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Scientists develop technique for high-resolution single cell epigenetic analysis

Van Andel Institute scientists have developed an improved technique to comprehensively profile DNA methylation in single cells, an advance that will help researchers better study the role of epigenetics in cancer and other diseases.

DNA methylation is an epigenetic mechanism that influences how and when the instructions in DNA are used without changing the DNA sequence itself. As a result, DNA methylation is a key player in many fundamental biological processes including development, gene expression and cell differentiation. Methylation errors are well-known contributors to cancer and have been implicated in a host of other disorders.

The new method, called scDEEP-mC, yields very high-resolution maps of DNA methylation and is the most efficient single-cell DNA methylation technique developed to date. Scientists can use scDEEP-mC to reveal powerful new insights into single-cell biology and identify differences that set rare cell types apart from other cells. scDEEP-mC also supports many cutting-edge analyses in single cells, including estimation of cellular age using epigenetic clocks, analysis of hemimethylation and creation of whole-chromosome X-inactivation epigenetic profiles.

A study describing scDEEP-mC was recently in the journal Nature Communications. VAI's Peter W. Laird, Ph.D., and Hui Shen, Ph.D., are co-corresponding authors.

"scDEEP-mC allows us to see DNA methylation at varying stages of DNA replication in individual cells—something that has not been possible until now," said Nathan Spix, Ph.D., co-first author of the study and a postdoctoral fellow in the Laird Lab.

"For example, scDEEP-mC can help us pinpoint early DNA methylation changes in single cells that go on to become cancerous. If we know what goes wrong in the early stages of this process, we can use that information to develop new ways to detect and treat disease."

Because of technical limitations, other DNA methylation analysis methods do not allow for direct comparisons between cells. Instead, they require scientists to average signals from groups of cells, obscuring important but subtle differences between individual cells. scDEEP-mC's high-resolution data enables scientists to more effectively identify cell subtypes, methylation patterns and other important features such as differences between older cells and newly replicated cells.