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Revealing nascent RNA processing dynamics with nano-COP

Nature Protocols volume 16, pages 1343–1375 (2021)Cite this article

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Abstract

During maturation, eukaryotic precursor RNAs undergo processing events including intron splicing, 3′-end cleavage, and polyadenylation. Here we describe nanopore analysis of co-transcriptional processing (nano-COP), a method for probing the timing and patterns of RNA processing. An extension of native elongating transcript sequencing, which quantifies transcription genome-wide through short-read sequencing of nascent RNA 3′ ends, nano-COP uses long-read nascent RNA sequencing to observe global patterns of RNA processing. First, nascent RNA is stringently purified through a combination of 4-thiouridine metabolic labeling and cellular fractionation. In contrast to cDNA or short-read–based approaches relying on reverse transcription or amplification, the sample is sequenced directly through nanopores to reveal the native context of nascent RNA. nano-COP identifies both active transcription sites and splice isoforms of single RNA molecules during synthesis, providing insight into patterns of intron removal and the physical coupling between transcription and splicing. The nano-COP protocol yields data within 3 d.

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Fig. 1: nano-COP schematic.
Fig. 2: Direct RNA sequencing of 4sU-labeled chromatin-associated RNA provides the most accurate measurement of the nascent transcriptome.
Fig. 3: Troubleshooting the incubation time for 3′ end poly(A) tailing with oGAB11.
Fig. 4: Detection of poly(A) and poly(I) tails in ONT direct RNA sequencing data.
Fig. 5: Representative RT-qPCR plots of RNA purified by cellular fractionation with varying incubation times in the presence of the splicing inhibitor pladienolide B (PlaB).
Fig. 6: Nano-COP captures the nascent transcriptome.

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Data availability

The accession numbers for the nanopore sequencing data presented in this paper are Gene Expression Omnibus (GEO): GSE123191 (data from ref. 18) and GSE154079. Supplementary Table 1 indicates the correct accession number for each sample. Source data are provided with this paper.

Code availability

All scripts for data analyses described in this paper are available at https://github.com/churchmanlab/nano-COP. The code for nanopolish-detect-polyI is available at https://github.com/jts/nanopolish.git.

References

  1. Keohavong, P., Gattoni, R., LeMoullec, J. M., Jacob, M. & Stévenin, J. The orderly splicing of the first three leaders of the adenovirus-2 major late transcript. Nucleic Acids Res. 10, 1215–1229 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Mariman, E. C., van Beek-Reinders, R. J. & van Venrooij, W. J. Alternative splicing pathways exist in the formation of adenoviral late messenger RNAs. J. Mol. Biol. 163, 239–256 (1983).

    Article  CAS  PubMed  Google Scholar 

  3. Beyer, A. L. & Osheim, Y. N. Splice site selection, rate of splicing, and alternative splicing on nascent transcripts. Genes Dev. 2, 754–765 (1988).

    Article  CAS  PubMed  Google Scholar 

  4. Audibert, A., Weil, D. & Dautry, F. In vivo kinetics of mRNA splicing and transport in mammalian cells. Mol. Cell. Biol. 22, 6706–6718 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Singh, J. & Padgett, R. A. Rates of in situ transcription and splicing in large human genes. Nat. Struct. Mol. Biol. 16, 1128–1133 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Coulon, A. et al. Kinetic competition during the transcription cycle results in stochastic RNA processing. eLife 3, e03939 (2014).

    Article  PubMed Central  Google Scholar 

  7. Martin, R. M., Rino, J., Carvalho, C., Kirchhausen, T. & Carmo-Fonseca, M. Live-cell visualization of pre-mRNA splicing with single-molecule sensitivity. Cell Rep. 4, 1144–1155 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rabani, M. et al. High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies. Cell 159, 1698–1710 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wachutka, L., Caizzi, L., Gagneur, J. & Cramer, P. Global donor and acceptor splicing site kinetics in human cells. eLife 8, e45056 (2019).

  10. Wan, Y. et al. Dynamic imaging of nascent RNA reveals general principles of transcription dynamics and stochastic splice site selection. SSRN Electron. J. https://doi.org/10.2139/ssrn.3467157 (2019).

