Imputation approaches and quality standards for single-cell epigenetic age predictions

DNA methylation describes the addition of methyl groups, often between CpG dinucleotides. Single-cell bisulfite sequencing technologies allow the measurement of DNA methylation levels within individual cells. Epigenetic clocks are statistical models for computing biological age from DNA methylation levels, and have been used for detecting age variations in various disease contexts. However, there have been no attempts to apply epigenetic clocks to single-cell methylation data in humans. Thus, we questioned whether pre-built epigenetic clocks could be applied to single-cell methylation data; if so, how can we perform data quality control and imputation. We concluded that 1) linear regression-based epigenetic clocks can be applied to bisulfite-sequencing data, 2) data quality control can be used to reach the desired level of prediction accuracy, 3) first-principle imputation strategies could be used for missing data on selected CpG methylation sites, and 4) machine learning-based imputation tools could be used for accuracy-based age predictions. We built the first training-free, reference data-free framework for estimating epigenetic age in human single-cells, which would provide a foundation for future single-cell methylation-based age analyses.