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Structural imprints in vivo decode RNA regulatory mechanisms

An Erratum to this article was published on 30 September 2015


Visualizing the physical basis for molecular behaviour inside living cells is a great challenge for biology. RNAs are central to biological regulation, and the ability of RNA to adopt specific structures intimately controls every step of the gene expression program1. However, our understanding of physiological RNA structures is limited; current in vivo RNA structure profiles include only two of the four nucleotides that make up RNA2,3. Here we present a novel biochemical approach, in vivo click selective 2′-hydroxyl acylation and profiling experiment (icSHAPE), which enables the first global view, to our knowledge, of RNA secondary structures in living cells for all four bases. icSHAPE of the mouse embryonic stem cell transcriptome versus purified RNA folded in vitro shows that the structural dynamics of RNA in the cellular environment distinguish different classes of RNAs and regulatory elements. Structural signatures at translational start sites and ribosome pause sites are conserved from in vitro conditions, suggesting that these RNA elements are programmed by sequence. In contrast, focal structural rearrangements in vivo reveal precise interfaces of RNA with RNA-binding proteins or RNA-modification sites that are consistent with atomic-resolution structural data. Such dynamic structural footprints enable accurate prediction of RNA–protein interactions and N6-methyladenosine (m6A) modification genome wide. These results open the door for structural genomics of RNA in living cells and reveal key physiological structures controlling gene expression.

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Figure 1: icSHAPE is a novel and robust method for measuring RNA structure.
Figure 2: icSHAPE reveals unique structural profiles for nucleobase reactivity and post-transcriptional interactions.
Figure 3: icSHAPE reveals structural profiles associated with translation.
Figure 4: icSHAPE dynamics reveal and predict post-transcriptional interactions.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

All genomic data sets have been deposited in the Gene Expression Omnibus under accession number GSE64169.


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We thank members of the Chang laboratory, J. Weissman and J. Doudna for comments. This work was supported by National Institutes of Health (NIH) R01HG004361 and P50HG007735, and the California Institute for Regenerative Medicine (H.Y.C.), NIH R01068122 (E.T.K.), the A.P. Giannini Foundation (R.C.S.), a Stanford Dean’s Fellowship (Q.C.Z.), NIH T32AR007422 (P.J.B.), and the Stanford Medical Scientist Training Program and NIH F30CA189514 (R.A.F.). H.Y.C. is an Early Career Scientist of the Howard Hughes Medical Institute.

Author information

Authors and Affiliations



R.C.S., E.T.K. and H.Y.C. conceived the study. R.C.S., P.C., J.-W.J., H.Y.K. and E.T.K. performed chemical design and synthesis. R.C.S., R.A.F., B.L., E.A.T. and P.J.B. performed biological experiments. Q.C.Z., R.C.S., R.A.F. and H.Y.C. performed data analysis. R.C.S. and H.Y.C. wrote the paper with input from all authors.

Corresponding authors

Correspondence to Eric T. Kool or Howard Y. Chang.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Chemical synthesis of NAI-N3.

a, Synthetic scheme for NAI-N3. b, 1HMR of methyl 2-(azidomethyl)nicotinate. c, 1HNMR of 2-(azidomethyl)nicotinic acid. d, 13CNMR of 2-(azidomethyl)nicotinic acid. e, 1HNMR of 2-(azidomethyl)nicotinic acid acyl imidazole. f, 13CNMR of 2-(azidomethyl)nicotinic acid acyl imidazole.

Extended Data Figure 2 NAI-N3 is a novel RNA acylation reagent that enables RNA purification.

a, Chemical schematic of RNA acylation and copper-free ‘click’ chemistry using NAI-N3 and dibenzocyclooxtyne (DIBO)–biotin conjugate. b, ATP acylation gel shift showing ATP acylation and copper-free ‘click’ chemistry using NAI-N3 and DIBO–biotin conjugate.

Extended Data Figure 3 NAI-N3 is a novel RNA acylation reagent that accurately reads out RNA structure.

a, Comparative denaturing gel of NAI and NAI-N3 RNA acylation. b, Denaturing gel analysis of cDNAs that originate from the biotin-purification protocol (Extended Data Fig. 1). c, Secondary structure of the SAM-I Riboswitch with enriched residues highlighted in orange and depleted residues highlighted in blue. d, Denaturing gel analysis of denatured RNA probed with NAI-N3 shows even coverage of 2′-hydroxyl reactivity when RNA is unfolded. e, Protein titration with bovine serum albumin (BSA), demonstrating no difference in the SHAPE pattern as a function of protein concentration.

