Single-Cell DNA Methylation Analysis in Cancer

Abstract

Morphological, transcriptomic, and genomic defects are well-explored parameters of cancer biology. In more recent years, the impact of epigenetic influences, such as DNA methylation, is becoming more appreciated. Aberrant DNA methylation has been implicated in many types of cancers, influencing cell type, state, transcriptional regulation, and genomic stability to name a few. Traditionally, large populations of cells from the tissue of interest are coalesced for analysis, producing averaged methylome data. Considering the inherent heterogeneity of cancer, analysing populations of cells as a whole denies the ability to discover novel aberrant methylation patterns, identify subpopulations, and trace cell lineages. Due to recent advancements in technology, it is now possible to obtain methylome data from single cells. This has both research and clinical implications, ranging from the identification of biomarkers to improved diagnostic tools. As with all emerging technologies, distinct experimental, bioinformatic, and practical challenges present themselves. This review begins with exploring the potential impact of single-cell sequencing on understanding cancer biology and how it could eventually benefit a clinical setting. Following this, the techniques and experimental approaches which made this technology possible are explored. Finally, the present challenges currently associated with single-cell DNA methylation sequencing are described.

Pancreatic Neuroendocrine Tumors: Signaling Pathways and Epigenetic Regulation

“…Larger-scale studies and more advanced technologies are desperately needed to validate the existing results because the identification of molecularly different NET subtypes will have a significant impact on clinical practice, given the high heterogeneity of PNETs. For example, in the research of some commonly occurring cancers, there have been reports of using advanced single-cell whole genome methylation profiling to obtain much more complicated information about the cancer epigenome [301].…”

Advances in single-cell long-read sequencing technologies

“…Genome and epigenome sequencing techniques such as single-cell whole-genome amplification and sequencing (scWGA, scWGS), single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), single-cell whole-genome bisulfite sequencing (scWGBS) and single-cell reduced-representation bisulfite sequencing (scRRBS-seq) are constrained by the available starting material that is one diploid DNA molecule per cell. This limitation is further exacerbated in single-cell epigenome profiling, where harsh bisulfite conversion leads to significant loss of DNA ( 12 ). Moreover, the short reads obtained have a high mapping uncertainty in repetitive regions, one of the key regions examined in DNA methylation sequencing ( 13 ).…”

Computational deconvolution of DNA methylation data from mixed DNA samples

“…to estimate tumour percentages or study the immune context) in mixed cell populations is challenging. Indeed, to properly identify the cell-type composition of a sample single-cell methylation profiling tools are required, but these are currently very costly, therefore difficult to scale, and often generate noisy and sparse data [ 14 ]. For this reason, several computational approaches have been developed during the past years to infer the abundance of different cell types in heterogeneous samples.…”