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Multi-region m6A epitranscriptome profiling of the human brain reveals spatial and temporal variation and enrichment of disease-associated loci

Nature Neuroscience volume 29, pages 195–205 (2026)Cite this article

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Abstract

N6-methyladenosine (m6A) is a major RNA modification in the brain, regulating neural processes and contributing to disease mechanisms. Despite its importance, regional, age-specific and sex-specific m6A patterns in the human brain are still poorly described. Here, we profiled m6A mRNA modifications in five human brain regions (Brodmann areas 9 and 24, and the caudate, hippocampus and thalamus) across 25 individuals of different ages, ranging from 0 to 71 years old. We uncovered widespread regional differences for m6A patterns in the brain, notably in disease-risk genes, while age-related changes were most prominent in the prefrontal cortex. Integrating m6A data with whole-genome sequencing revealed that m6A modifications are associated with disease-related genetic loci. Our work identifies the spatial and temporal variation in m6A modifications and suggests how they could contribute to neurological disorders.

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Fig. 1: Overview of m6A and gene expression profiles in five human brain regions.
Fig. 2: Brain-region-specific m6A changes.
Fig. 3: Age-specific m6A changes across brain regions.
Fig. 4: m6A is associated with brain disorders and diseases.
Fig. 5: Four m6A-QTLs associated with brain disorders.

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Data availability

The data used in this study are part of the PsychENCODE Consortium, a large-scale collaborative effort to understand the genetic and epigenetic mechanisms underlying psychiatric disorders by integrating multi-omics data.

The human brain tissue samples used in this study were obtained from the University of Maryland Brain and Tissue Bank, which contributes to the PsychENCODE Consortium. Because of the controlled nature of human genomic data, access to individual-level data requires approval. Users can request access through the National Institute of Mental Health Data Archive (NDA) under the data use limitations specific to each dataset. The raw datasets, including RNA-seq, m6A-seq and WGS, are available through NDA (https://nda.nih.gov/experimentView.html?experimentId=2878&collectionId=5032).

All data associated with this study are in the paper or its supplementary materials.

Three databases were used in this study: (1) m6A-Atlas (v.2.0) (http://rnamd.org/m6a/); (2) DAVID (v.6.8) (https://davidbioinformatics.nih.gov/summary.jsp); and (3) GWAS Catalog database (www.ebi.ac.uk/gwas/home).

Code availability

The scripts used for the QTL data analysis are available on GitHub at https://github.com/CTLife/xQTLs and Zenodo (https://doi.org/10.5281/zenodo.16895471)73. All scripts can be provided upon reasonable request.

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Acknowledgements

This work is part of the PsychENCODE Consortium (www.psychencode.org/home). This work was funded by NIH Grant U01MH116441 and in part by the Emory Alzheimer's Disease Research Center (grant no. P30 AG066511).

Author information

Author notes

  1. Yong Peng

    Present address: Innovative Institute of Chinese Medicine and Pharmacy and Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, China

  2. These authors contributed equally: Andrew M. Shafik, Yong Peng.

  3. These authors jointly supervised this work: Andrew M. Shafik, Mengjie Chen, Peng Jin.

Authors and Affiliations

  1. Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA

    Andrew M. Shafik, Junghwa Lim & Peng Jin

  2. Department of Human Genetics, The University of Chicago, Chicago, IL, USA

    Yong Peng, Chen Chang & Mengjie Chen

  3. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA

    Yong Peng, Chen Chang & Mengjie Chen

  4. Department of Chemistry, The University of Chicago, Chicago, IL, USA

    Zijie Zhang, Pingluan Wang & Chuan He

  5. Howard Hughes Medical Institute, Chicago, IL, USA

    Zijie Zhang, Pingluan Wang & Chuan He

  6. Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, USA

    Zijie Zhang & Chuan He

  7. Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA

    Zijie Zhang & Chuan He

  8. Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hongjun Song

  9. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hongjun Song

  10. Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hongjun Song

  11. The Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hongjun Song

  12. Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hongjun Song

Authors
  1. Andrew M. Shafik
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  2. Yong Peng
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  3. Zijie Zhang
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  4. Chen Chang
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  6. Junghwa Lim
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  8. Chuan He
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  9. Mengjie Chen
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  10. Peng Jin
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Contributions

A.M.S. performed the data analyses, supervised the overall study design and wrote the manuscript. Y.P. performed the m6A-seq data and QTL analyses. Z.Z. carried out the m6A IP and library preparations. C.C. performed the WGS data analysis and SNP calling. P.W. created the m6A-SAC-seq libraries. J.L. performed the RNA and DNA extractions. H.S. and C.H. supervised the study. M.C. and P.J. supervised the overall study and wrote the manuscript.

