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.

Keywords: DNA methylation; cancer; single cell.

Figure 1

Single-cell approaches to identify heterogeneity in tumour population. Tumour-initiating stem cells develop in tissue of origin. These undergo branched tumour evolution, acquiring random mutations and epigenomic alterations. The tumour microenvironment influences heterogeneity via physical and chemical signals. A combinatorial effect of these concepts induces a highly heterogeneous tumour, as well as circulating tumour cells (CTCs) disseminating from said tumour. Single-cell methylome sequencing of tumours can provide insight into subpopulations/differing cell states by clustering cells: (a) clustering via t-SNE, (b) clustering via UMAP, and (c) cell lineages/differentially methylated regions via heatmaps. Created with https://biorender.com/ (accessed on 25 July 2022).

Figure 2

Single-cell isolation and experimental approaches for single-cell DNA methylation analyses. Created using https://biorender.com/ (accessed on 25 July 2022).

Figure 2

Single-cell isolation and experimental approaches for single-cell DNA methylation analyses. Created using https://biorender.com/ (accessed on 25 July 2022).