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.

  • Article
  • Published:

Mild and ultrafast GLORI enables absolute quantification of m6A methylome from low-input samples

Nature Methods volume 22, pages 1226–1236 (2025)Cite this article

Subjects

Abstract

Methods for absolute quantification of N6-methyladenosine (m6A) have emerged as powerful tools in epitranscriptomics. We previously reported GLORI, a chemical-assisted approach to achieve unbiased and precise m6A measurement. However, its lengthy reaction time and severe RNA degradation have limited its applicability, particularly for low-input samples. Here, we present two updated GLORI approaches that are ultrafast, mild and enable absolute m6A quantification from one to two orders of magnitude less than the RNA starting material: GLORI 2.0 is compatible with RNA from ~10,000 cells and enhances sensitivity for both transcriptome-wide and locus-specific m6A detection; GLORI 3.0 further utilizes a reverse transcription-silent carrier RNA to achieve m6A quantification from as low as 500–1,000 cells. Using limited RNA from mouse dorsal hippocampus, we reveal a high modification level in synapse-related gene sets. We envision that the updated GLORI methods will greatly expand the applicability of absolute quantification of m6A in biology.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Optimization of deamination conditions.
Fig. 2: Transcriptome-wide m6A identification and quantification by GLORI 2.0.
Fig. 3: Site-specific m6A identification and quantification.
Fig. 4: A strategy for carrier RNA preparation.
Fig. 5: GLORI 3.0 achieves m6A profiling from ultralow-input RNA.
Fig. 6: Synaptic and cytoplasmic m6A epitranscriptome.

Similar content being viewed by others

Data availability

The sequencing data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO), under accession code GSE270643. The public GLORI 1.0 datasets were downloaded from the GEO under accession code GSE210563. Source data are provided with this paper.

Code availability

The scripts used for processing the raw data are deposited on Zenodo (https://doi.org/10.5281/zenodo.14233421)59.

References

  1. Moshitch-Moshkovitz, S., Dominissini, D. & Rechavi, G. The epitranscriptome toolbox. Cell 185, 764–776 (2022).

    Article  CAS  PubMed  Google Scholar 

  2. Roundtree, I. A., Evans, M. E., Pan, T. & He, C. Dynamic RNA modifications in gene expression regulation. Cell 169, 1187–1200 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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  PubMed  Google Scholar 

  4. Sun, H., Li, K., Liu, C. & Yi, C. Regulation and functions of non-m6A mRNA modifications. Nat. Rev. Mol. Cell Biol. 24, 714–731 (2023).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  6. 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  PubMed  PubMed Central  Google Scholar 

  7. Li, X., Xiong, X. & Yi, C. Epitranscriptome sequencing technologies: decoding RNA modifications. Nat. Methods 14, 23–31 (2016).

    Article  PubMed  Google Scholar 

  8. Harcourt, E. M., Kietrys, A. M. & Kool, E. T. Chemical and structural effects of base modifications in messenger RNA. Nature 541, 339–346 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Liu, C. et al. Absolute quantification of single-base m6A methylation in the mammalian transcriptome using GLORI. Nat. Biotechnol. 41, 355–366 (2023).

    Article  CAS  PubMed  Google Scholar 

  10. Xiao, Y. L. et al. Transcriptome-wide profiling and quantification of N6-methyladenosine by enzyme-assisted adenosine deamination. Nat. Biotechnol. 41, 993–1003 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Shen, W. et al. GLORI for absolute quantification of transcriptome-wide m6A at single-base resolution. Nat. Protoc. 19, 1252–1287 (2024).

    Article  CAS  PubMed  Google Scholar 

  12. Shapiro, R. & Pohl, S. H. The reaction of ribonucleosides with nitrous acid. Side products and kinetics. Biochemistry 7, 448–455 (1968).

    Article  CAS  PubMed  Google Scholar 

  13. Schuster, H. & Wilhelm, R. C. Reaction differences between tobacco mosaic virus and its free ribonucleic acid with nitrous acid. Biochim. Biophys. Acta 68, 554–560 (1963).

