Simultaneous profiling of transcriptome and DNA methylome from a single cell

Genome Biol. 2016 May 5:17:88. doi: 10.1186/s13059-016-0950-z.


Background: Single-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to understand the direct correlation of DNA methylation and gene expression within single-cells. Due to large cell-to-cell variability and the lack of direct measurements of transcriptome and methylome of the same cell, the association is still unclear.

Results: Here, we describe a novel method (scMT-seq) that simultaneously profiles both DNA methylome and transcriptome from the same cell. In sensory neurons, we consistently identify transcriptome and methylome heterogeneity among single cells but the majority of the expression variance is not explained by proximal promoter methylation, with the exception of genes that do not contain CpG islands. By contrast, gene body methylation is positively associated with gene expression for only those genes that contain a CpG island promoter. Furthermore, using single nucleotide polymorphism patterns from our hybrid mouse model, we also find positive correlation of allelic gene body methylation with allelic expression.

Conclusions: Our method can be used to detect transcriptome, methylome, and single nucleotide polymorphism information within single cells to dissect the mechanisms of epigenetic gene regulation.

Keywords: Dorsal root ganglion; Gene regulation; Sensory neurons; Single-cell methylome; Single-cell transcriptome.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cells, Cultured
  • CpG Islands
  • DNA Methylation*
  • Gene Expression Profiling / methods*
  • Mice
  • Mice, Inbred C57BL
  • Neurons / cytology
  • Neurons / metabolism
  • Polymorphism, Single Nucleotide
  • Promoter Regions, Genetic
  • Sequence Analysis, DNA / methods*
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*
  • Transcriptome*