Corresponding authors

Correspondence to Andrew M. Shafik, Mengjie Chen or Peng Jin.

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The authors declare no competing interests.

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Nature Neuroscience thanks Schahram Akbarian, Xushen Xiong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Quality control metrics for m6A-seq and technical variation controls.

(A) GC bias correction plot showing the offset applied for GC bias across different GC fractions. Each line represents an individual sample. The offset values are centered around zero, with higher corrections applied at extreme GC fractions, indicating the extent of GC-dependent variability across samples. (B) IP efficiency correction. Log-log plot comparing individual log2 (odds ratio) values to the mean log2 (odds ratio) across samples. Each line represents a sample, showing a strong linear relationship, indicating consistency in odds ratio estimates across different conditions. The alignment along the diagonal suggests minimal systematic bias in the data. (C) Boxplots showing the percent relative error across different resampling percentages. Each box represents the distribution of relative errors from multiple resampling iterations. Boxes extend from the 25th percentile (Q1) to the 75th percentile (Q3) of the relative errors, with the median indicated by a horizontal line inside each box. Whiskers indicate the minimum and maximum values within 1.5x the interquartile range (IQR) from the box. Higher resampling percentages yield lower relative errors, reflecting greater estimation stability with larger sample sizes. The total sample size is 100 individuals; for example, a resampling percentage of 50% corresponds to 50 individuals. (D) Violin plots showing the variance distribution explained by variables in m6A-seq and RNA-seq. Each violin plot illustrates the data’s density distribution, median, and interquartile range. Box plots within violin plots indicate the median (central line), interquartile range (box edges), and whiskers representing the minimum and maximum values within 1.5x IQR from the quartiles. Individual and regional variance contribute to both datasets. Variance fractions were estimated using linear models with fixed effects as implemented in the variancePartition R package. 93 individuals were included in this analysis. (E) Boxplot showing the number of m6A sites per individual, normalized by the total read count for that individual, grouped by brain region. Sample sizes are 18 individuals for BA24, C, and H; 19 individuals for BA9; and 20 individuals for T. The red diamonds represent the mean values for each region. The box spans the 25th percentile (Q1) to the 75th percentile (Q3) of the data (interquartile range, IQR), with the median indicated by a horizontal line inside the box. Whiskers extend to the minimum and maximum values within 1.5x the IQR from the box. Points beyond this range are shown as individual outliers (for example, the dot above BA9). Statistical comparison across brain regions was performed using a one-way ANOVA, resulting in a p-value of 0.651, indicating no statistically significant differences between regions. (F) Boxplots showing distribution of m6A values across all 93 samples. The box spans the 25th percentile (Q1) to the 75th percentile (Q3) of the data (interquartile range, IQR), with the median indicated by a horizontal line inside the box. Whiskers extend to the minimum and maximum values within 1.5x the IQR from the box.

Extended Data Fig. 2 Characteristics of m6A-seq.

(A) Number of m6A peaks across all samples. (B) Genomic distribution of high confidence m6A peaks. (C) Histogram showing m6A peak width. The majority of m6A peaks are between 199 and 258 nucleotides long. (D) Metagene plot showing distribution of m6A peaks across the gene. The red trace shows the profile of calibrated m6A peaks according to m6ACali. The teal trace is the profile of the m6A peaks considered false positives and removed. (E) The most enriched m6A motif in our dataset. The enrichment of the motif in the target set relative to a background set was assessed using a hypergeometric test, and p-values were adjusted for multiple testing using the Benjamini–Hochberg procedure as per the Homer package. (F) Histogram showing frequency of different DRACH motifs with a ± 50 bp m6A-peak-centered window. (G) Pearson correlation coefficient between normalized m6A levels and normalized gene expression for all 93 samples. The box plot within the violin extends from the 25th percentile (Q1) to the 75th percentile (Q3) of the relative errors, with the median indicated by a horizontal line inside each box. Whiskers indicate the minimum and maximum values within 1.5x the interquartile range (IQR) from the box. (H) Examples of the correlation between m6A and gene expression for specific individuals. The R2 value and two-sided p-value from Pearson correlation are shown.

