Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Transcriptome-wide profiling and quantification of N6-methyladenosine by enzyme-assisted adenosine deamination

Abstract

N6-methyladenosine (m6A), the most abundant internal messenger RNA modification in higher eukaryotes, serves myriad roles in regulating cellular processes. Functional dissection of m6A is, however, hampered in part by the lack of high-resolution and quantitative detection methods. Here we present evolved TadA-assisted N6-methyladenosine sequencing (eTAM-seq), an enzyme-assisted sequencing technology that detects and quantifies m6A by global adenosine deamination. With eTAM-seq, we analyze the transcriptome-wide distribution of m6A in HeLa and mouse embryonic stem cells. The enzymatic deamination route employed by eTAM-seq preserves RNA integrity, facilitating m6A detection from limited input samples. In addition to transcriptome-wide m6A profiling, we demonstrate site-specific, deep-sequencing-free m6A quantification with as few as ten cells, an input demand orders of magnitude lower than existing quantitative profiling methods. We envision that eTAM-seq will enable researchers to not only survey the m6A landscape at unprecedented resolution, but also detect m6A at user-specified loci with a simple workflow.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Global A deamination by TadA8.20.
Fig. 2: Transcriptome-wide m6A profiling in HeLa cells by eTAM-seq.
Fig. 3: Profiling of m6A in mESCs by eTAM-seq.
Fig. 4: m6A is strongly depleted in Mettl3 KO mESCs.
Fig. 5: The impact of m6A on transcript stability.
Fig. 6: Site-specific, deep-sequencing-free m6A detection and quantification.

Data availability

All eTAM-seq data have been deposited to the National Center for Biotechnology Information’s GEO and can be accessed through accession no. GSE201064.

Code availability

Codes for processing eTAM-seq data are available in the following GitHub repository (https://github.com/shunliubio/eTAM-seq_workflow).

References

  1. Frye, M., Jaffrey, S. R., Pan, T., Rechavi, G. & Suzuki, T. RNA modifications: what have we learned and where are we headed? Nat. Rev. Genet. 17, 365–372 (2016).

    Article  CAS  Google Scholar 

  2. Peer, E., Rechavi, G. & Dominissini, D. Epitranscriptomics: regulation of mRNA metabolism through modifications. Curr. Opin. Chem. Biol. 41, 93–98 (2017).

    Article  CAS  Google Scholar 

  3. Nachtergaele, S. & He, C. Chemical modifications in the life of an mRNA transcript. Annu. Rev. Genet. 52, 349–372 (2018).

    Article  CAS  Google Scholar 

  4. Jiang, X. et al. The role of m6A modification in the biological functions and diseases. Signal Transduct. Target. Ther. 6, 74 (2021).

    Article  CAS  Google Scholar 

  5. He, P. C. & He, C. m6A RNA methylation: from mechanisms to therapeutic potential. EMBO J. 40, e105977 (2021).

    Article  CAS  Google Scholar 

  6. Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).

  7. Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012).

    Article  CAS  Google Scholar 

  8. Linder, B. et al. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015).

  9. Garcia-Campos, M. A. et al. Deciphering the ‘m6A code’ via antibody-independent quantitative profiling. Cell 178, 731–747 (2019).

    Article  CAS  Google Scholar 

  10. Zhang, Z. et al. Single-base mapping of m6A by an antibody-independent method. Sci. Adv. 5, eaax0250 (2019).

    Article  CAS  Google Scholar 

  11. Wang, Y., Xiao, Y., Dong, S., Yu, Q. & Jia, G. Antibody-free enzyme-assisted chemical approach for detection of N6-methyladenosine. Nat. Chem. Biol. 16, 896–903 (2020).

    Article  CAS  Google Scholar 

  12. Shu, X. et al. A metabolic labeling method detects m6A transcriptome-wide at single base resolution. Nat. Chem. Biol. 16, 887–895 (2020).

    Article  CAS  Google Scholar 

  13. Meyer, K. D. DART-seq: an antibody-free method for global m6A detection. Nat. Methods 16, 1275–1280 (2019).

    Article  CAS  Google Scholar 

  14. Hu, L. et al. m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome. Nat. Biotechnol. 40, 1210–1219 (2022).

    Article  CAS  Google Scholar 

  15. Liu, N. et al. Probing N6-methyladenosine RNA modification status at single nucleotide resolution in mRNA and long noncoding RNA. RNA 19, 1848–1856 (2013).

  16. Xiao, Y. et al. An elongation- and ligation-based qPCR amplification method for the radiolabeling-free detection of locus-specific N6-methyladenosine modification. Angew. Chem. Int. Ed. 57, 15995–16000 (2018).

    Article  CAS  Google Scholar 

  17. Frommer, M. et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc. Natl Acad. Sci. USA 89, 1827–1831 (1992).

    Article  CAS  Google Scholar 

  18. Walkley, C. R. & Li, J. B. Rewriting the transcriptome: adenosine-to-inosine RNA editing by ADARs. Genome Biol. 18, 205 (2017).

