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.

  • Resource
  • Published:

Single-cell m6A profiling in the mouse brain uncovers cell type-specific RNA methylomes and age-dependent differential methylation

Nature Neuroscience volume 27, pages 2512–2520 (2024)Cite this article

Subjects

Abstract

N6-methyladenosine (m6A) is an abundant mRNA modification in the brain that has important roles in neurodevelopment and brain function. However, because of technical limitations, global profiling of m6A sites within the individual cell types that make up the brain has not been possible. Here, we develop a mouse model that enables transcriptome-wide m6A detection in any tissue of interest at single-cell resolution. We use these mice to map m6A across different brain regions and within single cells of the mouse cortex and discover a high degree of shared methylation across brain regions and cell types. However, we also identify a small number of differentially methylated mRNAs in neurons that encode important regulators of neuronal signaling, and we discover that microglia have lower levels of m6A than other cell types. Finally, we perform single-cell m6A mapping in aged mice and identify many transcripts with age-dependent changes in m6A.

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: Identification of m6A sites in vivo using DART mice.
Fig. 2: Single-cell m6A profiling in the mouse cortex.
Fig. 3: Differential methylation of RNA subsets across cell types in the mouse cortex.
Fig. 4: Patterns of m6A methylation across neuronal subtypes.
Fig. 5: Distinguishing cells based on their m6A profiles.
Fig. 6: Analysis of differential methylation in the cortex during aging.

Similar content being viewed by others

Data availability

All sequencing data generated by this study have been deposited to the Gene Expression Omnibus under Bioproject PRJNA996152 (accession numbers GSE237871 (bulk brain regions), GSE268159 (aged mouse scRNA-seq), GSE266876 (young cortex scRNA-seq), GSE267234 (sorted neuronal nuclei) and GSE267627 (GLORI)). Proteomics data have been deposited to the MassIVE Database (accession number MSV000094375). Owing to size limitations, images in the study are available upon request to the corresponding author. Source data are provided with this paper.

Code availability

All code for downloading, installing and using Bullseye is available at https://github.com/mflamand/Bullseye. Other R code is available upon request. A searchable list of m6A sites is available at http://dartsource.duke.edu.

References

  1. 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  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  3. 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 

  4. Wang, Y. et al. N6-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications. Nat. Neurosci. 21, 195–206 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yoon, K.-J. et al. Temporal control of mammalian cortical neurogenesis by m6A methylation. Cell 171, 877–889.e17 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Xu, H. et al. m6A mRNA methylation is essential for oligodendrocyte maturation and CNS myelination. Neuron 105, 293–309.e5 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Koranda, J. L. et al. Mettl14 is essential for epitranscriptomic regulation of striatal function and learning. Neuron 99, 283–292.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Engel, M. et al. The role of m6A/m-RNA methylation in stress response regulation. Neuron 99, 389–403.e9 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 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 

  10. Widagdo, J. et al. Experience-dependent accumulation of N6-methyladenosine in the prefrontal cortex is associated with memory processes in mice. J. Neurosci. 36, 6771–6777 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hess, M. E. et al. The fat mass and obesity associated gene (Fto) regulates activity of the dopaminergic midbrain circuitry. Nat. Neurosci. 16, 1042–1048 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Yang, C. et al. The role of m6A modification in physiology and disease. Cell Death Dis. 11, 960 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jiang, L. et al. Interaction of tau with HNRNPA2B1 and N6-methyladenosine RNA mediates the progression of tauopathy. Mol. Cell 81, 4209–4227.e12 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Han, M. et al. Abnormality of m6A mRNA methylation is involved in Alzheimer’s disease. Front. Neurosci. 14, 98 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Flamand, M. N. & Meyer, K. D. The epitranscriptome and synaptic plasticity. Curr. Opin. Neurobiol. 59, 41–48 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 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 

  17. Weng, Y.-L. et al. Epitranscriptomic m6A regulation of axon regeneration in the adult mammalian nervous system. Neuron 97, 313–325.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Madugalle, S. U., Meyer, K., Wang, D. O. & Bredy, T. W. RNA N6-methyladenosine and the regulation of RNA localization and function in the brain. Trends Neurosci. 43, 1011–1023 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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 

  22. Salpietro, V. et al. AMPA receptor GluA2 subunit defects are a cause of neurodevelopmental disorders. Nat. Commun. 10, 3094 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Carayol, J. et al. Converging evidence for an association of ATP2B2 allelic variants with autism in male subjects. Biol. Psychiatry 70, 880–887 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Yang, W. et al. The evidence for association of ATP2B2 polymorphisms with autism in Chinese Han population. PLoS ONE 8, e61021 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. O’Brien, R. J. & Wong, P. C. Amyloid precursor protein processing and Alzheimer’s disease. Annu. Rev. Neurosci. 34, 185–204 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Shinohara, M., Tachibana, M., Kanekiyo, T. & Bu, G. Role of LRP1 in the pathogenesis of Alzheimer’s disease: evidence from clinical and preclinical studies. J. Lipid Res. 58, 1267–1281 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Castellanos-Rubio, A. et al. A novel RT–QPCR-based assay for the relative quantification of residue specific m6A RNA methylation. Sci. Rep. 9, 4220 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Liu, J. et al. Landscape and regulation of m6A and m6Am methylome across human and mouse tissues. Mol. Cell 77, 426–440.e6 (2020).

