Single-cell DNA methylation analysis tool Amethyst reveals distinct non-CG methylation patterns in human glial cells

Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties from heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and enables clustering of distinct biological populations, cell type annotation, differentially methylated region calling, and interpretation of results - facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell and human cortex data. We further apply Amethyst to an atlas-scale brain dataset and deconvolute non-CG methylation patterns in human astrocytes and oligodendrocytes, challenging the notion that this form of methylation is principally relevant to neurons in the brain. Tools such as Amethyst will increase accessibility to single-cell methylation data analysis, catalyzing research progress across diverse contexts. Single-cell methylation analysis was performed on human brain tissue and peripheral blood mononuclear cells.