    Article  CAS  PubMed  Google Scholar 

  14. Shapiro, R. & Hachmann, J. The reaction of guanine derivatives with 1,2-dicarbonyl compounds. Biochemistry 5, 2799–2807 (1966).

    Article  CAS  PubMed  Google Scholar 

  15. Nakaya, K., Takenaka, O., Horinishi, H. & Shibata, K. Reactions of glyoxal with nucleic acids. Nucleotides and their component bases. Biochim. Biophys. Acta 161, 23–31 (1968).

    Article  CAS  PubMed  Google Scholar 

  16. Knutson, S. D. et al. Thermoreversible control of nucleic acid structure and function with glyoxal caging. J. Am. Chem. Soc. 142, 17766–17781 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Karloff, D. B. et al. Glyoxal caging of nucleoside antivirals toward self-activating, extended-release prodrugs. J. Am. Chem. Soc. 146, 29402–29406 (2024).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  19. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  21. Harcourt, E. M., Ehrenschwender, T., Batista, P. J., Chang, H. Y. & Kool, E. T. Identification of a selective polymerase enables detection of N6-methyladenosine in RNA. J. Am. Chem. Soc. 135, 19079–19082 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hong, T. et al. Precise antibody-independent m6A identification via 4SedTTP-involved and FTO-assisted strategy at single-nucleotide resolution. J. Am. Chem. Soc. 140, 5886–5889 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Bai, D. et al. Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq. Nat. Biotechnol. 43, 85–96 (2024).

    Article  PubMed  Google Scholar 

  24. Guo, H. et al. The DNA methylation landscape of human early embryos. Nature 511, 606–610 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. Yoshida, M. & Ukita, T. Modification of nucleosides and nucleotides. VII. Selective cyanoethylation of inosine and pseudouridine in yeast transfer ribonucleic acid. Biochim. Biophys. Acta 157, 455–465 (1968).

    Article  CAS  PubMed  Google Scholar 

  26. Ofengand, J. The function of pseudouridylic acid in transfer ribonucleic acid. I. The specific cyanoethylation of pseudouridine, inosine, and 4-thiouridine by acrylonitrile. J. Biol. Chem. 242, 5034–5045 (1967).

    Article  CAS  PubMed  Google Scholar 

  27. Sakurai, M., Yano, T., Kawabata, H., Ueda, H. & Suzuki, T. Inosine cyanoethylation identifies A-to-I RNA editing sites in the human transcriptome. Nat. Chem. Biol. 6, 733–740 (2010).

    Article  CAS  PubMed  Google Scholar 

  28. Li, Y. et al. Single-cell m6A mapping in vivo using picoMeRIP-seq. Nat. Biotechnol. 42, 591–596 (2024).

    Article  CAS  PubMed  Google Scholar 

  29. Hamashima, K. et al. Single-nucleus multiomic mapping of m6A methylomes and transcriptomes in native populations of cells with sn-m6A-CT. Mol. Cell https://doi.org/10.1016/j.molcel.2023.08.010 (2023).

  30. Yao, H. et al. scm6A-seq reveals single-cell landscapes of the dynamic m6A during oocyte maturation and early embryonic development. Nat. Commun. 14, 315 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Tegowski, M., Flamand, M. N. & Meyer, K. D. scDART-seq reveals distinct m6A signatures and mRNA methylation heterogeneity in single cells. Mol. Cell 82, 868–878 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Martin, K. C. & Ephrussi, A. mRNA localization: gene expression in the spatial dimension. Cell 136, 719–730 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Jung, H., Yoon, B. C. & Holt, C. E. Axonal mRNA localization and local protein synthesis in nervous system assembly, maintenance and repair. Nat. Rev. Neurosci. 13, 308–324 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Shi, H. et al. m6A facilitates hippocampus-dependent learning and memory through YTHDF1. Nature 563, 249–253 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhang, Z. et al. METTL3-mediated N6-methyladenosine mRNA modification enhances long-term memory consolidation. Cell Res. 28, 1050–1061 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Merkurjev, D. et al. Synaptic N6-methyladenosine (m6A) epitranscriptome reveals functional partitioning of localized transcripts. Nat. Neurosci. 21, 1004–1014 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Flamand, M. N. & Meyer, K. D. m6A and YTHDF proteins contribute to the localization of select neuronal mRNAs. Nucleic Acids Res. 50, 4464–4483 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Livneh, I., Moshitch-Moshkovitz, S., Amariglio, N., Rechavi, G. & Dominissini, D. The m6A epitranscriptome: transcriptome plasticity in brain development and function. Nat. Rev. Neurosci. 21, 36–51 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. Yu, Y. et al. Bi-functionality of glyoxal caged nucleic acid coupled with CRISPR/Cas12a system for Hg2+ determination. Mikrochim. Acta 191, 120 (2024).

