While recent advancements let us map the epigenome of a single-cell, the same studies can leave us a bit single-minded when facing the many layers of the epigenetic landscape. Thankfully, to quench our integrative omics thirst, a clever new technique reveals that just because you’re looking at single cells doesn’t mean you only need to consider a single solitary mark.
This new sequencing technique comes at you from the labs of Wolf Reik (Babraham Institute, UK) and Oliver Stegle (EMBL-EBI, UK). A previous collaboration between the two groups gave us single cell methylome and transcriptome sequencing (scM&T-seq), and now, the talented team throws nucleosomes occupancy into the mix by adapting Nucleosome Occupancy and Methylation sequencing (NOMe-seq) to give us Single-cell Nucleosome, Methylation, Transcription sequencing (scNMT-seq).
Here’s how scNMT-seq works:
- Single-cells are sorted and lysed
- A GpC methyltransferase labels accessible DNA
- RNA and DNA are physically separated
- RNA is subjected to library preparation and sequencing using Smart-seq2
- DNA is treated to library perpetration and single-cell bisulfite sequencing (scBS-seq), where CpG and GpC methylation provide information about DNA methylation and nucleosome occupancy, respectively, thanks to some clever bioinformatics
Notably, by building on NOME-seq, which uses a GpC methyltransferase to label accessible DNA, inaccessible chromatin can be distinguished from missing data, a problem that typically haunts single-cell methods. This difference provides NOMe-seq and scNMT-seq with a leg up when compared to ATAC-seq and DNase-seq.
In order to test out their fancy new technique, the group utilized mouse embryonic stem cells (mESCs) and directly compared the results to data from scM&T-seq, scBS-seq, and bulk BS-seq. Their tests established that not only does scNMT-seq reveal insight into marks in isolation, but it also exposes known coordinated events at distinct regulatory elements. Moving past comparisons to known data sets, scNMT-seq was also used to detail novel interactions at individual loci. Finally, the team journeyed down the epigenetic landscape and examined differentiating mESCs, where they discovered distinct dynamics in the coupling of all three molecular layers at developmentally relevant regions.
Co-senior author Wolf Reik shares, “It’s easy to see that different types of cells would have different genetic activity. But transcription in individual cells can also vary between cells of the same type, which isn’t quite as intuitive. As biologists we want to get to the bottom of this and understand what differences are ‘normal’, which ones might signal disease and how everything fits together. Using our new technique, we will be able to understand how changes in gene expression occur between cells of the same type, and what they could mean for the future of each cell.”
Go single in on this triple play over at Nature Communications, February 2018