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.

Multiplexed profiling facilitates robust m6A quantification at site, gene and sample resolution

Abstract

N6-methyladenosine (m6A) is the most prevalent modification of messenger RNA in mammals. To interrogate its functions and dynamics, there is a critical need to quantify m6A at three levels: site, gene and sample. Current approaches address these needs in a limited manner. Here we develop m6A-seq2, relying on multiplexed m6A-immunoprecipitation of barcoded and pooled samples. m6A-seq2 allows a big increase in throughput while reducing technical variability, requirements of input material and cost. m6A-seq2 is furthermore uniquely capable of providing sample-level relative quantitations of m6A, serving as an orthogonal alternative to mass spectrometry-based approaches. Finally, we develop a computational approach for gene-level quantitation of m6A. We demonstrate that using this metric, roughly 30% of the variability in RNA half life in mouse embryonic stem cells can be explained, establishing m6A as a main driver of RNA stability. m6A-seq2 thus provides an experimental and analytic framework for dissecting m6A-mediated regulation at three different levels.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: m6A-seq2 reduces batch-induced variability between replicates.
Fig. 2: Development of an m6A sample index.
Fig. 3: Development of an m6A gene index.

Data availability

All the m6A-seq/2 datasets that were generated by us are stored at GSE178832. The GSE accession number of datasets that were not generated by us are listed in Supplementary Table 11. Source data are provided with this paper.

Code availability

The used in-house python script for demultiplexing as the script (bam2ReadEnds.R) used for paired-end transcript coverage are stored at https://github.com/SchwartzLab.

References

  1. 1.

    Perry, R. P. & Kelley, D. E. Existence of methylated messenger RNA in mouse L cells. Cell 1, 37–42 (1974).

    CAS  Article  Google Scholar 

  2. 2.

    Desrosiers, R., Friderici, K. & Rottman, F. Identification of methylated nucleosides in messenger RNA from Novikoff hepatoma cells. Proc. Natl Acad. Sci. USA 71, 3971–3975 (1974).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

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

    CAS  Article  Google Scholar 

  4. 4.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

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

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Wang, X. et al. N(6)-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Roundtree, I. A. et al. YTHDC1 mediates nuclear export of N6-methyladenosine methylated mRNAs. eLife 6, e31311 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Ke, S. et al. m6A mRNA modifications are deposited in nascent pre-mRNA and are not required for splicing but do specify cytoplasmic turnover. Genes Dev. 31, 990–1006 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Li, A. et al. Cytoplasmic m6A reader YTHDF3 promotes mRNA translation. Cell Res. 27, 444–447 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Liu, N. et al. N(6)-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518, 560–564 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Batista, P. J. et al. m(6)A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Cui, Q. et al. m6A RNA methylation regulates the self-renewal and tumorigenesis of glioblastoma stem cells. Cell Rep. 18, 2622–2634 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Rosa-Mercado, N. A., Withers, J. B. & Steitz, J. A. Settling the m6A debate: methylation of mature mRNA is not dynamic but accelerates turnover. Genes Dev. 31, 957–958 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Darnell, R. B., Ke, S. & Darnell, J. E. Jr. Pre-mRNA processing includes N6 methylation of adenosine residues that are retained in mRNA exons and the fallacy of ‘RNA epigenetics’. RNA 24, 262–267 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Zhao, B. S., Nachtergaele, S., Roundtree, I. A. & He, C. Our views of dynamic N6-methyladenosine RNA methylation. RNA 24, 268–272 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Zaccara, S. & Jaffrey, S. R. A unified model for the function of YTHDF proteins in regulating m6A-modified mRNA. Cell 181, 1582–1595.e18 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Molinie, B. et al. m(6)A-LAIC-seq reveals the census and complexity of the m(6)A epitranscriptome. Nat. Methods 13, 692–698 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Legrand, C. et al. Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs. Genome Res. 27, 1589–1596 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Sas-Chen, A. et al. Dynamic RNA acetylation revealed by quantitative cross-evolutionary mapping. Nature 583, 638–643 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Schwartz, S. m1A within cytoplasmic mRNAs at single nucleotide resolution: a reconciled transcriptome-wide map. RNA 24, 1427–1436 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Garcia-Campos, M. A. et al. Deciphering the ‘m6A code’ via antibody-independent quantitative profiling. Cell 178, 731–747.e16 (2019).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Schwartz, S. et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5′ sites. Cell Rep. 8, 284–296 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Schwartz, S. et al. High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis. Cell 155, 1409–1421 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Agarwala, S. D., Blitzblau, H. G., Hochwagen, A. & Fink, G. R. RNA methylation by the MIS complex regulates a cell fate decision in yeast. PLoS Genet. 8, e1002732 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Clancy, M. J., Shambaugh, M. E., Timpte, C. S. & Bokar, J. A. Induction of sporulation in Saccharomyces cerevisiae leads to the formation of N6-methyladenosine in mRNA: a potential mechanism for the activity of the IME4 gene. Nucleic Acids Res. 30, 4509–4518 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Zhuang, F., Fuchs, R. T., Sun, Z., Zheng, Y. & Robb, G. B. Structural bias in T4 RNA ligase-mediated 3′-adapter ligation. Nucleic Acids Res. 40, e54 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Fuchs, R. T., Sun, Z., Zhuang, F. & Robb, G. B. Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure. PLoS ONE 10, e0126049 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30.