    Article  CAS  PubMed  Google Scholar 

  40. Knutson, S. D., Arthur, R. A., Johnston, H. R. & Heemstra, J. M. Selective enrichment of A-to-I edited transcripts from cellular RNA using endonuclease V. J. Am. Chem. Soc. 142, 5241–5251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Xiao, Z. et al. Holo-Seq: single-cell sequencing of holo-transcriptome. Genome Biol. 19, 163 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Taryma-Lesniak, O., Sokolowska, K. E. & Wojdacz, T. K. Current status of development of methylation biomarkers for in vitro diagnostic IVD applications. Clin. Epigenetics 12, 100 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Barbieri, I. & Kouzarides, T. Role of RNA modifications in cancer. Nat. Rev. Cancer 20, 303–322 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Delaunay, S. & Frye, M. RNA modifications regulating cell fate in cancer. Nat. Cell Biol. 21, 552–559 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Suzuki, T., Ueda, H., Okada, S. & Sakurai, M. Transcriptome-wide identification of adenosine-to-inosine editing using the ICE-seq method. Nat. Protoc. 10, 715–732 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Wang, Y. et al. LEAD-m6A-seq for locus-specific detection of N6-methyladenosine and quantification of differential methylation. Angew. Chem. Int. Ed. Engl. 60, 873–880 (2021).

    Article  CAS  PubMed  Google Scholar 

  47. Shen, W., Sipos, B. & Zhao, L. Y. SeqKit2: a Swiss army knife for sequence and alignment processing. Imeta 3, e191 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Park, Y., Figueroa, M. E., Rozek, L. S. & Sartor, M. A. MethylSig: a whole genome DNA methylation analysis pipeline. Bioinformatics 30, 2414–2422 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of Gene Ontology terms. PLoS ONE 6, e21800 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  55. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Lu, B. Codes for "Mild and ultrafast GLORI enables absolute quantification of m6A methylome from low-input samples". Zenodo https://doi.org/10.5281/zenodo.14233421 (2024).

Download references

Acknowledgements

We thank the National Center for Protein Sciences at Peking University for assistance with library size distribution. We thank the High Performance Computing Platform of the Center for Life Science for assistance with the analysis. We thank the Analytical Instrumentation Center in Peking University for assistance with identification of intermediates and X. Liu for help with the analysis of LC–TOF–MS. We thank H. Yao and Y. Cai from Peking University for discussion on chemistry experiments. This work was supported by the National Key R&D Program of China (2023YFC3402200 to C.Y.), the National Natural Science Foundation of China (22425071 to C.Y.), the Beijing Natural Science Foundation (Z220013 to C.Y., 5234030 to B.L.), the Beijing Municipal Science & Technology Commission (Z231100007223010 to X.-J.W.), the China National Postdoctoral Program for Innovative Talents (BX20230029 to H.S.; BX20230411 to Z. Zhang) and the China Postdoctoral Science Foundation (GZB20230026 to B.L.). This work was supported by the New Cornerstone Science Foundation through the XPLORER PRIZE.

