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:

MeCP2 and non-CG DNA methylation stabilize the expression of long genes that distinguish closely related neuron types

Abstract

The diversity of mammalian neurons is delineated by subtle gene expression differences that may require specialized mechanisms to be maintained. Neurons uniquely express the longest genes in the genome and use non-CG DNA methylation (mCA), together with the Rett syndrome protein methyl-CpG-binding protein 2 (MeCP2), to control gene expression. However, whether these distinctive gene structures and molecular machinery regulate neuronal diversity remains unexplored. Here, we use genomic and spatial transcriptomic analyses to show that MeCP2 maintains transcriptomic diversity across closely related neuron types. We uncover differential susceptibility of neuronal populations to MeCP2 loss according to global mCA levels and dissect methylation patterns driving shared and distinct MeCP2 gene regulation. We show that MeCP2 regulates long, mCA-enriched, ‘repeatedly tuned’ genes, that is, genes differentially expressed between many closely related neuron types, including across spatially distinct, vision-dependent gene programs in the visual cortex. Thus, MeCP2 maintains neuron type-specific gene programs to facilitate cellular diversity in the brain.

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

Access options

Buy this article

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

Fig. 1: Neuron subclass-specific analysis of global mCA levels and gene dysregulation in the Mecp2 KO mouse.
Fig. 2: Shared and distinct Mecp2 gene regulation across subclasses is associated with regional and gene-specific mCA patterns.
Fig. 3: Dysregulation of high-mCA cCREs in Mecp2 mutant PV+ interneurons.
Fig. 4: MeCP2 regulates genes that are repeatedly tuned across multiple, closely related neuronal types.
Fig. 5: Spatial transcriptomics of the RTT mouse model revealed region-specific and neuron-type-specific gene dysregulation.
Fig. 6: Loss of MeCP2 disrupts sublayer-resolved gene programs in L2/3 of the visual cortex.

Similar content being viewed by others

Data availability

All genomic data generated in this study have been uploaded to the NCBI Gene Expression Omnibus (GEO) under accession no. GSE237089. The cCREs identified as part of the BRAIN Initiative Cell Census Network38 and analyzed in this study can be found at the associated Cis-element Atlas website (http://catlas.org/mousebrain). We obtained single-cell methylomes for the pseudobulk analyses from the NCBI GEO under accession no. GSE97179 (ref. 8). We obtained the whole-cortex and hippocampus SMART-seq data from the Allen Institute (https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-smart-seq) to facilitate the identification of cell types in our MERFISH experiments. We downloaded gene expression data from ref. 75 to validate INTACT specificity for our ChIP–seq data. The Allen Common Coordinate Framework used to register the MERFISH images is located at https://scalablebrainatlas.incf.org/mouse/ABA_v3. Type-specific genes were obtained from ref. 1.

Code availability

The code related to this paper can be found at https://github.com/MatiNem/Moore-Nemera-2024.

References

  1. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Allaway, K. C. et al. Genetic and epigenetic coordination of cortical interneuron development. Nature 597, 693–697 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Cheng, S. et al. Vision-dependent specification of cell types and function in the developing cortex. Cell 185, 311–327 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Clemens, A. W. & Gabel, H. W. Emerging insights into the distinctive neuronal methylome. Trends Genet. 36, 816–832 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. de Mendoza, A. et al. The emergence of the brain non-CpG methylation system in vertebrates. Nat. Ecol. Evol. 5, 369–378 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mo, A. et al. Epigenomic signatures of neuronal diversity in the mammalian brain. Neuron 86, 1369–1384 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tillotson, R. & Bird, A. The molecular basis of MeCP2 function in the brain. J. Mol. Biol. 432, 1602–1623 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Boxer, L. D. et al. MeCP2 represses the rate of transcriptional initiation of highly methylated long genes. Mol. Cell 77, 294–309 (2020).

