Single-Cell Epigenetic Age Profiling via CpG Filtering
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Solution Overview
Problem
Existing epigenetic clocks rely on bulk samples, which obscure epigenetic heterogeneity among individual cells and require large amounts of DNA, limiting accurate epigenetic profiling, especially using few cells.
Innovation Solution
A method for estimating the epigenetic age of a single cell by providing a reference methylation probability dataset, filtering methylation profiles to include CpG sites with high Pearson correlation with age, calculating the likelihood of observing the filtered profile at different ages, and determining the age with the greatest likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bulk samples are used for DNA methylation analysis, then consistent and robust coverage of CpGs across the genome is achieved, but epigenetic heterogeneity among individual cells is obscured
Solution Approach 1:
The invention segments the bulk tissue sample into individual single cells for analysis. By isolating and analyzing methylation profiles at the single-cell level, the method preserves epigenetic heterogeneity information that would be obscured in bulk samples, while maintaining reliable coverage through targeted sequencing approaches.
Solution Approach 2:
The invention extracts specific age-related CpG sites from the complete methylome to create a focused profiling approach. By selecting and analyzing only the most informative CpG sites associated with aging, the method achieves reliable age estimation with limited sequencing depth, enabling single-cell analysis without requiring comprehensive genome-wide coverage.
2Measurement precision
If bulk samples are used for DNA methylation analysis, then robust CpG coverage is achieved, but large amounts of DNA input material are required
Solution Approach 1:
The invention extracts and focuses sequencing efforts on a specific subset of age-related CpG sites rather than attempting to sequence the entire methylome. This targeted approach achieves sufficient measurement precision for age estimation using minimal DNA input material, making the technique feasible for single-cell and low-input applications.
Solution Approach 2:
The invention applies partial sequencing coverage focused on the most informative CpG sites rather than complete genome-wide sequencing. By performing partial action on the methylome—sequencing only the critical age-related regions—the method achieves adequate measurement precision with dramatically reduced DNA requirements.
3Loss of information
If single-cell methylation sequencing is performed, then epigenetic heterogeneity is resolved, but sparse and partial methylome profiles are obtained
Solution Approach 1:
The invention extracts and analyzes only the most informative age-related CpG sites from single-cell methylome data. By focusing on this specific subset of sites that show strong age associations, the method achieves accurate age estimation despite the sparse and partial coverage inherent in single-cell sequencing, converting the weakness of incomplete data into a manageable profiling approach.
Data Source
AI summary
The invention features a method of estimating an epigenetic age of a single cell from a mammalian tissue, the method comprising: creating a reference methylation probability data set comprising estimates in the change in average methylation levels with age for each CpG site in a plurality of CpG sites, creating a filtered methylation profile of the single cell comprising a defined number of CpG sites that exhibit the greatest absolute Pearson correlation with an age in the reference methylation probability data set, wherein the CpG sites are those common between the single cell and the reference methylation probability dataset, calculating the likelihood of observing the filtered methylation profile of the single cell for a plurality of ages, and determining the age for which the likelihood is greatest among the ages in the plurality of ages to produce the epigenetic age of the single cell. This method, when modified, is also amenable to estimate epigenetic age from shallow methylation sequencing data in bulk samples. Altogether, this framework enables both high-resolution epigenetic age profiling in single cells, combined with drastic cost-reduction for shallow bulk epigenetic age profiling.