    Article  CAS  PubMed  Google Scholar 

  29. Chang, M. et al. Region-specific RNA m6A methylation represents a new layer of control in the gene regulatory network in the mouse brain. Open Biol. 7, 170166 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Huang, H., Camats-Perna, J., Medeiros, R., Anggono, V. & Widagdo, J. Altered expression of the m6A methyltransferase METTL3 in Alzheimer’s disease. eNeuro 7, ENEURO.0125-20.2020 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Uzonyi, A. et al. Exclusion of m6A from splice-site proximal regions by the exon junction complex dictates m6A topologies and mRNA stability. Mol. Cell 83, 237–251.e7 (2023).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Grozhik, A. V. & Jaffrey, S. R. Distinguishing RNA modifications from noise in epitranscriptome maps. Nat. Chem. Biol. 14, 215–225 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Choi, S. H. et al. RBM45 is an m6A-binding protein that affects neuronal differentiation and the splicing of a subset of mRNAs. Cell Rep. 40, 111293 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Cuevas-Diaz Duran, R., González-Orozco, J. C., Velasco, I. & Wu, J. Q. Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases. Front. Cell Dev. Biol. 10, 884748 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 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 

  38. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  39. Polioudakis, D. et al. A single-cell transcriptomic atlas of human neocortical development during mid-gestation. Neuron 103, 785–801.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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 

  42. Flamand, M. N., Ke, K., Tamming, R. & Meyer, K. D. Single-molecule identification of the target RNAs of different RNA binding proteins simultaneously in cells. Genes Dev. 36, 1002–1015 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Zou, Z. et al. FMRP phosphorylation modulates neuronal translation through YTHDF1. Mol. Cell 83, 4304–4317.e8 (2023).

    Article  CAS  PubMed  Google Scholar 

  44. McMillan, M. et al. RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia. Mol. Cell 83, 219–236.e7 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Shafik, A. M. et al. N6-methyladenosine dynamics in neurodevelopment and aging, and its potential role in Alzheimer’s disease. Genome Biol. 22, 17 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Izzy, S. et al. Time-dependent changes in microglia transcriptional networks following traumatic brain injury. Front. Cell. Neurosci. 13, 307 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wolf, S. A., Boddeke, H. W. G. M. & Kettenmann, H. Microglia in physiology and disease. Annu. Rev. Physiol. 79, 619–643 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Yeh, H. & Ikezu, T. Transcriptional and epigenetic regulation of microglia in health and disease. Trends Mol. Med. 25, 96–111 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Li, Q., Wen, S., Ye, W., Zhao, S. & Liu, X. The potential roles of m6A modification in regulating the inflammatory response in microglia. J. Neuroinflammation 18, 149 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Wen, L. et al. The m6A methyltransferase METTL3 promotes LPS-induced microglia inflammation through TRAF6/NF-κB pathway. NeuroReport 33, 243–251 (2022).

    Article  CAS  PubMed  Google Scholar 

  51. Yang, X., Triboulet, R., Liu, Q., Sendinc, E. & Gregory, R. I. Exon junction complex shapes the m6A epitranscriptome. Nat. Commun. 13, 7904 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  53. He, P. C. et al. Exon architecture controls mRNA m6A suppression and gene expression. Science 379, 677–682 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Keller, L. et al. The obesity related gene, FTO, interacts with APOE, and is associated with Alzheimer’s disease risk: a prospective cohort study. J. Alzheimer’s Dis 23, 461–469 (2011).

    Article  CAS  Google Scholar 

  55. Castro-Hernández, R. et al. Conserved reduction of m6A RNA modifications during aging and neurodegeneration is linked to changes in synaptic transcripts. Proc. Natl Acad. Sci. USA 120, e2204933120 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Zhao, F. et al. METTL3-dependent RNA m6A dysregulation contributes to neurodegeneration in Alzheimer’s disease through aberrant cell cycle events. Mol. Neurodegener. 16, 70 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Li, H. et al. FTO is involved in Alzheimer’s disease by targeting TSC1–mTOR–Tau signaling. Biochem. Biophys. Res. Commun. 498, 234–239 (2018).

    Article  CAS  PubMed  Google Scholar 

  58. Huang, H., Song, R., Wong, J. J.-L., Anggono, V. & Widagdo, J. The N6-methyladenosine RNA landscape in the aged mouse hippocampus. Aging Cell 22, e13755 (2023).