    Shishkin, A. A. et al. Simultaneous generation of many RNA-seq libraries in a single reaction. Nat. Methods 12, 323–325 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Roth, S. H., Levanon, E. Y. & Eisenberg, E. Genome-wide quantification of ADAR adenosine-to-inosine RNA editing activity. Nat. Methods 16, 1131–1138 (2019).

    CAS  PubMed  Article  Google Scholar 

  32. 32.

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

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Ries, R. J. et al. m6A enhances the phase separation potential of mRNA. Nature 571, 424–428 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Anders, M. et al. Dynamic m6A methylation facilitates mRNA triaging to stress granules. Life Sci. Alliance 1, e201800113 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Lasman, L. et al. Context-dependent functional compensation between Ythdf m6A reader proteins. Genes Dev. 34, 1373–1391 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Gokhale, N. S. et al. Altered m6A modification of specific cellular transcripts affects flaviviridae infection. Mol. Cell 77, 542–555.e8 (2020).

    CAS  Article  Google Scholar 

  37. 37.

    Mauer, J. et al. Reversible methylation of m6Am in the 5′ cap controls mRNA stability. Nature 541, 371–375 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    Akichika, S. et al. Cap-specific terminal N6-methylation of RNA by an RNA polymerase II-associated methyltransferase. Science 363, eaav0080 (2019).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Boulias, K. et al. Identification of the m6Am methyltransferase PCIF1 reveals the location and functions of m6Am in the transcriptome. Mol. Cell 75, 631–643.e8 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Sun, H., Zhang, M., Li, K., Bai, D. & Yi, C. Cap-specific, terminal N6-methylation by a mammalian m6Am methyltransferase. Cell Res. 29, 80–82 (2019).

    CAS  Article  Google Scholar 

  41. 41.

    Sendinc, E. et al. PCIF1 catalyzes m6Am mRNA methylation to regulate gene expression. Mol. Cell 75, 620–630.e9 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Cowling, V. H. CAPAM: the mRNA cap adenosine N6-methyltransferase. Trends Biochem. Sci. 44, 183–185 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Zhao, B. S., Roundtree, I. A. & He, C. Post-transcriptional gene regulation by mRNA modifications. Nat. Rev. Mol. Cell Biol. 18, 31–42 (2017).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Zaccara, S., Ries, R. J. & Jaffrey, S. R. Reading, writing and erasing mRNA methylation. Nat. Rev. Mol. Cell Biol. 20, 608–624 (2019).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Schwartz, S. Cracking the epitranscriptome. RNA 22, 169–174 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Grozhik, A. V., Linder, B., Olarerin-George, A. O. & Jaffrey, S. R. Mapping m6A at individual-nucleotide resolution using crosslinking and immunoprecipitation (miCLIP). Methods Mol. Biol. 1562, 55–78 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Liu, H. et al. Accurate detection of m6A RNA modifications in native RNA sequences. Nat. Commun. 10, 4079 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    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).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

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

    CAS  PubMed  Article  Google Scholar 

  52. 52.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    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).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Lorenz, D. A., Sathe, S., Einstein, J. M. & Yeo, G. W. Direct RNA sequencing enables m6A detection in endogenous transcript isoforms at base-specific resolution. RNA 26, 19–28 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Zhang, Z. et al. RADAR: differential analysis of MeRIP-seq data with a random effect model. Genome Biol. 20, 294 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Wen, J. et al. Zc3h13 regulates nuclear RNA m6A methylation and mouse embryonic stem cell self-renewal. Mol. Cell 69, 1028–1038.e6 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Longtine, M. S. et al. Additional modules for versatile and economical PCR-based gene deletion and modification in Saccharomyces cerevisiae. Yeast 14, 953–961 (1998).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Chia, M. et al. High-resolution analysis of cell-state transitions in yeast suggests widespread transcriptional tuning by alternative starts. Genome Biol. 22, 34 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Nishimura, K., Fukagawa, T., Takisawa, H., Kakimoto, T. & Kanemaki, M. An auxin-based degron system for the rapid depletion of proteins in nonplant cells. Nat. Methods 6, 917–922 (2009).

