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Abstract：
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.
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.
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.
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.
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.
摘要：
背景：细胞同一性部分由调节基因表达的细胞类型特异性表观基因组谱决定。在神经科学中，迫切需要分离和表征健康和疾病中特定CNS细胞类型的表观基因组。在这项研究中,我们开发了一种体内标记小鼠模型（Camk2a-NuTRAP），用于在没有细胞分选的情况下配对分离神经元DNA和RNA，然后使用该模型评估表观基因组调控，特别是DNA修饰，神经元和神经胶质之间的基因表达。
结果：在验证Camk2a-NuTRAP模型的细胞特异性后，我们进行了TRAP-RNA-Seq和INTACT-全基因组氧化亚硫酸氢盐测序(WGoxBS),以评估幼鼠(4月龄)海马区的神经元翻译组和表观基因组.使用酶促甲基-Seq(EM-Seq)和纳米孔测序来验证WGoxBS的发现。将神经元数据与NuTRAP模型的小胶质细胞和星形细胞数据进行比较，小胶质细胞的mCG水平最高，其次是星形胶质细胞，然后是神经元，对于hmCG和mCH观察到相反的模式。细胞类型之间的差异修饰区域主要在基因体内和远端基因间区域内发现，而不是近端启动子。在不同的细胞类型中，DNA修饰之间存在负相关(mCG，MCH,hmCG）和近端启动子的基因表达。相比之下,观察到基因体mCG与基因表达呈负相关，远端启动子和基因体hmCG与基因表达呈正相关。此外，我们确定了跨启动子和基因体区域的mCH与基因表达之间的神经元特异性反比关系。
结论：神经元，星形胶质细胞,和小胶质细胞表现出不同的全基因组水平的mCG，hmCG,和MCH，可在各种分析方法中重现。然而,修饰-基因表达关系在细胞类型之间是保守的。基因体和远端调控元件中细胞类型差异修饰的富集，但不是近端启动子，强调这些区域的表观基因组模式可能是细胞身份的更大决定因素。这些发现还证明了在神经表观基因组分析中区分mC和hmC的重要性。通常被解释为mCG的30%可以是hmCG，与mCG相比，通常与基因表达有不同的关系。
结果：在验证Camk2a-NuTRAP模型的细胞特异性后，我们进行了TRAP-RNA-Seq和INTACT-全基因组氧化亚硫酸氢盐测序(WGoxBS),以评估幼鼠(4月龄)海马区的神经元翻译组和表观基因组.使用酶促甲基-Seq(EM-Seq)和纳米孔测序来验证WGoxBS的发现。将神经元数据与NuTRAP模型的小胶质细胞和星形细胞数据进行比较，小胶质细胞的mCG水平最高，其次是星形胶质细胞，然后是神经元，对于hmCG和mCH观察到相反的模式。细胞类型之间的差异修饰区域主要在基因体内和远端基因间区域内发现，而不是近端启动子。在不同的细胞类型中，DNA修饰之间存在负相关(mCG，MCH,hmCG）和近端启动子的基因表达。相比之下,观察到基因体mCG与基因表达呈负相关，远端启动子和基因体hmCG与基因表达呈正相关。此外，我们确定了跨启动子和基因体区域的mCH与基因表达之间的神经元特异性反比关系。
结论：神经元，星形胶质细胞,和小胶质细胞表现出不同的全基因组水平的mCG，hmCG,和MCH，可在各种分析方法中重现。然而,修饰-基因表达关系在细胞类型之间是保守的。基因体和远端调控元件中细胞类型差异修饰的富集，但不是近端启动子，强调这些区域的表观基因组模式可能是细胞身份的更大决定因素。这些发现还证明了在神经表观基因组分析中区分mC和hmC的重要性。通常被解释为mCG的30%可以是hmCG，与mCG相比，通常与基因表达有不同的关系。