    Article  CAS  PubMed  Google Scholar 

  12. Chen, L. et al. MeCP2 binds to non-CG methylated DNA as neurons mature, influencing transcription and the timing of onset for Rett syndrome. Proc. Natl Acad. Sci. USA 112, 5509–5514 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Clemens, A. W. et al. MeCP2 represses enhancers through chromosome topology-associated DNA methylation. Mol. Cell 77, 279–293 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Gabel, H. W. et al. Disruption of DNA-methylation-dependent long gene repression in Rett syndrome. Nature 522, 89–93 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Guo, J. U. et al. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat. Neurosci. 17, 215–222 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Tillotson, R. et al. Neuronal non-CG methylation is an essential target for MeCP2 function. Mol. Cell 81, 1260–1275 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Christian, D. L. et al. DNMT3A haploinsufficiency results in behavioral deficits and global epigenomic dysregulation shared across neurodevelopmental disorders. Cell Rep. 33, 108416 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Tudor, M., Akbarian, S., Chen, R. Z. & Jaenisch, R. Transcriptional profiling of a mouse model for Rett syndrome reveals subtle transcriptional changes in the brain. Proc. Natl Acad. Sci. USA 99, 15536–15541 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hamagami, N. et al. NSD1 deposits histone H3 lysine 36 dimethylation to pattern non-CG DNA methylation in neurons. Mol. Cell 83, 1412–1428 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Tatton-Brown, K. et al. Mutations in the DNA methyltransferase gene DNMT3A cause an overgrowth syndrome with intellectual disability. Nat. Genet. 46, 385–388 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Amir, R. E. et al. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat. Genet. 23, 185–188 (1999).

    Article  CAS  PubMed  Google Scholar 

  22. Van Esch, H. et al. Duplication of the MECP2 region is a frequent cause of severe mental retardation and progressive neurological symptoms in males. Am. J. Hum. Genet. 77, 442–453 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Johnson, B. S. et al. Biotin tagging of MeCP2 in mice reveals contextual insights into the Rett syndrome transcriptome. Nat. Med. 23, 1203–1214 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sugino, K. et al. Cell-type-specific repression by methyl-CpG-binding protein 2 is biased toward long genes. J. Neurosci. 34, 12877–12883 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhao, Y.-T., Goffin, D., Johnson, B. S. & Zhou, Z. Loss of MeCP2 function is associated with distinct gene expression changes in the striatum. Neurobiol. Dis. 59, 257–266 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ben-Shachar, S., Chahrour, M., Thaller, C., Shaw, C. A. & Zoghbi, H. Y. Mouse models of MeCP2 disorders share gene expression changes in the cerebellum and hypothalamus. Hum. Mol. Genet. 18, 2431–2442 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Trostle, A. J. et al. A comprehensive and integrative approach to MeCP2 disease transcriptomics. Int. J. Mol. Sci. 24, 5122 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 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 

  29. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Deal, R. B. & Henikoff, S. A simple method for gene expression and chromatin profiling of individual cell types within a tissue. Dev. Cell 18, 1030–1040 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Liu, H. et al. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. Nature 624, 366–377 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ito-Ishida, A., Ure, K., Chen, H., Swann, J. W. & Zoghbi, H. Y. Loss of MeCP2 in parvalbumin-and somatostatin-expressing neurons in mice leads to distinct Rett syndrome-like phenotypes. Neuron 88, 651–658 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Liu, X. et al. Cell-type-specific gene inactivation and in situ restoration via recombinase-based flipping of targeted genomic region. J. Neurosci. 40, 7169–7186 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Renthal, W. et al. Characterization of human mosaic Rett syndrome brain tissue by single-nucleus RNA sequencing. Nat. Neurosci. 21, 1670–1679 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lagger, S. et al. MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain. PLoS Genet. 13, e1006793 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jin, Y. et al. Cell type-specific DNA methylome signatures reveal epigenetic mechanisms for neuronal diversity and neurodevelopmental disorder. Hum. Mol. Genet. 32, 218–230 (2023).

    Article  CAS  PubMed  Google Scholar 

  37. Stroud, H. et al. Early-life gene expression in neurons modulates lasting epigenetic states. Cell 171, 1151–1164 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Li, Y. E. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Liu, Y. et al. MECP2 directly interacts with RNA polymerase II to modulate transcription in human neurons. Neuron 112, 1943–1958 (2024).

    Article  CAS  PubMed  Google Scholar 

  40. He, L. et al. Conditional deletion of Mecp2 in parvalbumin-expressing GABAergic cells results in the absence of critical period plasticity. Nat. Commun. 5, 5036 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl Acad. Sci. USA 107, 21931–21936 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Heinz, S., Romanoski, C. E., Benner, C. & Glass, C. K. The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell Biol. 16, 144–154 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Liu, H. et al. DNA methylation atlas of the mouse brain at single-cell resolution. Nature 598, 120–128 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Gu, Y. et al. Balanced activity between Kv3 and Nav channels determines fast-spiking in mammalian central neurons. iScience 9, 120–137 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Miyamae, T. et al. Kcns3 deficiency disrupts parvalbumin neuron physiology in mouse prefrontal cortex: implications for the pathophysiology of schizophrenia. Neurobiol. Dis. 155, 105382 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Maksymetz, J. et al. mGlu1 potentiation enhances prelimbic somatostatin interneuron activity to rescue schizophrenia-like physiological and cognitive deficits. Cell Rep. 37, 109950 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mossink, B. et al. Cadherin-13 is a critical regulator of GABAergic modulation in human stem-cell-derived neuronal networks. Mol. Psychiatry 27, 1–18 (2022).