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Chu, S. & Herskowitz, I. Gametogenesis in yeast is regulated by a transcriptional cascade dependent on Ndt80. Mol. Cell 1, 685–696 (1998).

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Levi-Galibov, O. et al. Heat Shock Factor 1-dependent extracellular matrix remodeling mediates the transition from chronic intestinal inflammation to colon cancer. Nat. Commun. 11, 6245 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Stelzer, Y., Shivalila, C. S., Soldner, F., Markoulaki, S. & Jaenisch, R. Tracing dynamic changes of DNA methylation at single-cell resolution. Cell 163, 218–229 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Safra, M. et al. The m1A landscape on cytosolic and mitochondrial mRNA at single-base resolution. Nature 551, 251–255 (2017).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).

    Article  Google Scholar 

  69. 69.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Daley, T. & Smith, A. D. Predicting the molecular complexity of sequencing libraries. Nat. Methods 10, 325–327 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

D.D., M.A.G.C., A.U., M.S., S.E. and S.S. have received funding from the Israel Science Foundation (grant no. 543165), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 714023 and ERC-POC 899122), the Estate of Emile Mimran. S.S. is the incumbent of the Robert Edward and Roselyn Rich Manson Career Development Chair in Perpetuity. R.S.S. is supported by ISF grant nos. 401/17 and 1384/1, ERC grant no. 754320, and the Laura Gurwin Flug Family Fund. R.S.S. is the incumbent of the Ernst and Kaethe Ascher Career Development Chair in Life Sciences. F.v.W., T.S., R.A.V. and A.R. are supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (grant no. FC001203), the UK Medical Research Council (grant no. FC001203) and the Wellcome Trust (grant no. FC001203).

Author information

Affiliations

Authors

Contributions

D.D. and S.S. conceived the project. M.S., S.E., A.U. and D.D. conducted all genomic experiments. A.B. performed the conducted mass-spectroscopy analysis. F.v.W., T.S., R.A.V. and A.R. generated and conducted the experiment of yeast inducible perturbations. A.U. and Y.S. conducted the experiments of the partial KO experiments of m6A writers in mESC. M.A.G.C. provided and created the in-house generated scripts that were used to perform the analysis. D.D. and S.S. performed the data analysis. R.S.S. and S.S. acquired funding. D.D., R.S.S. and S.S. wrote the paper, with input from all authors.

Corresponding author

Correspondence to Schraga Schwartz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Christopher Mason and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 m6A-seq2 reduces batch-induced variability while maintaining m6A-enrichment capacities.

m6A-seq2 reduces batch-induced variability while maintaining m6A-enrichment capacities a) Sequencing complexity metric based on the ratio of distinct- to total read numbers for 500,000 sampled reads (Supplementary Table 11) b) m6A-IP and Input normalized coverage profile of MEI5 for the 12 m6A-seq2 replicates and the 3 m6A-seq replicates c) Metagene coverage densities of m6A-IP and Input for the 12 m6A-seq2 replicates (red) and 3 m6A-seq replicates (black). d) Motif analysis showing the most significant HOMER (Supplementary Table 11) de novo motif analysis, based on detected peaks (see Methods) of the 12 m6A-seq2 replicates (red) and 3 m6A-seq replicates (black) e) m6A-seq2 number of reads of the 12 technical replicates. f) Identity of nucleotide of the first position of read 2 (top), which resembles the 3′ terminus of a sequenced RNA fragment. Stacked barplot of the mean nucleotide identity of the first 30 positions (bottom). g) Principal component analysis of m6A-site score 2 of 486 m6A sites. h Cumulative plot showing the fraction of false-positive detected ‘differential-methylation’ cases and the effect-size as the absolute fold-change of the mean score1 between the sampled groups.