  11. Takahara, K. et al. Order of intron removal influences multiple splice outcomes, including a two-exon skip, in a COL5A1 acceptor-site mutation that results in abnormal Pro-α1(V) N-propeptides and ehlers-danlos syndrome type I. Am. J. Hum. Genet. 71, 451–465 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fong, N. et al. Pre-mRNA splicing is facilitated by an optimal RNA polymerase II elongation rate. Genes Dev. 28, 2663–2676 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Dujardin, G. et al. How slow RNA polymerase II elongation favors alternative exon skipping. Mol. Cell 54, 683–690 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. De la Mata, M. et al. A slow RNA polymerase II affects alternative splicing in vivo. Mol. Cell 12, 525–532 (2003).

    Article  PubMed  Google Scholar 

  15. Kessler, O., Jiang, Y. & Chasin, L. A. Order of intron removal during splicing of endogenous adenine phosphoribosyltransferase and dihydrofolate reductase pre-mRNA. Mol. Cell. Biol. 13, 6211–6222 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Schwarze, U., Starman, B. J. & Byers, P. H. Redefinition of exon 7 in the COL1A1 gene of type I collagen by an intron 8 splice-donor–site mutation in a form of osteogenesis imperfecta: influence of intron splice order on outcome of splice-site mutation. Am. J. Hum. Genet. 65, 336–344 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kim, S. W. et al. Widespread intra-dependencies in the removal of introns from human transcripts. Nucleic Acids Res. 45, 9503–9513 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Drexler, H. L., Choquet, K. & Churchman, L. S. Splicing kinetics and coordination revealed by direct nascent RNA sequencing through nanopores. Mol. Cell 77, 985–998.e8 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Churchman, L. S. & Weissman, J. S. Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469, 368–373 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. Mayer, A. et al. Native elongating transcript sequencing reveals human transcriptional activity at nucleotide resolution. Cell 161, 541–554 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Eid, J. et al. Single polymerase molecules. Science 323, 133–138 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Weirather, J. L. Et al. Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Res. 6, 100 (2017).

  23. Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Soneson, C. et al. A comprehensive examination of Nanopore native RNA sequencing for characterization of complex transcriptomes. Nat. Commun. 10, 3359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Jia, J. et al. Post-transcriptional splicing of nascent RNA contributes to widespread intron retention in plants. Nat. Plants 6, 780–788 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Danko, C. G. et al. Signaling pathways differentially affect RNA polymerase II initiation, pausing, and elongation rate in cells. Mol. Cell 50, 212–222 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Veloso, A. et al. Rate of elongation by RNA polymerase II is associated with specific gene features and epigenetic modifications. Genome Res. 24, 896–905 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Dölken, L. et al. High-resolution gene expression profiling for simultaneous kinetic parameter analysis of RNA synthesis and decay. RNA 14, 1959–1972 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Windhager, L. et al. Ultrashort and progressive 4sU-tagging reveals key characteristics of RNA processing at nucleotide resolution. Genome Res. 22, 2031–2042 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Schwalb, B. et al. TT-seq maps the human transient transcriptome. Science 352, 1225–1228 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Rabani, M. et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat. Biotechnol. 29, 436–442 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Gregersen, L. H., Mitter, R. & Svejstrup, J. Q. Using Ttchem-seq for profiling nascent transcription and measuring transcript elongation. Nat. Protoc. 15, 604–627 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Pai, A. A. et al. The kinetics of pre-mRNA splicing in the Drosophila genome and the influence of gene architecture. eLife 6, 1–26 (2017).

    Article  Google Scholar 

  34. Maier, K. C., Gressel, S., Cramer, P. & Schwalb, B. Native molecule sequencing by nano-ID reveals synthesis and stability of RNA isoforms. Genome Res. 30, 1332–1344 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Carrillo Oesterreich, F. et al. Splicing of nascent RNA coincides with intron exit from RNA polymerase II. Cell 165, 372–381 (2016).

    Article  CAS  Google Scholar 

  36. Brody, Y. et al. The In vivo kinetics of RNA polymerase II elongation during co-transcriptional splicing. PloS Biol. 9, e1000573 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Herzel, L., Straube, K. & Neugebauer, K. M. Long-read sequencing of nascent RNA reveals coupling among RNA processing events. Genome Res. 28, 1008–1019 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mayer, A. & Churchman, L. S. Genome-wide profiling of RNA polymerase transcription at nucleotide resolution in human cells with native elongating transcript sequencing. Nat. Protoc. 11, 813–833 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lindell, T. J., Weinberg, F., Morris, P. W., Roeder, R. G. & Rutter, W. J. Specific inhibition of nuclear RNA polymerase II by alpha-amanitin. Science 170, 447–449 (1970).