    Article  CAS  PubMed  Google Scholar 

  50. Paul, A. et al. Transcriptional architecture of synaptic communication delineates GABAergic neuron identity. Cell 171, 522–539 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gompers, A. L. et al. Germline Chd8 haploinsufficiency alters brain development in mouse. Nat. Neurosci. 20, 1062–1073 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Sessa, A. et al. SETD5 regulates chromatin methylation state and preserves global transcriptional fidelity during brain development and neuronal wiring. Neuron 104, 271–289 (2019).

    Article  CAS  PubMed  Google Scholar 

  53. Seabra, C. M. et al. Transcriptional consequences of MBD5 disruption in mouse brain and CRISPR-derived neurons. Mol. Autism 11, 45 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Darnell, J. C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fazel Darbandi, S. et al. Neonatal Tbr1 dosage controls cortical layer 6 connectivity. Neuron 100, 831–845 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. Wu, S. J. et al. Cortical somatostatin interneuron subtypes form cell-type-specific circuits. Neuron 111, 2675–2692 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Sugino, K. et al. Mapping the transcriptional diversity of genetically and anatomically defined cell populations in the mouse brain. eLife 8, e38619 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Kim, E. J. et al. Extraction of distinct neuronal cell types from within a genetically continuous population. Neuron 107, 274–282 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 598, 137–143 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Zhang, Z. et al. Epigenomic diversity of cortical projection neurons in the mouse brain. Nature 598, 167–173 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).

    PubMed  PubMed Central  Google Scholar 

  63. Chao, H.-T. et al. Dysfunction in GABA signalling mediates autism-like stereotypies and Rett syndrome phenotypes. Nature 468, 263–269 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Beard, D. C. et al. Distinct disease mutations in DNMT3A result in a spectrum of behavioral, epigenetic, and transcriptional deficits. Cell Rep. 42, 113411 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Nord, A. S. & West, A. E. Neurobiological functions of transcriptional enhancers. Nat. Neurosci. 23, 5–14 (2020).

    Article  CAS  PubMed  Google Scholar 

  67. Lee, W., Kim, J., Yun, J.-M., Ohn, T. & Gong, Q. MeCP2 regulates gene expression through recognition of H3K27me3. Nat. Commun. 11, 3140 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Li, J. et al. Dnmt3a knockout in excitatory neurons impairs postnatal synapse maturation and increases the repressive histone modification H3K27me3. eLife 11, e66909 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Osterhout, J. A., Stafford, B. K., Nguyen, P. L., Yoshihara, Y. & Huberman, A. D. Contactin-4 mediates axon-target specificity and functional development of the accessory optic system. Neuron 86, 985–999 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Oguro-Ando, A. et al. Cntn4, a risk gene for neuropsychiatric disorders, modulates hippocampal synaptic plasticity and behavior. Transl. Psychiatry 11, 106 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Richardson, F. C. & Kaczmarek, L. K. Modification of delayed rectifier potassium currents by the Kv9.1 potassium channel subunit. Hear. Res. 147, 21–30 (2000).

    Article  CAS  PubMed  Google Scholar 

  72. Costigan, M. et al. Multiple chronic pain states are associated with a common amino acid-changing allele in KCNS1. Brain 133, 2519–2527 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Dehorter, N. et al. Tuning of fast-spiking interneuron properties by an activity-dependent transcriptional switch. Science 349, 1216–1220 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Di Bella, D. J. et al. Molecular logic of cellular diversification in the mouse cerebral cortex. Nature 595, 554–559 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Huntley, M. A. et al. Genome-wide analysis of differential gene expression and splicing in excitatory neurons and interneuron subtypes. J. Neurosci. 40, 958–973 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Shamash, P., Carandini, M., Harris, K. & Steinmetz, N. A tool for analyzing electrode tracks from slice histology. Preprint at bioRxiv https://doi.org/10.1101/447995 (2018).

  77. Cohen, S. et al. Genome-wide activity-dependent MeCP2 phosphorylation regulates nervous system development and function. Neuron 72, 72–85 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  80. 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 

  81. Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 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 

  83. Guo, W. et al. BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data. BMC Genomics 14, 774 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Rao, S. S. P. et al. A 3D Map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Hinrichs, A. S. et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–D598 (2006).

    Article  CAS  PubMed  Google Scholar 

  88. Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl Acad. Sci. USA 113, 11046–11051 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).

    Article  Google Scholar 

  92. Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020).