    Article  CAS  PubMed  Google Scholar 

  59. Zhu, H., Yin, X., Holley, C. L. & Meyer, K. D. Improved methods for deamination-based m6A detection. Front. Cell Dev. Biol. 10, 888279 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Kulkarni, A., Anderson, A. G., Merullo, D. P. & Konopka, G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr. Opin. Biotechnol. 58, 129–136 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Prakadan, S. M., Shalek, A. K. & Weitz, D. A. Scaling by shrinking: empowering single-cell ‘omics’ with microfluidic devices. Nat. Rev. Genet. 18, 345–361 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Roehr, J. T., Dieterich, C. & Reinert, K. Flexbar 3.0—SIMD and multicore parallelization. Bioinformatics 33, 2941–2942 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  66. 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  PubMed  PubMed Central  Google Scholar 

  67. 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 

  68. Olarerin-George, A. O. & Jaffrey, S. R. MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites. Bioinformatics 33, 1563–1564 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  PubMed  Google Scholar 

  70. Sherman, B. T. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  PubMed  Google Scholar 

  72. Stuart, T. et al. Comprehensive Integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241.e26 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all members of the Meyer laboratory for helpful discussions. We are grateful to the Duke Center for Genomic and Computational Biology, the Duke Human Vaccine Institute Flow Cytometry Core, the Duke Light Microscopy Core Facility and the Duke Proteomics and Metabolomics Shared Resource for providing the infrastructure and support for sequencing, sorting of single cells, confocal imaging, ultra-performance liquid chromatography–tandem mass spectrometry and proteomics mass spectrometry. This work was supported by the Rita Allen Foundation Scholars Program to K.D.M., the Kinship Foundation Searle Scholars Program to K.D.M. and the National Institutes of Health (R01MH118366, DP1DA046584 and RM1HG011563 to K.D.M.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Biochemistry, Duke University School of Medicine, Durham, NC, USA

    Matthew Tegowski, Anna K. Prater & Kate D. Meyer

  2. Department of Medicine, Duke University School of Medicine, Durham, NC, USA

    Christopher L. Holley

  3. Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA

    Christopher L. Holley

  4. Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA

    Kate D. Meyer

Authors
  1. Matthew Tegowski
    View author publications

    Search author on:PubMed Google Scholar

  2. Anna K. Prater
    View author publications

    Search author on:PubMed Google Scholar

  3. Christopher L. Holley
    View author publications

    Search author on:PubMed Google Scholar

  4. Kate D. Meyer
    View author publications

    Search author on:PubMed Google Scholar

Contributions

K.D.M. and M.T. conceptualized the project, designed experiments and interpreted data. A.P. performed PCR and Sanger sequencing as well as immunofluorescence staining, imaging and analysis. A.P. assisted with single-cell isolation of mouse cortex. C.L.H. oversaw the ultra-performance liquid chromatography–tandem mass spectrometry experiments to quantify m6A and supplied and tested the standards. M.T. performed all other experiments and data analysis. M.T. and K.D.M. wrote the manuscript.

Corresponding author

Correspondence to Kate D. Meyer.

Ethics declarations

Competing interests

K.D.M. has filed a patent application for the DART-seq technology through Duke University. All other authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Peng Jin, Hongjun Song 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.

Extended data

Extended Data Fig. 1 Tamoxifen-inducible APOBEC1-YTH expression in DART mice.

a, Western blots from liver, lung, kidney, and heart showing inducible transgene expression in APOBEC1-YTH mice (DART mice; top row) and APOBEC1-YTHmut mice (bottom row). Blots are representative of n = 2 biological replicates. GAPDH was run concurrently on a separate identical blot. b, Immunofluorescence of APOBEC1-YTH expression in a coronal DART mouse brain section. Expression is induced across all layers of the cortex. Images are representative of data from n = 2 vehicle-treated and n = 3 tamoxifen-treated mice, each biological replicates). c, Western blot comparing APOBEC1-YTH expression after different induction protocols. Blots are representative of n = 2 biological replicates. GAPDH was run concurrently on a separate identical blot. d, Quantification of %C2U adjacent to Zhx1 A2295 after each induction protocol (n = 2 biological replicates for 4 days tamoxifen +1 and 5 days tamoxifen + 3; n = 3 biological replicates for 5 days tamoxifen + 7). e, Western blot detecting METTL3 from the cortex of mice treated with corn oil or tamoxifen. Image is representative of n = 3 biological replicates. GAPDH was run concurrently on a separate identical blot. f, Quantification of western blots from (e). n = 3 biological replicates. Corn oil mean = 1. Tamoxifen mean 1.18. p = 0.44. Error bars represent standard deviation.