Author information

Author notes

  1. These authors contributed equally: Hanxiao Sun, Bo Lu, Zeyu Zhang.

Authors and Affiliations

  1. State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China

    Hanxiao Sun, Bo Lu, Zhe Zhou, Zhichao Li, Zhe Jiang, Jiayi Zhang, Cong Liu, Jinying Peng & Chengqi Yi

  2. Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Zeyu Zhang, Meng Wang & Xiu-Jie Wang

  3. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Ye Xiao, Lin Xi & Yichen Ma

  4. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Ye Xiao, Yichen Ma & Chengqi Yi

  5. School of Future Technology, University of Chinese Academy of Sciences, Beijing, China

    Xiu-Jie Wang

  6. Department of Chemical Biology and Synthetic and Functional Biomolecules Center, College of Chemistry and Molecular Engineering, Peking University, Beijing, China

    Chengqi Yi

  7. Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing, China

    Chengqi Yi

Authors
  1. Hanxiao Sun
    View author publications

    Search author on:PubMed Google Scholar

  2. Bo Lu
    View author publications

    Search author on:PubMed Google Scholar

  3. Zeyu Zhang
    View author publications

    Search author on:PubMed Google Scholar

  4. Ye Xiao
    View author publications

    Search author on:PubMed Google Scholar

  5. Zhe Zhou
    View author publications

    Search author on:PubMed Google Scholar

  6. Lin Xi
    View author publications

    Search author on:PubMed Google Scholar

  7. Zhichao Li
    View author publications

    Search author on:PubMed Google Scholar

  8. Zhe Jiang
    View author publications

    Search author on:PubMed Google Scholar

  9. Jiayi Zhang
    View author publications

    Search author on:PubMed Google Scholar

  10. Meng Wang
    View author publications

    Search author on:PubMed Google Scholar

  11. Cong Liu
    View author publications

    Search author on:PubMed Google Scholar

  12. Yichen Ma
    View author publications

    Search author on:PubMed Google Scholar

  13. Jinying Peng
    View author publications

    Search author on:PubMed Google Scholar

  14. Xiu-Jie Wang
    View author publications

    Search author on:PubMed Google Scholar

  15. Chengqi Yi
    View author publications

    Search author on:PubMed Google Scholar

Contributions

C.Y., X.-J.W., H.S. and B.L. conceived the project and designed the experiments; C.Y., H.S., B.L. and Z. Zhang wrote the manuscript with the help of X.-J.W., Z.J. and J.P.; H.S., Y.X. and Z. Zhang performed the experiments with the help of Z.L., L.X., Y.M. and J.Z.; H.S. develop GLORI 2.0 and GLORI 3.0 and identify intermediates; B.L. designed and performed the bioinformatics analysis with the help of Z. Zhou and C.L.; Z. Zhang performed mouse experiments with the help of M.W.; C.Y. and X.-J.W. supervised the project.

Corresponding authors

Correspondence to Xiu-Jie Wang or Chengqi Yi.

Ethics declarations

Competing interests

A patent application has been filed by Peking University for the technology disclosed in this publication. C.Y. and H.S. are coinventors on a patent application describing GLORI 2.0 and GLORI 3.0. The other authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Qihan Chen, Kate Meyer and Weixin Tang for their contribution to the peer review of this work. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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 Figs. 1–24 and Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Data 1

m6A sites identified by GLORI 2.0 with 50 ng, 10 ng and 2 ng mRNA input (300 M raw reads).

Supplementary Data 2

m6A sites identified by GLORI 2.0 after STM2457 treatment (300 M raw reads).

Supplementary Data 3

m6A sites identified by GLORI 1.0 with 50 ng and 10 ng mRNA input (300 M raw reads).

Supplementary Data 4

m6A sites identified by GLORI 3.0 with 100 ng, 20 ng and 10 ng total RNA input (300 M raw reads).

Supplementary Data 5

m6A sites identified by GLORI 3.0 in synapse and cytoplasm (300 M raw reads).

Supplementary Data 6

Differentially methylated m6A sites between synapse and cytoplasm (300 M raw reads).

Supplementary Data 7

Library quality assessment.

Supplementary Data 8

Primer and spike-in sequences.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Original Gel Figures

Unprocessed gel images.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, H., Lu, B., Zhang, Z. et al. Mild and ultrafast GLORI enables absolute quantification of m6A methylome from low-input samples. Nat Methods 22, 1226–1236 (2025). https://doi.org/10.1038/s41592-025-02680-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41592-025-02680-9

Search

Advanced 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