    Article  CAS  PubMed  Google Scholar 

  93. McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Morimoto, M. M., Uchishiba, E. & Saleem, A. B. Organization of feedback projections to mouse primary visual cortex. iScience 24, 102450 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

Download references

Acknowledgements

We thank members of the Gabel laboratory and E. Pollina for providing support and feedback on the paper. Next-generation sequencing was carried out through the Genome Technology Access Center at the McDonnell Genome Institute and The Edison Family Center for Genome Sciences and Systems Biology at Washington University. We thank M. Watson and J. Snider for MERFISH imaging support. This work was supported by the McDonnell Center for Cellular & Molecular Neurobiology, the NIH and National Institute of Child Health and Human Development (NICHD) grant no. F30HD102147-02 to J.R.M., a National Institute of General Medical Sciences grant no. R25GM103757 to M.T.N., an NICHD grant no. F30HD110156-01 to N.H., National Institute of Neurological Disorders and Stroke (NINDS) grant nos. R21NS127191 and R21NS137254 to J.R.E. and H.W.G., NINDS grant no. R01NS04102 to H.W.G., and National Institute of Mental Health grant no. R01MH117405 and Simons Foundation grant no. AR-PIW-00002314-01 to H.W.G., and an NIH grant no. T32HG000045 to T.E.L. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Authors and Affiliations

Authors

Contributions

J.R.M. performed all INTACT isolation, RNA-seq and ChIP–seq experiments. J.R.M. collaborated with A.W.C. to perform ChIP–seq, with R.D.D. and N.H. to perform the MERFISH experiments, and with Y.R. to perform the WGBS experiments. M.T.N. analyzed the ChIP–seq, RNA-seq and methylation data. M.T.N. developed a pipeline for analyzing the MERFISH data generated in this study. D.C.B. performed the bisulfite sequencing experiments and analysis. R.D.D., V.R.M. and A.U. also analyzed the MERFISH data. T.E.L. and J.R.E. processed the WGBS data. H.W.G., J.R.M. and M.T.N. designed the experiments and analysis, and wrote the paper.

Corresponding author

Correspondence to Harrison W. Gabel.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks the anonymous reviewers 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 Gene expression and methylation in INTACT-isolated PV, SST, L4, and L5 neurons.

a, Representative images of Rbp4-Cre;SUN1:GFP labeling of L5 excitatory neurons and Nr5a1-Cre;SUN1:GFP labeling of L4 excitatory neurons. b, Top: marker gene expression in RNA sequencing data from each subclass profiled. Bottom: number of differentially expressed genes (DEGs) between WT cells of each subclass. c, Log2 fold-difference in gene expression between WT subclasses. The x-axis represents mean normalized counts of genes from DESeq2. d,e Log2 gene body mCA/CA calculated from bisulfite-sequencing of INTACT-isolated nuclei vs log2 gene body mCA/CA derived from pseudobulk snmC-seq8 data (d) or log2 gene expression TPMs from INTACT RNA-seq (e) for neuronal subclasses. ρ = Spearman’s correlation coefficient. f, Pairwise comparisons of gene body mCA/CA across subclasses. In b and f, genes enriched for expression >5 fold in one subclass over another are colored according to the subclass where they are highly expressed. For panels c-f, genes with a minimum length of 5 kb located in chromosomes 1-19 and X were analyzed to facilitate accurate mean mCA/CA calculations. Data generated from cerebral cortex tissue from 8–10-week-old MeCP2 WT and KO mice. n = 4 bioreplicates per genotype for RNA-seq, n = 2-3 bioreplicates per genotype for whole-genome bisulfite sequencing. WT and Mecp2 KO replicates were averaged for INTACT methylation calculations except d, where only WT is used to compare to WT snmC-seq data.

Extended Data Fig. 2 Analysis of global mCA levels and gene dysregulation in Mecp2 KO mice.