Source data

Extended Data Fig. 2 Characterization of tamoxifen-inducible C-to-U editing at m6A sites in vivo.

a, Sanger sequencing traces indicating C-to-U editing adjacent to m6A sites in five different mRNAs across tissue types following tamoxifen induction of APOBEC1-YTH. Arrows indicate cytidines adjacent to m6A sites. Data are representative of 3 biological replicates. b, Quantification of editing percentage (%C2U) in each tissue for sites in the Arl5a, Uba5, and Zhx1 mRNAs. Methylation at the indicated sites differs across tissues. Error bars represent standard error (n = 3 biological replicates). Mean values for Arl5a: Cortex = 25.33, Liver = 39.0, Lung = 17.67, Kidney = 24.0, Heart = 30.33. Mean values for Uba5: Cortex =18.33, Liver =19.33, Lung = 14.0, Kidney = 15.33, Heart = 34.33. Mean values for Zhx1: Cortex = 20.33, Liver = 26.67, Lung = 13.67, Kidney = 16.33, Heart = 27.33. c, Quantification of editing percentage (%C2U) in each tissue adjacent to m6A sites in the Chd4, and Man2b1 mRNAs. Methylation at the indicated sites is consistent across tissues. Error bars represent standard error (n = 3 biological replicates). Mean values for Chd4: Cortex = 42.67, Liver = 46.0, Lung = 40.0, Kidney = 44.67, Heart = 46.67. Mean values for Man2b1: Cortex = 35.67, Liver = 38.67, Lung = 44.33, Kidney = 44.67, Heart =33.0. d, Pie chart indicating the proportion of m6A sites in the cortex with non-adjacent editing events within a 25nt window. e, Histogram showing the number of non-adjacent editing events observed in each 25nt window for the 53% of m6A sites in (d) that contain non-adjacent edits.

Source data

Extended Data Fig. 3 C-to-U editing in vivo requires APOBEC1-YTH induction and m6A binding.

a, Sanger sequencing traces indicate a lack of C-to-U editing in the absence of APOBEC1-YTH induction. DART mice were injected with corn oil (vehicle control, n = 1 biological replicates) and RT-PCR/Sanger sequencing was performed to assess editing adjacent to m6A sites in four different mRNAs across tissue types. Arrows indicate cytidines adjacent to m6A sites. b-f, Quantification of %C2U values adjacent to m6A sites in cellular mRNAs from APOBEC1-YTHmut-expressing mice compared to APOBEC1-YTH-expressing mice. C-to-U editing is substantially reduced in APOBEC1-YTHmut-expressing mice, indicating that editing in DART mice depends on m6A recognition. Error bars represent standard error (n = 3 biological replicates). Mean values for APOBEC1-YTH samples are the same as Extended Data Fig. 2b. Mean values for APOBEC1-YTHmut samples are: Arl5a - Cortex = 3.67, Liver = 3.67, Lung = 4.33, Kidney = 3.33, Heart = 6.0; Uba5 - Cortex = 8.33, Liver = 7.67, Lung = 6.0, Kidney = 9.0, Heart = 9.0; Zhx1 - Cortex = 10.67, Liver = 6.67, Lung = 7.0, Kidney = 7.0, Heart = 10.33; Chd4 - Cortex = 1.33, Liver = 2.67, Lung = 2.67, Kidney = 1.67, Heart = 3.33; Man2b1 - Cortex = 6.0, Liver = 1.67, Lung = 3.33, Kidney = 2.33, Heart = 4.33.

Source data

Extended Data Fig. 4 Characterization of gene expression and m6A methylation in the cortex, hippocampus, and cerebellum.

