The complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand—methylated or non-methylated—and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing. We compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a “FibroScore” correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types. This proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.

中文翻译：

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基于单个CpG位点的靶向DNA甲基化分析，对人体组织中的细胞亚群进行反卷积

组织中不同细胞类型的复杂组成可以通过对大量基因表达图谱进行反卷积或使用各种单细胞测序方法来估算。另外，DNA甲基化（DNAm）配置文件已用于建立多种人类组织和细胞类型的地图集。DNAm特别适用于细胞类型的反卷积，因为每个CG二核苷酸（CpG位点）每个DNA链只有两个状态（甲基化或非甲基化），并且这些表观遗传修饰在细胞分化过程中非常一致。到目前为止，DNAm图谱的去卷积意味着许多CpG的复杂特征，而这些特征通常通过Illumina BeadChip微阵列通过全基因组分析来测量。在这个研究中，我们研究了利用单个细胞类型特异性CpG位点表征组织中细胞类型的可行性是否可行，这可以通过靶向分析（例如焦磷酸测序）解决。我们编辑并整理了14种不同的非恶性人类细胞类型的579 Illumina 450k BeadChip DNAm配置文件。应用了训练和验证策略来识别和测试特定于细胞类型的CpG。我们最初专注于使用两种在成纤维细胞中甲基化或甲基化不足的CpG来估计成纤维细胞的相对数量。这两个DNAm水平组合成“ FibroScore”与纤维化状态相关，并与各种类型癌症的总体存活率相关。此外，我们确定了用于白细胞，内皮细胞，上皮细胞，肝细胞，神经胶质细胞，神经元的低甲基化CpG，成纤维细胞和诱导性多能干细胞。在定义的细胞混合物的其他BeadChip数据集中测试了这8个CpG签名的准确性，其结果可与基于数千个CpG的先前发布的签名相媲美。最后，我们建立并验证了可用于细胞类型分类和去卷积的相关CpG的焦磷酸测序测定。该概念验证研究表明，对单个CpG进行的DNAm分析反映了细胞混合物和不同组织的细胞组成。这些基因组区域的目标分析有助于在基础研究和临床环境中应用的可靠方法。在定义的细胞混合物的其他BeadChip数据集中测试了这8个CpG签名的准确性，其结果可与基于数千个CpG的先前发布的签名相媲美。最后，我们建立并验证了可用于细胞类型分类和去卷积的相关CpG的焦磷酸测序测定。该概念验证研究表明，对单个CpG进行的DNAm分析反映了细胞混合物和不同组织的细胞组成。这些基因组区域的目标分析有助于在基础研究和临床环境中应用的可靠方法。在定义的细胞混合物的其他BeadChip数据集中测试了这8个CpG签名的准确性，其结果可与基于数千个CpG的先前发布的签名相媲美。最后，我们建立并验证了可用于细胞类型分类和反卷积的相关CpG的焦磷酸测序测定。该概念验证研究表明，对单个CpG进行的DNAm分析反映了细胞混合物和不同组织的细胞组成。这些基因组区域的目标分析有助于在基础研究和临床环境中应用的可靠方法。我们建立并验证了可用于细胞类型分类和去卷积的相关CpG的焦磷酸测序测定。该概念验证研究表明，对单个CpG进行的DNAm分析反映了细胞混合物和不同组织的细胞组成。这些基因组区域的目标分析有助于在基础研究和临床环境中应用的可靠方法。我们建立并验证了可用于细胞类型分类和去卷积的相关CpG的焦磷酸测序测定。该概念验证研究表明，对单个CpG进行的DNAm分析反映了细胞混合物和不同组织的细胞组成。这些基因组区域的目标分析有助于在基础研究和临床环境中应用的可靠方法。