Decoding Single-cell DNA Methylomes for Epigenetic Cell Identity

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PROJECT SUMMARY / ABSTRACT Detailed information about the state of a cell, e.g., its lineage, mitotic history, proliferation potential, and functional competence, consolidates through epigenetic modifications of its DNA and chromatin. Among these modifications, DNA methylation has been widely studied and profiled to dissect tissue heterogeneity, disease cell of origin, and implement liquid biopsy-based disease diagnosis, thanks to its chemical stability and genome- wide distribution. Compared to bulk tissue methylome assays, which yield convoluted, hard-to-decipher signals from thousands to millions of cells, single-cell DNA methylome profiling is advantageous in cell identity-related applications. Despite the rapid increase in the volume and variety of single-cell DNA methylome data in recent years, availability of powerful and easy-to-use computational tools for their analyses is still an unmet demand. Consensus on the optimal strategy of interpreting cell states based on single-cell methylome data has not been reached. My lab’s long-term goal is to elucidate epigenetic cell identities at the single-cell level in humans and mice. Towards that goal, I propose to develop a suite of computational tools, for analyzing single-cell methylation data, that will encompass functions for data preprocessing, quality control, imputation, methylome signature extraction, cell state annotation, and exploratory visualization. These software tools will be engineered to be efficient, modular, and will be designed to operate both in high-performance computing environments and on basic laptops. These tools would be able to alert the investigator of potential data quality issues, feedback to accelerate methylation assay development, and discover biological links between the DNA methylome and the cell’s genetic makeup, mitotic history, cell-cycle stage, differentiation capacity, and functional state. They can also be used to study cell population traits in bulk tissue samples. Together with these computational tools, we also aim to distribute a cell-type-resolution reference methylome catalog to benefit the research community. My proposed work will deliver computational tools and methylation references to deepen our understanding of the role of DNA methylation in determining cell lineages and provide practical tools for epigenetic cell typing. The methods to be developed could be readily plugged into exploratory and translational applications in broader biomedical contexts.