Extended Data Fig. 3 Brain-region–specific and age-specific comparisons.

(A) Comparative analysis of brain-region–specific m6A and gene expression. Plots show –log10(p-value) versus log2(fold change) for differentially methylated sites and genes. Differential analysis was performed using DESeq2 with a paired design ( ~ individual + brain_region), and p-values were derived from Wald tests. For m6A methylation, vertical dashed lines indicate a log2(fold change) threshold of ±0.5 and the horizontal dashed line indicates a p-value threshold of 0.001. For gene expression, vertical dashed lines indicate a log2(fold change) threshold of ±1, and the horizontal dashed line indicates a p-value threshold of 0.001. (B) Comparative analysis of age-specific m6A and gene expression. Plots show –log10(p-value) versus log2(fold change) for differentially methylated peaks (DMPs) and differentially expressed genes (DEGs). Age-specific DEGs were identified using DESeq2 (version 1.39.5) with TMM-normalized counts as input, and p-values were derived from Wald tests. Age-specific DMPs were identified using the generalized linear model in RADAR R pacakge. For m6A methylation, vertical dashed lines indicate a log2(fold change) threshold of ±0.5 and the horizontal dashed line indicates a p-value threshold of 0.001. For gene expression, vertical dashed lines indicate a log2(fold change) threshold of ±1, and the horizontal dashed line indicates a p-value threshold of 0.001.

Extended Data Fig. 4 Correlation between age-specific and brain-region–specific m6A methylation and gene expression.

(A) Comparison of BA9 age-specific m6A methylation log2 FC values with other brain regions (B) Comparison of BA9 brain-region–specific m6A methylation log2 FC values with other brain regions (C) Heatmap showing the gene expression values of genes marked with brain-region–specific m6A methylation (rows), with red indicating high expression and blue indicating low expression. Majority of brain-region–specific m6A marked genes have consistent gene expression across all brain regions (indicated by blue bar). Some brain-region–specific have brain-region–specific expression (indicated by orange bar). (D) Scatterplots showing the correlation between normalized m6A levels and normalized gene expression values for each brain region.

Extended Data Fig. 5 Validation of age-specific-m6A candidate peaks in mouse.

Top panel: m6A peaks upregulated in young (2-week-old mice) vs old (1-year-old mice) Bottom panel: m6A peaks downregulated in young (2-week-old mice) vs old (1-year-old mice). Ctx = Cortex; IP = m6A immunoprecipitation track; IN = input track (RNA-seq).

Extended Data Fig. 6 Age-related changes in gene expression of m6A writers, readers, erasers and exon junction complex proteins.

The shaded bands represent the 95% confidence interval (CI).

Extended Data Fig. 7 Limited m6A differences in sex-linked genes are also brain-region–specific.

(A) m6A levels vs gene expression levels for 260 brain-region–specific m6A sites located in genes on chrX (B) DisGeNET analysis of the chrX m6A sites found in hippocampus compared to other brain regions.

Extended Data Fig. 8 Disease associated with age specific m6A methylation.

(A) Genes with age-specific m6A methylation associated with neurodegenerative disease, neurodevelopmental disorders and neuropsychiatric disorders (B) DisGeNET enrichment analysis of genes marked with age-specific m6A methylation. These genes were associated with autism, nicotine dependence, schizophrenia and epilepsy. P-values were adjusted for multiple testing using the Benjamini–Hochberg procedure to control the false discovery rate, and associations with FDR < 0.05 are considered statistically significant. (C) PsyGeNET enrichment analysis of genes marked with age-specific m6A methylation. Schizophrenia is the most enriched disorder.

Extended Data Fig. 9 Regional m6A patterns in disorder-risk genes.