a, Gene expression of MeCP2-repressed (MR) and MeCP2-activated (MA) genes in WT and Mecp2 KO L4, L5, SST, and PV subclasses. Central dots represent medians. b, Number of significantly dysregulated genes identified in each subclass plotted vs average mCA level in that subclass, using previously published single-cell methylation data of the 8-week-old mouse frontal cortex.8 c, Mean fold-change of long (greater than 100 kb), highly methylated (top decile of mCA) genes (left) and core MeCP2-repressed genes (right) vs. average mCA level for all genes in each subclass, using the same methylation data in b. Data are presented as mean values +/- SEM. d, Log2 fold-change (Mecp2 KO/WT) of expression for all genes in subclasses. The x-axis is normalized counts of genes from DESeq2. e, Smooth line plots of mCA levels vs log2 fold-change in gene expression (Mecp2 KO/WT) in each neuronal subclass, separated by genes longer than (red) or shorter than (gray) 100 kb. f, Gene body mCA/CA of core MeCP2-repressed genes in each subclass compared to expression-resampled control genes. ****p < 0.0001 two-sided Wilcoxon rank-sum test. g, Left: depiction of the three brain regions analyzed in analysis of global DNA methylation and gene dysregulation in MeCP2 mutants. Right: global mCA/CA of WT and Mecp2 KO cells in each brain region. ns=not significant (p > 0.05, Wilcoxon rank-sum test). h, Mean fold-change in mRNA expression for long (greater than 100 kb), high mCA (top decile of whole gene mCA) genes in the Mecp2 KO vs. WT in brain tissues. The x-axis represents global mCA/CA for each tissue averaged across bioreplicates. Central dot is the mean fold-change of gene expression between Mecp2 KO and WT, and the y-axis error bars represent the standard error of that mean. The x-axis error bars represent the standard error of the mean of global mCA levels of a tissue type averaged over all replicates. i, Left: mCG/CG of genes in each subclass. Grand mean genic mCG/CG across replicates and standard error are shown. Middle: number of significantly dysregulated genes identified in each subclass plotted vs average mCG/CG in that subclass. Right: expression fold-change of core MeCP2-repressed genes vs. average mCG/CG for all genes in each subclass. Mean and standard error are shown. j, Global mCA (left) and mCG (right) levels of WT and Mecp2 KO neuronal subclasses. ns=not significant (p > 0.05 Wilcoxon rank-sum test). k, WT vs Mecp2 KO gene-body mCA/CA in neuronal subclasses. Genes with a minimum length of 5 kb located in chromosomes 1-19 and X were used. ρ = Spearman’s correlation coefficient. For a, d-f, and i-k, data are from cerebral cortex tissue from 8–10-week-old MeCP2 WT and KO mice. n = 4 bioreplicates per genotype for RNA-seq, n = 2-3 bioreplicates per genotype for whole-genome bisulfite sequencing. WT and Mecp2 KO replicates were averaged for their methylation values except where stated otherwise. For e and f, n = 2 bioreplicates for WT and Mecp2 KO cerebellum, striatum, and hypothalamus whole-genome bisulfite sequencing, n = 5 bioreplicates per genotype of microarray data from brain tissue25,26. Boxplots represent data as described in Fig. 2. WT and Mecp2 KO replicates were averaged for INTACT methylation calculations in b, c, e, and f.

Extended Data Fig. 3 Methylation patterns associated with shared and distinct MeCP2-regulated genes in PV, SST, L4, and L5 neurons.

a, Left: log2 fold-change in mRNA (Mecp2 KO/WT) of MeCP2-regulated genes identified in L4, L5, PV and SST subclasses, with every subclass’s MeCP2-regulated gene list plotted in each subclass. ****p < 0.0001 two-sided Wilcoxon rank-sum test. Top right: overlap of MeCP2-activated genes from each subclass and core MeCP2-activated genes from multiple datasets. -Log10 p-value is calculated from two-sided Fisher’s exact test. Bottom right: overlap of L5 and L4 MeCP2-repressed genes identified in this publication with MeCP2-repressed genes identified in excitatory neurons by Renthal et al.34. OR = odds ratio. b, mCA/CA enrichment in regions, gene bodies, and linked cCREs of subclass MR genes or subclass MA genes over those of expression-resampled unchanged genes. c, Regional mCA/CA vs cCRE mCA/CA for all cCREs in the genome in each subclass. Here, regions are defined as topologically associated domains (TADs) identified in the cortex85. ρ = Spearman’s correlation coefficient. Consistent with mCA enrichment at cCREs resulting from regional methylation set-points, as described previously13,19, mCA levels at cCREs are predicted by regional levels in each subclass. d, Log 10 gene length of genes MeCP2-repressed neuronal subclasses. The gray box next to each subclass represents the expression-matched genes resampled from that subclass’s list of unchanged genes. ****p < 0.0001 two-sided Wilcoxon rank-sum test. e, Left: Aggregate mCG/CG for MeCP2-regulated genes in neuronal subclasses. Aggregate plots generated as in Fig. 2d. f, mCG/CG enrichment in elements (regions, gene bodies, TSS, and linked cCREs) of subclass MR genes or subclass MA genes over those of expression-resampled unchanged genes. g, mCG/CG enrichment in elements of other-subclass MR genes over those of subclass MR genes. h, Enrichment for element mCA/CA for core MR genes over those of all other genes. i, Enrichment for element mCA/CA associated with non-subclass core MR genes over those associated with subclass core MR genes. j, Aggregate mCA/CA levels at gene bodies (left) and linked cCREs (right) of L5 core MR genes, non-L5 core MR genes, and unchanged genes. Data are from cerebral cortex tissue from 8–10-week-old MeCP2 WT and KO mice. n = 4 bioreplicates per genotype for RNA-seq, n = 2-3 bioreplicates per genotype for whole-genome bisulfite sequencing. Boxplots represent data as described in Fig. 2. For heatmaps b and f-i, the numbers in the tiles represent the ratio of median methylation level of elements associated with one group of genes to the median methylation level of elements associated with a comparison group of genes described in each panel, and colors represent the log 10 p-value from two-sided Wilcoxon rank-sum test. WT and Mecp2 KO replicates were averaged for their methylation calculations.

Extended Data Fig. 4 Distribution of epigenomic signals at MeCP2-regulated cCREs in PV interneurons.

a, Log2 input-normalized MeCP2 ChIP signal at MeCP2-repressed (MR) and MeCP2-activated (MA) cCREs in PV cells. ****p < 0.0001 two-sided Wilcoxon rank-sum test. b, Boxplot of PV mCG/kb in MeCP2-regulated cCREs. ****p < 0.0001 two-sided Wilcoxon rank-sum test. c, Genomic distributions of MeCP2-regulated cCREs. d, Log2 H3K27ac fold-change for extragenic cCREs linked to PV MR genes or unchanged genes in PV Mecp2 KO and Mecp2 OE. ****p < 0.0001 two-sided Wilcoxon rank-sum test. e, Log2 H3K27ac fold-change of extragenic cCREs linked to PV MR genes, other-subclass MR genes, or unchanged genes in PV Mecp2 KO and Mecp2 OE. ns=not significant, *p < 0.05, ****p < 0.0001 two-sided Wilcoxon rank-sum test. f, Log2 H3K27ac ChIP fold-change (MeCP2 mutant/wild-type) in cCREs linked to PV core MR, non-PV core MR, or unchanged genes. **p < 0.01, ****p < 0.0001 two-sided Wilcoxon rank-sum test. g, Log2 input-normalized MeCP2 ChIP-seq signal in cCREs linked to PV MR genes, other-subclass MR genes, or unchanged genes. ns=not significant, **p < 0.01, ****p < 0.0001 two-sided Wilcoxon rank-sum test. h, Left: expression of genes linked to PV or non-PV cCREs. Right: log2 input-normalized H3K27ac ChIP-seq signal of PV or non-PV cCREs linked to genes. ****p < 0.0001 two-sided Wilcoxon rank-sum test. i, Log2 input-normalized MeCP2 ChIP-seq signal in PV cCREs and non-PV cCREs linked to PV MR genes or unchanged genes. ****p < 0.0001 two-sided Wilcoxon rank-sum test. j, PV mCA/CA (left) and log2 H3K27ac fold-change in PV Mecp2 KO and Mecp2 OE (right) of PV and non-PV extragenic cCREs linked to unchanged genes or PV MR genes. *p < 0.05, ***p < 0.001, ****p < 0.0001 two-sided Wilcoxon rank-sum test. k, PV mCG/CG of PV and non-PV cCREs linked to unchanged genes or PV MR genes. ****p < 0.0001 two-sided Wilcoxon rank-sum test. l, HOMER output showing ROR family motif enrichment in PV and non-PV cCREs. m, Log2 fold-change in mRNA expression (PV Mecp2 KO/WT) of PV MeCP2-repressed genes, PV MeCP2-activated genes, transcription factors whose motifs are enriched in MeCP2-repressed PV or non-PV cCREs, and all other genes. ns=not significant (p > 0.05), ****p < 0.0001 two-sided Wilcoxon rank-sum test. n, WT and Mecp2 KO PV mCA/CA for MeCP2-repressed, MeCP2-activated, and all other cCREs. Right: WT and Mecp2 KO PV mCA/CA of PV and non-PV cCREs linked to unchanged genes or PV MeCP2-repressed genes. ns=not significant (p > 0.05) two-sided Wilcoxon rank-sum test. Data are from cerebral cortex of 8–10-week-old male mice. n = 3 bioreplicates for PV WT, Mecp2 KO, and Mecp2 OE H3K27ac ChIP-seq. n = 2 bioreplicates for PV WT and Mecp2 KO for whole-genome bisulfite sequencing. Boxplots represent data as described in Fig. 2. WT and Mecp2 KO replicates were averaged for their methylation calculations for j and k.

Extended Data Fig. 5 Functional annotation of subclass-defined MeCP2-repressed genes and their representation in other datasets.

a, Gene ontology of MeCP2-repressed (MR) genes in L4, L5, PV, and SST neurons. Top 10 terms for Molecular Function shown. b, Overlap odds ratio and significance of differentially expressed genes across L5, PV, and SST cellular hierarchy with core MeCP2-repressed genes, MeCP2-repressed genes previously identified in the cortex13, and dysregulated genes detected in other neurodevelopmental disorder mouse models. P-value from two-sided Fisher’s exact test. c, Aggregate mCG/CG8 for genes that distinguish types within L5, PV, and SST subclasses, as well as expression-resampled controls. Aggregate plots generated as in Fig. 2d. Analysis was performed on differentially expressed genes from INTACT RNA-seq analysis described in Fig. 1. n = 4 bioreplicates per genotype per subclass, as well as the indicated published gene lists13,51,52,53,54,55.

Extended Data Fig. 6 Genes that are repeatedly tuned between neuron types have characteristics that predispose them to regulation by the MeCP2 pathway.

a, Number of intragenic cCREs (left) and gene length (right) for genes that are repeatedly tuned between closely related neuron types. Genes are plotted by the number of times they are detected as differential between two closely related neuron types. n = 23822 cells used in single-cell RNA-seq1 to identify these genes. ****p < 0.0001 two-sided Wilcoxon rank-sum test. b, Aggregate profiles of mCA/CA in the cerebral cortex for genes found to be differentially expressed between closely related neuron types more than three times or non-type-specific genes. Aggregate plots generated as in Fig. 2d. c, Odds ratio of overlap between repeatedly tuned genes and core MeCP2-repressed genes or MeCP2-repressed genes identified in L5, PV, or SST neurons. Analysis was performed on type-specific gene lists described in Tasic et al.1 in combination with DNA methylation from Luo et al.8. Differential gene lists from Fig. 1 were used in c. Boxplots represent data as described in Fig. 2.

Extended Data Fig. 7 MERFISH analysis of cell types and Mecp2 expression in Mecp2KO/+ brain shows expected gene expression and cell distributions across transcriptotypes.

a, Left: transcripts detected (colored dots) and DAPI images of Mecp2KO/+ MERFISH data in one experiment. Scale bar is 250 μm for low resolution view, and 25 μm for close-ups. Middle: correlation of mean log2 CPM (across all detected cells) of each gene for experiments 1 and 2. Right: correlation between each pair of MERFISH experiments in log2 mean CPM of each gene. b, UMAP of MERFISH cells, colored by experiment identity and biological replicate. c, Log-transformed CPMs of expression for each gene probed by MERFISH in neuronal subclasses compared to its expression detected by INTACT RNA-seq. d, Type and subclass numbers in MERFISH experiments. e. Z-scores of average expression for each gene in the MERFISH panel in each cell type. Inset: close-ups of Pvalb (PV marker gene), Fezf2 (L5 PT CTX marker gene), and Rorb (L4/5 IT CTX marker gene). f, Representative distributions of transcript counts per cell for Mecp2 and a representative negative control (‘Blank counts’) detected in PV cells in MERFISH analysis of wild-type or Mecp2KO/+ coronal sections. Gray = thresholds used for calling ‘WT’ and ‘KO’ transcriptotypes. g, UMAP of MERFISH cells colored by transcriptotype. h, MERFISH log2 fold-change in expression of genes identified as MeCP2-repressed in PV, SST, L4, and L5 INTACT RNA-seq analyses. ****p < 0.0001 two-sided Wilcoxon rank-sum test. i, WT, Mecp2 KO, or unassigned cell proportions of a subclass or all cells, for each biological replicate. ns=not significant, *p < 0.05, **p < 0.01 two-sided paired t-test. j, Locations of WT and KO cells of subclasses across cortical layers in the visual cortex identified using MERFISH. k, Comparing the WT fold-differences in expression of type-specific DEGs between closely related types of the same subclass to fold-differences between Mecp2 KO and WT cells of MeCP2-repressed genes or non-DEGs (as in Fig. 5h). Comparison of the magnitudes of differences shows that loss of MeCP2 changes leads to only a partial loss of differences in type-specific DEGs between closely related types. High/low: log2 fold-difference in expression of previously defined type-specific DEGs2 between types within a subclass. KO/WT: log2 fold-difference between Mecp2 KO and WT of MeCP2-repressed genes lowly expressed (‘Low MR’) or highly expressed (‘High MR’) in types relative to related types, or non-DEGs. MERFISH data are from n = 3 bioreplicates across 4 imaged brain sections from 8–10-week-old female Mecp2KO/+ mice. Boxplots represent data as described in Fig. 2. ρ = Spearman’s correlation coefficient. B1-3=biological replicate 1-3.