a, Hierarchical clustering of gene expression similarity scores across samples from each brain region expressing either APOBEC1-YTH or APOBEC1-YTHmut. Samples primarily cluster by brain region, not by which transgene is expressed. b, Volcano plots indicating the number of differentially expressed genes between APOBEC1-YTH-expressing brain regions. Significance was determined using DESeq2 (negative binomal regression followed by Wald test and FDR-based p-value adjustment). c, Volcano plots indicating the small number of gene expression differences between APOBEC1-YTH and APOBEC1-YTHmut-expressing cells in each brain region. Numbers indicate the number of up/downregulated genes. Significance was determined using DESeq2 (negative binomal regression followed by Wald test and FDR-based p-value adjustment). d, Number of differentially expressed RNAs between the cortex of DART mice and wild type mice29,62. There are similar numbers of differentially expressed RNAs across all samples, indicating that APOBEC1-YTH expression does not substantially alter gene expression patterns. e, Distribution of %C2U values at m6A sites identified in the cortex (n = 17,878 sites from 4 biological replicates), hippocampus (n = 13,461 sites from 4 biological replicates), and cerebellum (11,391 sites from 4 biological replicates). Significance was determined using a two-sided Wilcoxon rank-sum test. Center line in box represents median, while the box represents the 25th-75th percentile. Whiskers represent the highest and lowest values (1.5x interquartile range), with outliers shown as dots. Cortex vs. Hippocampus p-value = 0.004. Cortex vs. Cerebellum p-value = < 2.2e-16. Hippocampus vs. Cerebellum p-value = < 2.2e-16. * = p < 0.05. **** = p < 0.0001. f, Metagene analysis showing the distribution of m6A sites identified in DART mice in each brain region. Significance was determined using a two-sided t-test with no p-value adjustment. g, m6A sites identified in the cortex of DART mice are enriched in long internal exons. Barplot indicates the number of methylated internal exons relative to the total number of internal exons within each group. Solid line at 1 indicates no enrichment. h, Overlap of methylated RNAs identified by in vivo DART-seq and antibody-based m6A profiling in each brain region. i, Independent validation of methylation at randomly selected m6A sites using RT-qPCR-based relative m6A quantification. Dotted line at 1 indicates no detectable methylation. n = 3 biological replicates. Error bars represent standard error. Significance was determined using a one-sided t-test comparing to 1. Mean for Paqr8 = 49.41, mean for Ube2v1 = 6.31, mean for Nova2 = 1,375. j, Overlap of individual m6A sites identified across all three brain regions. k, Comparison of APOBEC1-YTH transgene RNA levels across brain regions. The number of normalized counts mapping to rat APOBEC1 is shown. Significance was determined using DESeq2 (negative binomal regression followed by Wald test and FDR-based p-value adjustment). Error bars represent standard error (n = 4 biological replicates). Mean for APOBEC1-YTH samples: Cortex = 2,218; Hippocampus = 2,644; Cerebellum = 1,278. Mean for APOBEC1-YTH samples: Cortex = 2,680; Hippocampus = 2,855; Cerebellum = 1,304. * = p < 0.05. **** = p < 0.0001. l, Bar chart showing the number of overlapping methylated RNAs between DART-seq in the cortex and MeRIP-seq datasets (Liu et al 2020) across mouse tissues. m, Clustering of m6A methylation patterns from mouse brain regions and other organs using the DART-seq and Liu et al 2020 datasets. n, Gene expression analysis of m6A writers, readers, erasers, and core EJC components derived from RNA-seq data from Liu et al 2020. Log2FC represents brain relative to other tissues. o, Validation of differential m6A levels using RT-qPCR-based m6A quantification. Two sites identified with in vivo DART-seq as being differentially methylated between the cortex and cerebellum (n = 4 biological replicates) and two sites differentially methylated between the cortex and hippocampus (n = 2 biological replicates) are validated. Data are plotted as the relative methylation level detected in cortex, normalized to either cerebellum or hippocampus from the same animal. Individual data points are shown with lines indicating brain regions from the same animal. Error bars represent standard error. Significance was determined using a one-sided t-test comparing values to 1. *** = p < 0.01.

Source data

Extended Data Fig. 5 Characteristics of gene expression and RNA methylation in the adult mouse cortex.

a, Density plot showing the number of total RNA-seq reads per cell from replicate mice with the indicated genotype. b, Density plot showing the percentage of reads mapping to mitochondrial mRNAs in replicate mice with the indicated genotype. The vast majority of cells in all replicates show very low mitochondrial representation (< 2%). c, Metagene analysis of RNA-seq reads from bulk and scDART-seq data using split-pool barcoding. Although there is an enrichment of reads at the 3’ end of transcripts using split-pool barcoding scRNA-seq, good coverage is obtained across transcript length. d, Marker gene expression across individual cell types in the cortex. Dot color indicates average normalized expression in the indicated cell type. Dot size indicates percentage of cells that express the marker gene. e, Heatmap showing marker gene expression within different cell types in the cortex. f, Barplot showing the enrichment of m6A sites in long internal exons from scDART-seq analysis of the mouse cortex. Enrichment indicates the number of methylated internal exons relative to the total number of internal exons within each group. g, Overlap of methylated RNAs identified by scDART-seq and bulk DART-seq in the mouse cortex (left). Overlap of methylated sites identified in the cortex using scDART-seq and bulk DART-seq (middle). Overlap of methylated RNAs identified by scDART-seq and MeRIP-seq in the mouse cortex (right). h, Cumulative distribution plot as in (d), but showing each cell type separately. i, Cumulative distribution plot of all m6A sites identified in the mouse cortex and the percentage of cells in which each site is methylated. Only cells expressing the parent RNA were considered (n = 27,400). j, Cumulative distribution plot of methylated RNAs identified in the mouse cortex and the percentage of cells in which the RNA is methylated. Cells that do not express the RNA were excluded from the analysis (n = 3,991). k, Cumulative distribution plot as in (c), but showing each cell type separately. l, Overlap of methylated RNAs identified by performing nuclear DART-seq on mouse cortical neurons and those identified in neurons from the scDART-seq dataset. Glut = Glutamatergic Neuron; GABA = GABAergic Neuron; Oligo = Oligodendrocyte; Micro = Microglia; CR = Cajal-Retzius Cell; Macro = Peripheral Macrophage; SMC = Smooth Muscle Cell.

Extended Data Fig. 6 Analysis of m6A in microglia and other cortical cell types.

a, Total number of m6A sites per cell within the indicated cell types, not normalized to read counts. Red dot indicates the mean. b, Total number of reads detected per cell within each cell type. Red dot indicates the mean. c, All single-cell %C2U values for m6A sites identified within each cell type (Glut n = 280,790; GABA n = 11,214; Astro n = 19,370; Oligo n = 4,769; Micro n = 133; CR n = 211; Ependy n = 6,775; Endo n = 6,314; Peri n = 1,949; Macro n = 935; SMC n = 2,232). Boxes show 25-75th percentiles and the median, with whiskers for the highest and lowest values and outliers shown. d, Number of m6A sites per cell, normalized to read coverage, identified in microglia (n = 54 cells) and glutamatergic neurons (“All Glut”; n = 3,842 cells). 54 Glut represents 10,000 iterations of 54 randomly selected glutamatergic neurons within the scDART-seq dataset. Coverage-matched represents 10,000 iterations of 54 randomly selected glutamatergic neurons with similar reads per cell as microglia. Error bars represent standard deviation. Significance was determined using a two-sided Wilcoxon rank-sum test. p-value for all shown comparisons < 2.2e-16. **** = p < 0.0001. e, Violin plot of data in (d). Each dot represents the average number of read-normalized sites per cell obtained from 54 random glutamatergic neurons sampled. Significance was determined using a two-sided Wilcoxon rank-sum test. p-value for all shown comparisons < 2.2e-16. **** = p < 0.0001. f, Number of m6A sites per cell, normalized to read coverage, identified in microglia (n = 54 cells) and astrocytes (“All Astrocytes”; n = 1,952 cells). 54 Astrocytes represents 10,000 iterations of 54 random astrocytes within the scDART-seq dataset. Coverage-matched represents 10,000 iterations of 54 random astrocytes with similar reads per cell as microglia. Error bars represent standard deviation. Significance was determined using a two-sided Wilcoxon rank-sum test. p-value for all shown comparisons < 2.2e-16. **** = p < 0.0001. g, Violin plot of data in (f). Each dot represents the average read-normalized sites per cell obtained from each iteration of 54 random astrocytes sampled. h, Normalized expression of APOBEC1-YTH in microglia compared to all other cell types. Significance was determined using FindMarkers in Seurat (two-sided Wilcoxon rank-sum test was used, with Bonferroni p-value adjustment). i, Representative images of n = 2 biological replicates, comparing APOBEC1-YTH expression (magenta) in a microglial cell (marked by Iba1 staining, green) and an astrocyte (marked by GFAP staining, red). Merge shows the neighboring microglia and astrocyte, with external APOBEC1-YTH signal removed for clarity. j, Quantification of APOBEC1-YTH immunofluorescence intensity in microglia (n = 43 cells from 2 distinct animals) and astrocytes (n = 49 cells from 2 distinct animals) in DART mice. Significance was determined using a two-sided Wilcoxon rank-sum test. n.s. = not statistically significant. Boxes show 25-75th percentiles and the median, with whiskers for the highest and lowest values and outliers shown. k, Western blot showing APOBEC1-YTH and METTL3 expression within cortical microglia and astrocytes isolated by FACS. n = 1 biological replicate. GAPDH was run concurrently on a separate identical blot. l, Quantification of (k) showing the GAPDH-normalized expression of APOBEC1-YTH. m, Dot plot showing expression level and percentage of cells with expression of mRNAs for m6A methyltransferase components, m6A readers, m6A erasers, and EJC proteins. n, Heatmap showing the total number of differentially methylated sites identified between each pair of cell types in the cortex. Heatmap is colored by the number of differentially methylated sites identified in the reference cell type relative to the comparison cell type. o, Heatmap showing the number of differentially methylated sites with increased average %C2U in the reference cell type relative to the comparison cell type, colored by the number of differentially methylated sites. p, Heatmap showing the average log2 fold-change in %C2U for all sites found in non-coding RNAs (ncRNAs) within each region in the reference cell type relative to the comparison cell type. q, Heatmap showing the average log2 fold-change in %C2U for all sites found in 5’ UTRs within each region in the reference cell type relative to the comparison cell type. r, Heatmap showing the average log2 fold-change in %C2U for all sites found in 3’ UTRs in the reference cell type relative to the comparison cell type. There are no pairs of cell types with significant differential methylation within coding sequences.

