Dataset Information

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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE303678 | GEO | 2025/08/24

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:The recognition of tumor heterogeneity has highlighted the necessity of examining tumor samples through the lens of single-cell genomics. In glioblastoma (GBM), a highly heterogeneous tumor, single-cell analysis is critical to assist in assessing tumor composition and in the longitudinal analysis of response to therapies. However, single-cell genomic approaches face practical challenges for broad implementation, underscoring the importance of developing deconvolution methods that may assist in the interpretation of bulk profiles and can be deployed at scale. Bulk DNA methylation data, a stable and widely used diagnostic tool in gliomas and central nervous system tumors, provides a promising substrate for deconvolution. However, the limited availability of cell state-specific references in DNA methylation, coupled with low-coverage single-cell DNA methylation data, poses significant challenges. We present a hierarchical non-negative matrix factorization approach to deconvolute bulk DNA methylation profiles, initially resolving cell types and subsequently refining cell states within a cell type. By integrating multi-omics single-cell data, we mapped DNA methylation components to their transcriptional counterparts, enabling accurate predictions of transcriptional cellular composition from bulk DNA methylation. This methodology allows the decomposition of GBM bulk DNA methylation into glial, immune, neuronal, and malignant cell types, with further distinction into malignant stem-like and malignant differentiated cell states. Our findings reveal that low cancer cell fractions can distort classification, prompting the development of an in-silico purification method to enhance diagnostic accuracy. Additionally, we provide a framework to assist in quantifying the influences of the immune micro-environment on GBM bulk classification, unmasking the underlying genetic heterogeneity and tumor subtype. Our work provides a blueprint to reconcile DNA methylation, bulk transcription-based and single-cell classifications of GBM.

Dataset Information

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

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets