Cambridge, UK, 22 February 2018, Scientists at the Babraham Institute, the European Bioinformatics Institute (EMBL-EBI) and the University of Edinburgh have developed the first method to analyse three molecular layers simultaneously during single-cell analysis. Comparing the molecular interactions of cells in this way reveals differences that could have an impact on both the early stages of life and the first stages of diseases such as cancer. Published in Nature Communications, the new method is called single-cell Nucleosome, Methylation, Transcription sequencing (scNMT-seq).
Biologists are always looking for a clearer view on biological systems, and how they interact with one another to sustain life. In cell biology, it is relatively easy to obtain averaged results collected from many cells, and more recently, we are even able to zoom into single cells.
Before scNMT-seq, it was extremely difficult to study the effect that changes taking place in single cells were having across different molecular levels.
With better tools for deciphering differences between single cells, scientists can gain a clearer picture of how differences between cells influence our normal development, and the development of disease.
scNMT-seq is a new method that combines three ‘layers’ of molecular activity: nucleosome, DNA methylation and transcription.
Nucleosomes: Genes are packaged in the nuclei of cells, nicely wrapped around protein structures called nucleosomes. The way a gene is wrapped around a nucleosome affects its activity – that is, whether the gene is ‘on’ or ‘off’.
DNA methylation refers to chemical changes to DNA that also affect gene activity – specifically, how the instructions represented by genes are ‘read’.
Transcription involves active genes being copied to produce messenger RNA (mRNA) molecules, which are essentially molecular ‘transcripts’ that can be read.
“Our method integrates multiple aspects of cell biology, combining precise biological studies and complex computational approaches,” explains Oliver Stegle, Group Leader at EMBL-EBI. “This gives us more direct insights into the relationships between epigenetics and gene activity. That is really important for understanding the processes that convert a single cell, either during normal development or in the development of a disease.”
“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,” explains Wolf Reik, Head of the Epigenetics Laboratory at the Babraham Institute. “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.”
“Each life starts out as a single cell, which develops into a community of hundreds of different cell types,” says Ricard Argelaguet, PhD student at EMBL-EBI. “Unravelling the many mysteries that make this happen is the ultimate quest of all developmental biologists. scNMT-seq allows researchers to study molecular changes in single cells at multiple molecular levels simultaneously. The method is a very powerful tool for uncovering the devil in the details of development and disease.”
CLARK, S., et al. (2018). scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells . Nature Communications (in press). Published online 22 February; 10.1038/s41467-018-03149-4
Read more on the Babraham Institute website.
Work at the Babraham Institute is possible thanks to the Biotechnology and Biological Sciences Research Council (BBSRC), in particular this work forms part of the Strategic Programme Grant for Epigenetics. This work also included funding from the Wellcome Trust, EU Blueprint, EpiGeneSys. Gavin Kelsey is supported by the Medical Research Council (MRC). Oliver Stegle is supported by the European Molecular Biology Laboratory (EMBL). Chantriolnt-Andreas Kapourani is supported by the EPSRC Centre for Doctoral Training in Data Science and the University of Edinburgh.