    Article  Google Scholar 

  19. Gaudelli, N. M. et al. Programmable base editing of A*T to G*C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).

    Article  CAS  Google Scholar 

  20. Gaudelli, N. M. et al. Directed evolution of adenine base editors with increased activity and therapeutic application. Nat. Biotechnol. 38, 892–900 (2020).

    Article  CAS  Google Scholar 

  21. Grunewald, J. et al. Transcriptome-wide off-target RNA editing induced by CRISPR-guided DNA base editors. Nature 569, 433–437 (2019).

    Article  CAS  Google Scholar 

  22. Kint, S., De Spiegelaere, W., De Kesel, J., Vandekerckhove, L. & Van Criekinge, W. Evaluation of bisulfite kits for DNA methylation profiling in terms of DNA fragmentation and DNA recovery using digital PCR. PLoS ONE 13, e0199091 (2018).

    Article  Google Scholar 

  23. Benjamini, Y. & Speed, T. P. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012).

    Article  CAS  Google Scholar 

  24. Liu, J. et al. A METTL3–METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2014).

  25. Hussain, S., Aleksic, J., Blanco, S., Dietmann, S. & Frye, M. Characterizing 5-methylcytosine in the mammalian epitranscriptome. Genome Biol. 14, 215 (2013).

    Article  Google Scholar 

  26. Zhang, Z. et al. Systematic calibration of epitranscriptomic maps using a synthetic modification-free RNA library. Nat. Methods 18, 1213–1222 (2021).

    Article  CAS  Google Scholar 

  27. Piekna-Przybylska, D., Decatur, W. A. & Fournier, M. J. The 3D rRNA modification maps database: with interactive tools for ribosome analysis. Nucleic Acids Res. 36, D178–D183 (2008).

    Article  CAS  Google Scholar 

  28. Herbert, Z. T. et al. Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction. BMC Genomics 19, 199 (2018).

    Article  Google Scholar 

  29. Maden, B. E. Identification of the locations of the methyl groups in 18S ribosomal RNA from Xenopus laevis and man. J. Mol. Biol. 189, 681–699 (1986).

    Article  CAS  Google Scholar 

  30. van Tran, N. et al. The human 18S rRNA m6A methyltransferase METTL5 is stabilized by TRMT112. Nucleic Acids Res. 47, 7719–7733 (2019).

  31. Maden, B. E. Locations of methyl groups in 28S rRNA of Xenopus laevis and man. Clustering in the conserved core of molecule. J. Mol. Biol. 201, 289–314 (1988).

    Article  CAS  Google Scholar 

  32. Ma, H. et al. N6-Methyladenosine methyltransferase ZCCHC4 mediates ribosomal RNA methylation. Nat. Chem. Biol. 15, 88–94 (2019).

    Article  CAS  Google Scholar 

  33. Poldermans, B., Roza, L. & Van Knippenberg, P.H. Studies on the function of two adjacent N6,N6-dimethyladenosines near the 3′ end of 16S ribosomal RNA of Escherichia coli. III. Purification and properties of the methylating enzyme and methylase-30 S interactions. J. Biol. Chem. 254, 9094–9100 (1979).

  34. Lafontaine, D., Vandenhaute, J. & Tollervey, D. The 18S rRNA dimethylase Dim1p is required for pre-ribosomal RNA processing in yeast. Genes Dev. 9, 2470–2481 (1995).

    Article  CAS  Google Scholar 

  35. Zorbas, C. et al. The human 18S rRNA base methyltransferases DIMT1L and WBSCR22-TRMT112 but not rRNA modification are required for ribosome biogenesis. Mol. Biol. Cell 26, 2080–2095 (2015).

    Article  CAS  Google Scholar 

  36. Wei, C., Gershowitz, A. & Moss, B. N6, O2′-dimethyladenosine a novel methylated ribonucleoside next to the 5′ terminal of animal cell and virus mRNAs. Nature 257, 251–253 (1975).

  37. Wei, J. et al. Differential m6A, m6Am, and m1A demethylation mediated by FTO in the cell nucleus and cytoplasm. Mol. Cell 71, 973–985 (2018).

    Article  CAS  Google Scholar 

  38. Wang, X. et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).

  39. McIntyre, A. B. R. et al. Limits in the detection of m6A changes using MeRIP/m6A-seq. Sci. Rep. 10, 6590 (2020).

  40. Ge, R. et al. m6A-SAC-seq for quantitative whole transcriptome m6A profiling. Nat. Protoc. in press (2022).

  41. Geula, S. et al. m6A mRNA methylation facilitates resolution of naive pluripotency toward differentiation. Science 347, 1002–1006 (2015).

  42. Wang, Y. et al. N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014).

  43. Batista, P. J. et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).

    Article  CAS  Google Scholar 

  44. Takahashi, K. & Yamanaka, S. A decade of transcription factor-mediated reprogramming to pluripotency. Nat. Rev. Mol. Cell Biol. 17, 183–193 (2016).

