Technologies and applications of single-cell DNA methylation sequencing

Abstract

DNA methylation is the most stable epigenetic modification. In mammals, it usually occurs at the cytosine of CpG dinucleotides. DNA methylation is essential for many physiological and pathological processes. Aberrant DNA methylation has been observed in human diseases, particularly cancer. Notably, conventional DNA methylation profiling technologies require a large amount of DNA, often from a heterogeneous cell population, and provide an average methylation level of many cells. It is often not realistic to collect sufficient numbers of cells, such as rare cells and circulating tumor cells in peripheral blood, for bulk sequencing assays. It is therefore essential to develop sequencing technologies that can accurately profile DNA methylation using small numbers of cells or even single cells. Excitingly, many single-cell DNA methylation sequencing and single-cell omics sequencing technologies have been developed, and applications of these methods have greatly expanded our understanding of the molecular mechanism of DNA methylation. Here, we summaries single-cell DNA methylation and multi-omics sequencing methods, delineate their applications in biomedical sciences, discuss technical challenges, and present our perspective on future research directions.

Mini-review of DNA Methylation Detection Techniques and Their Potential Applications in Disease Diagnosis, Prognosis, and Treatment

“…Integration of novel sequencing techniques with advanced bioinformatics tools could provide a deeper understanding of the spatial and temporal dynamics of DNA methylation changes. In addition, advances in single-cell sequencing technologies may lead to the ability to analyze DNA methylation patterns at the single-cell level . This could unveil heterogeneity within cell populations and offer insights into the role of DNA methylation in cellular diversity.…”

“…In addition, advances in single-cell sequencing technologies may lead to the ability to analyze DNA methylation patterns at the single-cell level. 117 This could unveil heterogeneity within cell populations and offer insights into the role of DNA methylation in cellular diversity. Besides, continued progress in machine learning and artificial intelligence (AI) algorithms is likely to enhance the analysis of complex DNA methylation data.…”

DenoiseST: A dual-channel unsupervised deep learning-based denoising method to identify spatial domains and functionally variable genes in spatial transcriptomics

“…In such circumstances, addressing the integration issue of multiple batch data becomes imperative. In addition, the augmentation of DenoiseST's capabilities can be realized through the integration of diverse single-cell omics datasets, including scRNA-seq, scATAC-seq 52 , and scMethyl-seq 53 . This integrative approach seeks to attain a holistic comprehension of cellular heterogeneity and epigenetic regulation.…”

Prognostic evaluation of the novel blueprint of DNA methylation sites by integrating bulk RNA‐sequencing and methylation modification data in endometrial cancer

“…In mammals, CpG islands, which are DNA sequences rich in CpG dinucleotides, are commonly methylated and located in the promoter and exonic regions of approximately 40% of mammalian genes. 20 The methylation of CpG dinucleotides is catalyzed by three DNA methyltransferases (DNMTs), namely DNMT1, DNMT3A and DNMT3B, utilizing S-adenosyl-L-methionine as the methyl group donor. 21 Normal DNA methylation levels are crucial for regulating the expression of imprinted genes from both the paternal and maternal alleles during development.…”