Extended Data Fig. 8 Loss of mutually exclusive type-specific gene expression in Mecp2 KO PV interneurons.

a, MeCP2 mutant heterozygous females contain wildtype and mutant cells. b, Representative image of visual cortex L4 area focused on for analysis. White arrow pointing to L4 region. c, Representative images from all 12 target probes and DAPI stain for each of the three imaging rounds. Merged images of each round shown. d, RNAScope analysis of expression of mutually exclusive marker genes for visual cortex PV interneurons in Mecp2 KO and WT PV neurons. Identification of PV neurons using Pvalb and call of Mecp2 KO and WT cells using Mecp2. e, Plots of rate of co-expression of putatively mutually exclusive PV marker genes in MeCP2 null and WT PV interneurons (n = 3, 50-100 cells per experiment, two-sided unpaired t test, **p < 0.01, ***p < 0.005). Center lines represent the mean and error bars represent standard error of the mean. n = 3 bioreplicates for RNAScope from 8–10-week-old female Mecp2KO/+ mice.

Extended Data Fig. 9 Visual cortex layer 2/3 sublayer-specific genes are targets of MeCP2 regulation.

a, Gene length, number of intragenic cCREs, and mCA/CA and mCG/CG of V1 L2/3 excitatory neuron sublayer type-specific genes defined by Cheng et al.4. ****p < 0.0001 two-sided Wilcoxon rank-sum test. b, Overlap of excitatory neuron sublayer type-specific genes4 with core MeCP2-regulated gene lists. Numbers in the tiles represent enrichment (log2 odds ratio) of core MeCP2-regulated genes in each L2/3 gene list. P-values from two-sided Fisher’s exact test. c, MERFISH-quantified expression for excitatory neuron sublayer type-specific genes4 in IT excitatory neurons in L2/3 of Mecp2KO/+ V1. d, Overlap of excitatory neuron sublayer type-specific genes4 with genes detected as significantly dysregulated in pseudobulkDGE analysis of MERFISH data in excitatory neurons in sublayer depth quintiles of L2/3 of V1. Numbers in the tiles represent enrichment (log2 odds ratio) of L2/3 genes in dysregulated gene lists of the excitatory neurons in each sublayer depth quintile of L2/3 of V1. P-values from two-sided Fisher’s exact test. MERFISH data are from n = 3 bioreplicates across 4 imaged brain sections from 8–10-week-old female Mecp2KO/+ mice. Methylation data is from the 8-week mouse cortex37, n = 2 bioreplicates. Boxplots represent data as described in Fig. 2.

Supplementary information

Reporting Summary

Supplementary Tables 1–6

Supplementary Table 1: Information about INTACT-isolated cells including Cre line, genotype, percentage of nuclei that are GFP+ and average number of isolated nuclei. Supplementary Table 2: MeCP2-regulated gene lists identified in L4, L5, PV and SST INTACT-isolated neurons. Columns in L4, L5, PV, SST tabs: ashr_log2FoldChange: DESeq2 estimate of log(fold change) of gene expression between WT and Mecp2 KO, with adaptive shrinkage (‘ashr’). ashr_lfcSE: standard error estimate for the log2(fold change) estimate from DESeq2. ashr_pvalue: P value calculated using DESeq2 and a two-tailed Wald test. ashr_padj: P value adjusted using Benjamini–Hochberg multiple testing correction. gene_class: category to which each gene belongs. For a subclass, MR (MeCP2-repressed) genes were those that had a DESeq2 adjusted P ≤ 0.1 and log2 fold change > 0, while MA (MeCP2-activated) genes had a DESeq2 adjusted P ≤ 0.1 and log2(fold change) < 0; subclass unchanged genes were genes with a nominal P > 0.5. The tab subclass_core_label describes whether a gene is identified as regulated by MeCP2 in INTACT-isolated subclasses in this study or studies across non-cortical brain regions (core MeCP2-repressed or MeCP2-activated genes). Supplementary Table 3: Motif enrichment in PV and non-PV cCREs analyzed using HOMER. The column ‘P value’ is the motif enrichment P value calculated by HOMER using a binomial distribution. ‘q value (Benjamini)’ is the Benjamini–Hochberg correction on the P value. Supplementary Table 4: MERFISH gene panel. The columns describe the categories to which each gene belongs. Supplementary Table 5: Number and proportion of cells called as WT, Mecp2 KO and unassigned in mice assayed using MERFISH. Supplementary Table 6: Statistics for the figures, including P values.

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

Moore, J.R., Nemera, M.T., D’Souza, R.D. et al. MeCP2 and non-CG DNA methylation stabilize the expression of long genes that distinguish closely related neuron types. Nat Neurosci 28, 1185–1198 (2025). https://doi.org/10.1038/s41593-025-01947-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-025-01947-w

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