Source data

Extended Data Fig. 2 m6A-seq2 reduces batch-induced variability in technical replicates of mouse embryonic fibroblasts.

m6A-seq2 reduces batch-induced variability in technical replicates of mouse embryonic fibroblasts. a) Library complexity metric as the ratio of distinct- to total read numbers based on one million reads of the m6A-seq2 and m6A-seq MEF dataset (preseq70) b) Motif analysis showing the most significant HOMER (Supplementary Table 11) de novo motif analysis, based on detected peaks (see Methods) of the m6A-seq2 technical replicates (left) and m6A-seq technical replicates (right). c) Metagene m6A-peak distribution (see Methods) of the m6A-seq technical replicates (top) and m6A-seq2 replicates (bottom). d) m6A-site score 1 comparison between technical replicates generated via m6A-seq or m6A-seq2. score2 for 3416 m6A-sites passing the coverage thresholds for high-confidence m6A site estimation (see Methods). e) Principal component analysis of m6A-site score 1 of 3416 high-confidence m6A sites. f) Comparison of the percentage coefficient of variation (%CV) of score1 (left) and score2 (right) estimates across the technical replicates measured via m6A-seq and m6A-seq2. Wilcoxon two-sided test p value annotated. Comparison of n = 3416 m6A-sites for score1 and score 2. Boxplot: Center is the median, lower and upper hinges depict the first and third quartile and the whiskers stretch to 1.5 times the interquartile range from the corresponding hinge. g) Cumulative plot showing the fraction of false-positive detected ‘differential-methylation’ cases and the effect-size as the absolute fold-change of the mean score between the sampled groups.

Source data

Extended Data Fig. 3 m6A-methylome profiling upon genetic perturbations during yeast meiosis.

m6A-methylome profiling upon genetic perturbations during yeast meiosis a) m6A-SI based on m6A-Seq2 measurements on individual genetic perturbations strains with ndt80Δ/Δ background. Barplot for each of the m6A-seq2 batches. In total 3 gene deletion strains (pho92Δ/Δ, not3Δ/Δ, gis2Δ/Δ), 12 genes with an auxin-inducible degrons (AID), 5 positive controls (only ndt80Δ/Δ, red) and 5 negative controls (ime4Δ/Δ & ndt80Δ/Δ, blue) (noted in legend) and AID-control (TIR1). U: untreated samples and T: treatment with CU and IAA (see Methods). b) Raf1 m6A-IP fragment coverage normalized by sample library size and Raf1 expression level (RPM) based on the input sample for all 24 samples (% amount of WT mESC RNA per triplicate is annotated at the right). The Y-axis is fixed to an identical range across all samples. c) Heatmap of log2 transformed m6A-site score 1 of 4505 annotated high-confidence m6A-sites (scaled by row) of the samples (merged triplicates).

Source data

Extended Data Fig. 4 m6A-GI infers with half-life and steady-state expression.

m6A-GI infers with half-life and steady-state expression a) m6A-GI of the mESC WT cells plotted against mRNA half-life in Mettl3-KO mESC b) HPLC-MS abundances of m6A across WT and m6A-writer perturbed mESC clones plotted against the ‘expression-vs-methylation index’, defined as the spearman correlation between normalized expression (TPM, see Methods) and m6A-GIs calculated based on 3 previously published m6A-seq datasets23. Annotated is the Pearson’s R coefficient and p-value (two-sided analysis) and 95 % confidence interval.

Source data

Extended Data Fig. 5 m6A-gene index correlates with the increase of m6A.

m6A-gene index correlates with the increase of m6A a) m6A-gene index (m6A-GI) of 100 % WT sample (merged triplicates) plotted against the other sample concentrations (merged triplicates) with a linear model fit (blue line). b) Scatterplot of the slope of the linear modeling fit of all unique combinations of m6A-GIs derived from different % WT mESC (as in a)), plotted against the log2 transformed fold-change (FC) of the corresponding unique combination out of the 7 samples (14 % WT to 100 % WT). Annotated is the Pearson’s R coefficient and p-value (two-sided analysis) and 95 % confidence interval c) Heatmap of the log2 transformed m6A-GIs (scaled by gene) for 6572 genes of the different samples (merged triplicates). d) Scatter plots of the m6A index of five distinct in vitro transcribed spikes (IS1-5, see Supplementary Table 1) with a defined amount of m6A ranging from 0% m6A (in red) over 7 increasing concentrations to 100% (in black). Linear-modeling and Pearson’s R determined and p-value (two-sided analysis) for all data points with m6A (black) annotated. Spikes with 0% methylation are plotted in red, and were not taken into account for the linear model.

Source data

Supplementary information

Reporting Summary

Supplementary Tables 1–12.

Contains all Supplementary Tables in different tabs.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dierks, D., Garcia-Campos, M.A., Uzonyi, A. et al. Multiplexed profiling facilitates robust m6A quantification at site, gene and sample resolution. Nat Methods 18, 1060–1067 (2021). https://doi.org/10.1038/s41592-021-01242-z

Download citation

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