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

Nature Methods volume 18, pages 1060–1067 (2021)Cite this article

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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.

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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.

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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.

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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).

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Authors and Affiliations

  1. Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

    David Dierks, Miguel Angel Garcia-Campos, Anna Uzonyi, Modi Safra, Sarit Edelheit & Schraga Schwartz

  2. Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel

    David Dierks & Ruth Scherz-Shouval

  3. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Anna Uzonyi & Yonatan Stelzer

  4. Cell Fate and Gene Regulation Laboratory, The Francis Crick Institute, London, UK

    Alice Rossi, Theodora Sideri, Radhika A. Varier & Folkert van Werven

  5. Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel

    Alexander Brandis

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  1. David Dierks
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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.

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Correspondence to Schraga Schwartz.

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The authors declare no competing interests.

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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.

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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.

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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

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