While chromosome conformation capture (3C) methods like single-cell Hi-C focus on coupled pairs of chromosome contacts, and even their promiscuous regions, candid snapshots of chromosomal threesomes have remained an intimate secret … until now. The genomic architects in the labs of Ana Pombo and Mario Nicodemi developed a new method and accompanying statistical model to map chromatin contacts in a way you’ve yet to experience. This technique is ligation-free, requires few cells, is substantially different from both 3C-based methods and FISH, and can capture not only two-way, but also three-way chromosome interactions.
Co-first author Robert Beagrie frames the advantage, “People have been measuring two-way contacts for a long time. Those studies have often shown that you can have a set of different DNA elements that interact with each other in pairs. With this new approach we are able to generate a genome-wide catalogue of all the regions that we are confident interact in groups.”
Genome architecture mapping (GAM) involves ultrathin cryosectioning of cells, laser microdissection of their nuclei, and then sequencing of these sections. Then, after all that slicing and dicing, a mathematical model termed SLICE (statistical inference of co-segregation) can cut through the noise and identify hotspots of multi-enhancer contact.
By using GAM to SLICE through mouse embryonic stem cells, the team found that:
- It reproduces the overall genome architecture seen by Hi-C.
- There is an enrichment for interactions between enhancers and active genes.
- These occur particularly at transcription start and termination sites.
- The patterns mirror the distribution of RNA polymerase II over active genes.
- There is an abundance of three-way interactions between topologically associated domains (TADs), particularly at super-enhancers and highly transcribed genes.
Pombo opines, “There is huge potential for applying this in human tissue samples to catalogue contacts between regulatory regions and their target genes, and to use that to understand genetic variation and how it might alter aspects of nuclear biology.” Nicodemi expands, “By exploiting the unique nature of GAM data, mathematical models can reliably derive such information, opening the way to identify multiple, group interactions that could play a key role in the regulation of genes.”
Beagrie concludes, “We can now ask whether a gene is contacted at the same time by all of its enhancers, or by each enhancer one at a time? We know that many genes that are important for early development have multiple enhancers. But how and why they are acting to regulate genes remain unanswered questions.”
Go learn how to catch chromatin in the act over at Nature, March 2017