A) Brain-region–specific m6A marked genes are more highly enriched for brain-related diseases than uniformly methylated genes according to DisGeNET analysis. P-values were adjusted for multiple testing using the Benjamini–Hochberg procedure to control the false discovery rate, and associations with FDR < 0.05 are considered statistically significant. (B) Boxplots showing brain-region–specific m6A levels of genes known to be associated with each disease/disorder. Brain regions most affected by the particular disorder tend to show higher m6A methylation rates in those associated genes than other brain regions tested. Each boxplot shows the number of genes with brain-region—specific m6A linked to each disease: Alzheimer’s disease (57), Parkinson’s disease (28), intellectual disability (90), ADHD (21), schizophrenia (91), bipolar disorder (46), epilepsy (61), and depression (33). Boxes extend from the 25th percentile (Q1) to the 75th percentile (Q3) of the relative errors, with the median indicated by a horizontal line inside each box. Whiskers indicate the minimum and maximum values within 1.5x the interquartile range (IQR) from the box. Differences between pairs of groups were assessed using a two-sided Wilcoxon-test, with a total of four pairwise comparisons performed. P-values are indicated on the plot. NS = not statistically significant.

Extended Data Fig. 10 Quality control metrics for m6A-QTL analysis.

(A) Numbers of m6A-QTLs and genetically controlled m6A peaks per brain region. (B) Overlap of m6A-QTLs between different brain regions. (C) Scatter plots showing the distance between m6A peaks and SNPs versus nominal p-values. Nominal p-values were calculated using QTLtools cis nominal analysis with PEER factors included as covariates and adjusted for multiple testing using the Benjamini–Hochberg procedure. Each brain region has an enrichment of SNPs around m6A peaks, and most statistically significant m6A-QTLs are closest to the m6A peak. Histograms also show the frequency of SNPs from the m6A peak.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1: Individual sample information including clinical diagnosis, sex, age, ethnicity and manner of death. Supplementary Table 2: Lists of m6A peaks identified for each individual sample. Supplementary Table 3: Normalized m6A levels across 93 samples for 22,465 calibrated peaks. Normalization was performed to account for technical variation, ensuring comparability across samples. Values represent relative m6A signal intensities after normalization. Supplementary Table 4: Brain-region-specific m6A methylation changes. Differential m6A methylation across brain regions was analyzed, with log2(fold change) and false discovery rate (FDR) values reported for each region. Positive log2(fold change) values indicate increased m6A methylation in other brain regions, while negative log2(fold change) values indicate decreased methylation in other regions. FDR-adjusted P values account for multiple testing, with P < 0.1 considered statistically significant. Supplementary Table 5: Age-specific m6A methylation changes. Differential m6A methylation across age was analyzed, with log2(fold change) and false discovery rate (FDR) values reported for each region. Positive log2(fold change) values indicate increased m6A methylation in children (aged 0–7 years), while negative log2(fold change) values indicate decreased methylation in children. FDR-adjusted P values account for multiple testing, with P < 0.2 considered statistically significant. Supplementary Table 6: List of sex-linked genes with brain-region-specific m6A differences. Supplementary Table 7: Age-specific m6A marked gene transcripts linked to disease. Supplementary Table 8: DisGeNET analysis results for (1) all m6A-marked genes, (2) brain-region-specific m6A-marked genes and (3) brain-invariant m6A peaks (all m6A peaks excluding brain-region-specific peaks). Supplementary Table 9: Characteristics of genetic variants that overlap with m6A peaks and are associated with neurodevelopmental disorder (NDD), intellectual disability (ID), seizure and autism. Each sheet is specific to a disease. Supplementary Table 10: m6A-QTL statistics for five brain regions: BA9, BA24, C, H and T. Each sheet contains region-specific data. Supplementary Table 11: Sex, age and number of donors included in each analysis. Information is provided separately for brain region comparisons, age-specific analyses (children aged 0–7 years versus adults aged 36–71 years) and sex-specific analyses (female versus male).

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Shafik, A.M., Peng, Y., Zhang, Z. et al. Multi-region m6A epitranscriptome profiling of the human brain reveals spatial and temporal variation and enrichment of disease-associated loci. Nat Neurosci 29, 195–205 (2026). https://doi.org/10.1038/s41593-025-02112-z

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