Extended Data Figure 4 icSHAPE is capable of reproducing RNA acylation profiles obtained by manual RNA modification experiments.

icSHAPE profiles (right) of rRNA, and compared to those obtained by manual SHAPE (left).

Extended Data Figure 5 Reverse transcription stops measured by icSHAPE are very well correlated in different library replicates.

Extended Data Figure 6 icSHAPE is capable of measuring the RNA structure profiles of thousands of RNAs simultaneously.

a, The RNAs represented in polyA-selected RNA, in vivo. b, The RNAs represented in polyA-selected RNA, in vitro.

Extended Data Figure 7 Non-AUG start codons are associated with preceding reactivity, and non-AUG start codons have a different profile, suggesting that RNA accessibility alone is not sufficient to drive translation.

a, icSHAPE profile at AUG start codons, in vivo. b, icSHAPE profile at AUG start codons, in vitro. c, icSHAPE profile at CUG start codons, in vivo. d, icSHAPE profile at CUG start codons, in vitro.

Extended Data Figure 8 icSHAPE can be used to predict post-transcriptional regulatory elements.

a, icSHAPE profile at Rbfox2 targets, in vivo. b, icSHAPE profile at Rbfox2 targets, in vitro. c, ROC curve of Rbfox2 RNA–protein interactions, predicted using icSHAPE profiles. d, icSHAPE profile at m6A targets, in vivo. The negative control is the set of motif instances that are not m6A modified. e, icSHAPE profile at m6A targets, in vitro. f, ROC curve of m6A RNA modification sites, predicted using icSHAPE profiles. g, icSHAPE profile at HuR targets, in vivo. h, icSHAPE profile at HuR targets, in vitro. i, ROC curve of HuR RNA–protein interactions, predicted using icSHAPE profiles.

Extended Data Figure 9 iCLIP analysis of HuR in mouse ES cells.

a, Global binding preference of the RBP HuR in mouse ES cells as represented by reverse transcription (RT) stops across the mouse transcriptome (mm9). HuR mainly binds protein-coding, processed and ribosomal RNAs. b, Number of unique RNA transcripts bound by HuR. c, HuR reverse transcription stops distributed across protein-coding transcript functional domains. HuR prefers intronic and 3′ UTR regions. d, Metagene analysis of all HuR binding sites. Each mRNA region (5′ UTR, CDS or 3′ UTR) was scaled to a standard width and reverse transcription stop density across all bound protein-coding genes and was plotted, revealing a clear enrichment for 3′ UTR regions in mature protein-coding transcripts. e, Individual mRNA binding events of HuR to genes important for mouse ES cell biology, including Tet1, β-actin, Elav1 (encoding HuR itself) and Lin28a. Discrete binding sites are observed focused in 3′ UTR and intronic regions.

Extended Data Figure 10 m6A-associated RNA structure features are preserved, independent of their position along the RNA transcript.

Supplementary information

Supplementary Table 1 - VTD Analysis on hexamer sequences corresponding to RNA motifs identified by RNA Compete.

VTD information on the RBP binding motifs identified in an RNAcompete assay. Each row corresponds to aRNAcompete motif. Rows of human and mouse motifs are highlighted in green. Motifs are indexed by the Protein_name. The species and kingdom of the source organism are indicated, as well as the type(s) of RNA-binding domains contained in the protein. RNAcompete_logo is a sequence logo representing the motif obtained from RNAcompete, IUPAC_hexamerMotif are the manually curated IUPAC representation of the consensus sequences of the closest hexamers. VTD is the reactivity score difference in vivo and in vitro, as defined in the manuscript. pVaule_stable, pValue_gain and pValue_loss are p values of three permutation tests that ask: 1), whether the absolute value of the motif VTD is significantly less than a random hexamer, i.e., represents a stable region; 2), whether the motif VTD is significantly smaller than a random hexamer, i.e., represents a region that is more structured in vivo; and 3), whether the motif VTD is significantly bigger than a random hexamer, i.e., represents a region that is more structured in vitro. (XLSX 2568 kb)

Supplementary Table 2 - VTD information on hexamers that match to hexamer-seed of a mouse miRNA.

The file contains VTD information on hexamers that match to hexamer-seed of a mouse miRNA. Each row corresponds to a mouse miRNA seed complementary hexamer. Motifs are indexed by the miRNA name; miRNAs with the same hexamer seeds are grouped together. All other values are defined the same as in the first spreadsheet. (XLSX 88 kb)

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Spitale, R., Flynn, R., Zhang, Q. et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519, 486–490 (2015).

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