem. Soc. 127, 42234231 (2005).
  12. Risca, V.I. & Greenleaf, W.J. Beyond the linear genome: paired-end sequencing as a biophysical tool. Trends in Cell Biol. 25, 716 (2015).
  13. Kertesz, M. et al. Genome-wide measurement of RNA secondary structure in yeast. Nature 467, 103107 (2010).
  14. Underwood, J.G. et al. FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat. Methods 7, 9951001 (2010).
  15. Li, F. et al. Global analysis of RNA secondary structure in two metazoans. Cell Rep. 1, 6982 (2012).
  16. Lucks, J.B. et al. Multiplexed RNA structure characterization with selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-seq). Proc. Natl. Acad. Sci. USA 108, 1106311068 (2011).
  17. Rouskin, S., Zubradt, M., Washietl, S., Kellis, M. & Weissman, J.S. Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505, 701705 (2014).
  18. Ding, Y. et al. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature 505, 696700 (2014).
  19. Talkish, J., May, G., Lin, Y., Woolford, J.L. & McManus, C.J. Mod-seq: high-throughput sequencing for chemical probing of RNA structure. RNA 20, 713720 (2014).
  20. Spitale, R.C. et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519, 486490 (2015).
  21. Kelley, D.R., Hendrickson, D.G. & Tenen, D. Transposable elements modulate human RNA abundance and splicing via specific RNA-protein interactions. Genome 15, 537 (2014).
  22. König, J. et al. iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat. Struct. Mol. Biol. 17, 909915 (2010).
  23. Spitale, R.C. et al. RNA SHAPE analysis in living cells. Nat. Chem. Biol. 9, 1820 (2012).
  24. Poulsen, L.D., Kielpinski, L.J., Salama, S.R., Krogh, A. & Vinther, J. SHAPE selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data. RNA 21, 10421052 (2015).
  25. Reuter, J.S. & Mathews, D.H. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics 11, 129 (2010).
  26. Ouyang, Z., Snyder, M.P. & Chang, H.Y. SeqFold: genome-scale reconstruction of RNA secondary structure integrating high-throughput sequencing data. Genome Res. 23, 377387 (2013).
  27. Eddy, S.R. Computational analysis of conserved RNA secondary structure in transcriptomes and genomes. Annu. Rev. Biophys. 43, 433456 (2014).
  28. Sloma, M.F. & Mathews, D.H. Chapter four-improving RNA secondary structure prediction with structure mapping data. Methods Enzymol. 553, 91114 (2015).
  29. Bai, Y., Dai, X., Harrison, A., Johnston, C. & Chen, M. Toward a next-generation atlas of RNA secondary structure. Brief. Bioinform. doi:10.1093/bib/bbv026 (2015).
  30. Liu, N. et al. N(6)-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518, 560564 (2015).
  31. Flynn, R.A. et al. Dissecting noncoding and pathogen RNA-protein interactomes. RNA 21, 13543 (2015).
  32. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621628 (2008).

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Author information

  1. These authors contributed equally to this work.

    • Ryan A Flynn &
    • Qiangfeng Cliff Zhang


  1. Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, California, USA.

    • Ryan A Flynn,
    • Qiangfeng Cliff Zhang,
    • Robert C Spitale,
    • Byron Lee,
    • Maxwell R Mumbach &
    • Howard Y Chang


R.A.F., Q.C.Z., R.C.S. and H.Y.C. designed the experimental and computational strategy. R.A.F., R.C.S., B.L. and M.R.M. optimized experimental conditions. Q.C.Z. optimized computational parameters. R.A.F., Q.C.Z. and H.Y.C. wrote the manuscript with input from all authors.

Competing financial interests

H.Y.C. is an inventor on a patent for in vivo SHAPE reagents. H.Y.C. is a founder of Epinomics and a member of the Scientific Advisory Board of RaNA Therapeutics.

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