Source data

Extended Data Fig. 7 Differential methylation between neurons and astrocytes in the mouse cortex.

a, Correlation between changes in methylation and gene expression for all DM-RNAs with increased %C2U identified across cell types. Blue line indicates linear regression, gray band represents 95% confidence interval. Spearman correlation coefficient is shown. p = 0.0013. n = 1,436. b, As in (a), except only DM-RNAs with decreased %C2U. Blue line indicates linear regression, gray band represents 95% confidence interval. Spearman correlation coefficient is shown. p < 2.2 ×10-16. n = 1,436. c, Comparison of log2−fold change in gene expression and %C2U values for DM-RNAs identified between glutamatergic neurons and astrocytes. Blue line indicates linear regression, gray band represents 95% confidence interval. Spearman correlation coefficient is shown. p = 0.77. n = 214. d, As in (c), except only DM-RNAs with increased %C2U. Blue line indicates linear regression, gray band represents 95% confidence interval. Spearman correlation coefficient is shown. p = 0.6325. n = 18. e, As in (c), except only DM-RNAs with decreased %C2U. Blue line indicates linear regression, gray band represents 95% confidence interval. Spearman correlation coefficient is shown. p = 0.08654. n = 196. f, Top 10 most significant Gene Ontology terms enriched among DM-RNAs identified between glutamatergic neurons and astrocytes. g, UMAP visualization of glutamatergic neurons and astrocytes colored by the mean %C2U value across all m6A sites identified in the Gria1 mRNA (n = 2,506). h, Density plot showing the distribution of the average %C2U value for all sites in Gria1 in single cells in glutamatergic neurons and astrocytes. i, Violin plot showing the normalized expression of Gria1 in glutamatergic neurons and astrocytes. Red dot indicates the mean. Significance was determined using FindMarkers in Seurat (two-sided Wilcoxon rank-sum test was used, with Bonferroni p-value adjustment). n = 6,162. p < 2.2e-308. **** = p < 0.0001. j, UMAP visualization of glutamatergic neurons and astrocytes colored by the mean %C2U value across all m6A sites identified in the Gria2 mRNA (n = 4,818). k, Density plot showing the distribution of the average %C2U value for all sites in Gria2 in single cells in glutamatergic neurons and astrocytes. l, Violin plot showing the normalized expression of Gria2 in glutamatergic neurons and astrocytes. Red dot indicates the mean. Significance was determined using FindMarkers in Seurat (two-sided Wilcoxon rank-sum test was used, with Bonferroni p-value adjustment). n = 6,162. p = 0.01. * = p < 0.05. Glut = Glutamatergic neurons.

Source data

Extended Data Fig. 8 Differential methylation in neuronal subtypes.

a, Expression of marker genes within each glutamatergic neuron subtype. Dot color indicates average expression level of the indicated mRNA. Dot size indicates percentage of cells in which the mRNA is expressed. b, Average %C2U value of all sites in the Fos mRNA in IT glutamatergic neurons from different cortical layers. Red dots indicate mean values. n = 320. c, Normalized gene expression of the Fos mRNA in IT glutamatergic neurons from different cortical layers. n = 2,930. d, Average %C2U value of all sites in the Nr4a2 mRNA in IT glutamatergic neurons from different cortical layers. Red dots indicate mean values. n = 360. e, Normalized gene expression of the Nr4a2 mRNA in IT glutamatergic neurons from different cortical layers. Red dots indicate mean values. n = 2,930. f, APOBEC1-YTH RNA expression within each neuronal subtype. Red dots indicate mean values. n = 3,823. g, Correlation of changes in %C2U and gene expression of DM-RNAs between neuronal subtypes. Spearman correlation coefficient is shown, blue indicates linear regression, gray band represents 95% confidence interval. p < 2.2 ×10-16. n = 1,034. h, As in (g), but only showing DM-RNAs with increased %C2U values in each neuronal subtype. p = 2.65 ×10-5. n = 512. Spearman correlation coefficient is shown, blue indicates linear regression, gray band represents 95% confidence interval. i, As in (g), but only showing DM-RNAs with decreased %C2U values in each neuronal subtype. p = 3.9 ×10-4. n = 522. Spearman correlation coefficient is shown, blue indicates linear regression, gray band represents 95% confidence interval. L2/3 = Layer 2/3; L4/5 = Layer 4/5; L6 = Layer 6; IT = Intratelencephalic; PT = Pyrimidal tract; CT = Corticothalamic tract; Claust = Claustrum.