    Article  CAS  PubMed  Google Scholar 

  40. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Han, F. & Lillard, S. J. In-situ sampling and separation of RNA from individual mammalian cells. Anal. Chem. 72, 4073–4079 (2000).

    Article  CAS  PubMed  Google Scholar 

  42. Jackson, D. A., Iborra, F. J., Manders, E. M. & Cook, P. R. Numbers and organization of RNA polymerases, nascent transcripts, and transcription units in HeLa nuclei. Mol. Biol. Cell 9, 1523–1536 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Rädle, B. et al. Metabolic labeling of newly transcribed RNA for high resolution gene expression profiling of RNA synthesis, processing and decay in cell culture. J. Vis. Exp. https://doi.org/10.3791/50195 (2013).

  44. Tani, H. et al. Genome-wide determination of RNA stability reveals hundreds of short-lived noncoding transcripts in mammals. Genome Res. 22, 947–956 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Schofield, J. A., Duffy, E. E., Kiefer, L., Sullivan, M. C. & Simon, M. D. TimeLapse-seq: adding a temporal dimension to RNA sequencing through nucleoside recoding. Nat. Methods 15, 221–225 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kovaka, S., Fan, Y., Ni, B., Timp, W. & Schatz, M. C. Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0731-9 (2020).

  47. Payne, A. et al. Nanopore adaptive sequencing for mixed samples, whole exome capture and targeted panels. Preprint at bioRxiv https://doi.org/10.1101/2020.02.03.926956 (2020).

  48. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Dale, R. K., Pedersen, B. S. & Quinlan, A. R. Pybedtools: a flexible Python library for manipulating genomic datasets and annotations. Bioinformatics 27, 3423–3424 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Workman, R. E. et al. Nanopore native RNA sequencing of a human poly(A) transcriptome. Nat. Methods 16, 1297–1305 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of the Churchman lab, F. Winston, W. Timp, R. Workman, N. Sadowski, M. Marin, B. Smalec, M. Richardson, R. Ietswaart, and A. Markham for helpful discussions, advice, and assistance and C. Kaplan, J. Bridgers, B. Smalec, J. Falk and C. Patil for critical reading of the manuscript. This work was supported by the NIH (R21-HG009264, R01-HG010538, and R01-GM117333 to L.S.C.; F31-GM122133 to H.L.D.), an NSF Graduate Research Fellowship to H.E.M., the Fonds de Recherche du Québec–Santé and the Canadian Institutes of Health Research (Post-doctoral fellowship awards to K.C.). J.T.S. is supported by the Ontario Institute for Cancer Research through funds provided by the Government of Ontario and the Government of Canada through Genome Canada and Ontario Genomics (OGI-136).

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Author notes

  1. These authors contributed equally: Heather L. Drexler, Karine Choquet.

Authors and Affiliations

  1. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Heather L. Drexler, Karine Choquet, Hope E. Merens & L. Stirling Churchman

  2. Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    Paul S. Tang & Jared T. Simpson

  3. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

    Jared T. Simpson

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Contributions

H.L.D., K.C. and L.S.C. conceived and designed the study. H.L.D. established the nano-COP protocol and K.C. developed the poly(I) tailing approach. H.L.D., K.C. and H.E.M. performed experiments. H.L.D. and K.C. generated scripts and performed data analysis. P.S.T. and J.T.S. developed nanopolish-detect-polyI. H.L.D., K.C., H.E.M. and L.S.C. wrote the manuscript. P.S.T. and J.T.S. reviewed and edited the manuscript.

Corresponding author

Correspondence to L. Stirling Churchman.

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Competing interests

J.T.S. receives research funding from Oxford Nanopore Technologies. J.T.S. and H.L.D. have received travel support to attend and speak at meetings organized by Oxford Nanopore Technologies. All other authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Shobbir Hussain, Jixian Zhai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key reference using this protocol:

Drexler, H. L., Choquet, K. & Churchman, L. S. Mol. Cell 77, 985–998.e8 (2020): https://doi.org/10.1016/j.molcel.2019.11.017

This protocol is an extension to: Nat. Protoc. 11, 813–833 (2016): https://doi.org/10.1038/nprot.2016.047

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Fig. 1, Supplementary Note and Supplementary Table 1

Source data

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Unprocessed gel.

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Drexler, H.L., Choquet, K., Merens, H.E. et al. Revealing nascent RNA processing dynamics with nano-COP. Nat Protoc 16, 1343–1375 (2021). https://doi.org/10.1038/s41596-020-00469-y

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