Background
Cellular identity is determined partly by cell type-specific epigenomic profiles that regulate gene expression. In neuroscience, there is a pressing need to isolate and characterize the epigenomes of specific CNS cell types in health and disease. In this study, we developed an in vivo tagging mouse model (Camk2a-NuTRAP) for paired isolation of neuronal DNA and RNA without cell sorting and then used this model to assess epigenomic regulation, DNA modifications in particular, of gene expression between neurons and glia.
Conclusions
Neurons, astrocytes, and microglia demonstrate different genome-wide levels of mCG, hmCG, and mCH that are reproducible across analytical methods. However, modification-gene expression relationships are conserved across cell types. Enrichment of differential modifications across cell types in gene bodies and distal regulatory elements, but not proximal promoters, highlights epigenomic patterning in these regions as potentially greater determinants of cell identity. These findings also demonstrate the importance of differentiating between mC and hmC in neuroepigenomic analyses, as up to 30% of what is conventionally interpreted as mCG can be hmCG, which often has a different relationship to gene expression than mCG.
Results
After validating the cell-specificity of the Camk2a-NuTRAP model, we performed TRAP-RNA-Seq and INTACT-whole genome oxidative bisulfite sequencing (WGoxBS) to assess the neuronal translatome and epigenome in the hippocampus of young mice (4 months old). WGoxBS findings were validated with enzymatic methyl-Seq (EM-Seq) and nanopore sequencing. Comparing neuronal data to microglial and astrocytic data from NuTRAP models, microglia had the highest global mCG levels followed by astrocytes and then neurons, with the opposite pattern observed for hmCG and mCH. Differentially modified regions between cell types were predominantly found within gene bodies and distal intergenic regions, rather than proximal promoters. Across cell types there was a negative correlation between DNA modifications (mCG, mCH, hmCG) and gene expression at proximal promoters. In contrast, a negative correlation of gene body mCG and a positive relationship between distal promoter and gene body hmCG with gene expression was observed. Furthermore, we identified a neuron-specific inverse relationship between mCH and gene expression across promoter and gene body regions. Conclusions: Neurons, astrocytes, and microglia demonstrate different genome-wide levels of mCG, hmCG, and mCH that are reproducible across analytical methods. However, modification-gene expression relationships are conserved across cell types. Enrichment of differential modifications across cell types in gene bodies and distal regulatory elements, but not proximal promoters, highlights epigenomic patterning in these regions as potentially greater determinants of cell identity. These findings also demonstrate the importance of differentiating between mC and hmC in neuroepigenomic analyses, as up to 30% of what is conventionally interpreted as mCG can be hmCG, which often has a different relationship to gene expression than mCG.