    Article  CAS  Google Scholar 

  45. Lee, Y., Choe, J., Park, O. H. & Kim, Y. K. Molecular mechanisms driving mRNA degradation by m6A modification. Trends Genet. 36, 177–188 (2020).

    Article  CAS  Google Scholar 

  46. Wang, X. et al. N6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).

  47. Shi, H. et al. YTHDF3 facilitates translation and decay of N6-methyladenosine-modified RNA. Cell Res. 27, 315–328 (2017).

  48. Shi, H., Wei, J. & He, C. Where, when, and how: context-dependent functions of RNA methylation writers, readers, and erasers. Mol. Cell 74, 640–650 (2019).

    Article  CAS  Google Scholar 

  49. Kluesner, M. G. et al. EditR: a method to quantify base editing from Sanger sequencing. CRISPR J. 1, 239–250 (2018).

    Article  CAS  Google Scholar 

  50. Schaefer, M., Pollex, T., Hanna, K. & Lyko, F. RNA cytosine methylation analysis by bisulfite sequencing. Nucleic Acids Res. 37, e12 (2009).

    Article  Google Scholar 

  51. Liu, N. et al. N6-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518, 560–564 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  53. Liu, J. et al. N6-methyladenosine of chromosome-associated regulatory RNA regulates chromatin state and transcription. Science 367, 580–586 (2020).

    Article  CAS  Google Scholar 

  54. Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020).

    Article  CAS  Google Scholar 

  55. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 3 (2011).

    Article  Google Scholar 

  56. Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    Article  CAS  Google Scholar 

  57. Zhang, Y., Park, C., Bennett, C., Thornton, M. & Kim, D. Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N. Genome Res. 31, 1290–1295 (2021).

    Article  Google Scholar 

  58. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  Google Scholar 

  59. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  60. Ramaswami, G. et al. Accurate identification of human Alu and non-Alu RNA editing sites. Nat. Methods 9, 579–581 (2012).

    Article  CAS  Google Scholar 

  61. Lo Giudice, C., Tangaro, M. A., Pesole, G. & Picardi, E. Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal. Nat. Protoc. 15, 1098–1131 (2020).

    Article  CAS  Google Scholar 

  62. Cuddleston, W. H. et al. Cellular and genetic drivers of RNA editing variation in the human brain. Nat. Commun. 13, 2997 (2022).

    Article  CAS  Google Scholar 

  63. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  65. Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  Google Scholar 

  66. Chen, C. Y., Ezzeddine, N. & Shyu, A. B. Messenger RNA half-life measurements in mammalian cells. Methods Enzymol. 448, 335–357 (2008).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank K. M. Watters for scientific editing of the manuscript. We thank L. Yang and Y. Xiao for maintaining and characterizing Mettl3 cKO mESCs. We thank P. Faber of the University of Chicago Genomics Facility for sequencing support. Funding: C.H. is supported by the National Institutes of Health (NIH; grant no. RM1 HG008935) and is a Howard Hughes Medical Institute Investigator. M.C. is supported by the NIH (grant nos. R01 GM126553 and R01 HG011883), the National Science Foundation (grant no. NSF 2016307), the Sloan Research Fellowship Program and the Chan Zuckerberg Initiative. W.T. is supported by the Searle Scholars Program, a pilot award under grant no. RM1 HG008935.

Author information

Authors and Affiliations

Authors

Contributions

Y.L.X. and W.T. conceived the project and designed the experiments. Y.L.X. screened deaminases, purified and characterized TadA8.20, and carried out site-specific m6A quantification experiments. S.L. performed all bioinformatic analyses. R.G. designed the workflow for eTAM-seq, optimized the protocol for generating IVT RNA and prepared the libraries. Y.W. assisted with enzyme purification and characterization as well as site-specific m6A quantification. Y.W. analyzed site-specific m6A quantification results. M.C. supervised bioinformatic analyses. C.H. and W.T. supervised the study. Y.L.X., C.H., M.C. and W.T. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Chuan He, Mengjie Chen or Weixin Tang.

Ethics declarations

Competing interests

Patent application no. 63/417,245 has been filed for eTAM-seq by the University of Chicago. C.H. is a scientific founder and a scientific advisory board member of Accent Therapeutics, Inc. and Aferna Bio, Inc. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Rui Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Notes 1–9, Tables 1–10 and Figs. 1–24.

Reporting Summary

Supplementary Table 1

TadA8.20-enabled A-to-I conversion in different sequence contexts as reported by nonmethylated RNA probes.

Supplementary Table 2

The sequencing and processing statistics of eTAM-seq libraries.

Supplementary Data 1

Unprocessed western blot for Supplementary Fig. 20a.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xiao, YL., Liu, S., Ge, R. et al. Transcriptome-wide profiling and quantification of N6-methyladenosine by enzyme-assisted adenosine deamination. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-022-01587-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41587-022-01587-6

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