Extended Data Fig. 9 Clustering by m6A reveals heterogeneous methylation in glutamatergic neurons.

a, Stacked bar plot showing the percentage of cells within each m6A cluster that belong to the corresponding neuronal subtype. b, Stacked bar plot showing the percentage of cells within each neuronal subtype belonging to different m6A clusters. c, Number of differentially expressed RNAs within each m6A cluster. d, Metagene plot showing distribution of m6A sites in each m6A cluster. e, UMAP visualization of m6A clustering using shuffled methylation data. %C2U values for all m6A sites were randomly shuffled across all cells. f, Top 10 Gene Ontology terms among all RNAs containing differentially methylated sites across m6A clusters. g, Density plot showing the distribution of the number of differentially methylated sites identified within each parent RNA. Highlighted region and inset indicate RNAs with at least 10 differentially methylated sites. h, UMAP visualization of relative %C2U values for site A284, the most significantly differentially methylated site in Grin2b, across m6A clusters. n = 2,246. i, Violin plot showing relative %C2U values for each cell within each m6A cluster for site A284 in Grin2b. j, UMAP visualization of relative %C2U values for site A1397, the most significantly differentially methylated site in Gria2, across m6A clusters. n = 2,246. k, Violin plot showing relative %C2U values for each cell within each m6A cluster for site A1397 in Gria2. l, Correlation between the log2-fold change in parent gene expression and the log2-fold change in %C2U for all differentially methylated sites found across m6A clusters. Blue line shows linear regression, gray band represents 95% confidence interval. Spearman correlation coefficient is shown. p = 0.40. n = 2,794. m, As in (l), but only showing sites with increased %C2U within each methylation cluster. p = 0.96. n = 2,533. Blue line shows linear regression, gray band represents 95% confidence interval. n, As in (l), but only showing sites with decreased %C2U within each methylation cluster. p = 0.002. n = 261. Blue line shows linear regression, gray band represents 95% confidence interval.

Extended Data Fig. 10 Differential methylation patterns in glutamatergic neurons during aging.

a, UMAP visualization showing that cortical glutamatergic neurons from young and aged mice cluster together based on marker gene expression. b, Dot plot showing marker gene expression in the indicated cell types for the integrated scDART-seq datasets from young and aged mice. c, Comparison of log2-fold change of gene expression and average %C2U values of DM-RNAs identified in glutamatergic neurons from young versus aged mice. Blue line indicates linear regression gray band represents 95% confidence interval. Spearman correlation coefficient is shown. p < 2.2 ×10-16. n = 2,055. d, As in (c), but only showing DM-RNAs with increased %C2U changes in neurons from aged mice. Spearman correlation coefficient is shown. Blue line indicates linear regression gray band represents 95% confidence interval. p = 0.149. n = 852. e, As in (c), but only showing DM-RNAs with increased %C2U changes in neurons from young mice. Spearman correlation coefficient is shown. Blue line indicates linear regression gray band represents 95% confidence interval. p = 0.000725. f, Normalized expression of APOBEC1-YTH RNA in glutamatergic neurons from young and aged mice. Significance was determined using FindMarkers in Seurat (two-sided Wilcoxon rank-sum test was used, with Bonferroni p-value adjustment). p = 0.003. ** = p < 0.01. Red dots indicate mean values. Average log2 fold-change = 0.22. n = 5,401. g, Western blot densitometry quantification of APOBEC1-YTH protein level in young (n = 3 biological replicates) and aged (n = 2 biological replicates) DART mouse cortex, normalized to GAPDH. Error bars represent standard error. Mean for young cortex = 0.87. Mean for aged cortex = 1.29. h, Plot showing the –log10 of the adjusted p-value and %C2U log2-fold change from differential methylation analysis. The second most significant differentially methylated RNA is App. i, Adjusted p-values and %C2U log2 fold-changes for all differentially methylated sites in the App mRNA. j, Comparison of the adjusted p-value and %C2U log2-fold changes for differential methylation of App in glutamatergic neurons and non-neuronal cells between young and aged mice. k, In vitro DART-seq results showing the %C2U values adjacent to App A2599 in bulk cortex from young (n = 5) and aged (n = 4) mice. Significance was determined using a two-sided Wilcoxon rank-sum test with no p-value adjustment. p = 0.38. n.s. = not significant. Error bars represent standard error. Mean for young cortex = 11.4. Mean for aged cortex = 8.5.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–11.

Source data

Source Data Fig. 1

Unprocessed western blots.

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig./Table 1

Statistical source data.

Source Data Extended Data Fig./Table 1

Unprocessed western blots.

Source Data Extended Data Fig./Table 2

Statistical source data.

Source Data Extended Data Fig./Table 3

Statistical source data.

Source Data Extended Data Fig./Table 4

Statistical source data.

Source Data Extended Data Fig./Table 6

Unprocessed western blots.

Source Data Extended Data Fig./Table 7

Statistical source data.

Source Data Extended Data Fig./Table 10

Statistical source data.

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

Tegowski, M., Prater, A.K., Holley, C.L. et al. Single-cell m6A profiling in the mouse brain uncovers cell type-specific RNA methylomes and age-dependent differential methylation. Nat Neurosci 27, 2512–2520 (2024). https://doi.org/10.1038/s41593-024-01768-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-024-01768-3

This article is cited by

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