Genome organization by the slice

The human genome—46 long molecules of DNA bound to proteins and RNA—is not stored in the nucleus in a random fashion, like spaghetti in a bowl. Yet technologies to investigate its three-dimensional organization are few, and the ability to crosscheck experimental findings using independent methods has been very limited. Writing in Nature, Pombo and colleagues¹ now report an entirely new way of mapping chromatin topology. The approach, called genome architecture mapping (GAM), works with very small numbers of cells and reveals additional levels of genomic organization beyond what is easily accessible with current techniques.

That chromatin is not evenly distributed in the nucleus was apparent from early microcopy studies on stained nuclei, which showed distinct patterns of euchromatin and heterochromatin. Later, fluorescence in situ hybridization was applied to image the spatial relationships between a few specific genomic loci. In the early 2000s, the field was revitalized with the development of chromosome conformation capture (3C) methods, ranging from the original 3C itself, which detects interactions between two selected loci, to Hi-C, which investigates pairwise-interactions between all genomic loci^2,3. These approaches harness the power of proximity ligation⁴ and high-throughput sequencing to generate two-dimensional genome-wide maps of loci that are in physical proximity, which can then be computationally reconstructed into the three-dimensional architectures most likely to fit the observed contacts.

3C methods have been dogged by concerns that the sample processing steps—cell fixation, DNA digestion, and proximity ligation to link adjacent DNA regions—introduce biases in the data. Indeed, in some instances, validation experiments with in situ hybridization produced data that were difficult to reconcile with 3C results⁵. Another drawback of 3C methods is that they cannot directly measure organizational levels higher than point-to-point contacts.

The new GAM method also begins with a fixation step, but here the similarity to 3C ends. In the next step, ultramicrotome cutting and laser microdissection are used to collect 220-nm thin, randomly oriented slices of individual nuclei. The genetic material in each slice is sequenced, and three-dimensional information is extracted from the sequencing data by analyzing how often specific sequences co-occur in the same slice. “The approach is quite orthogonal to 3C methods, which is really important for the field,” says Job Dekker, a researcher at the University of Massachusetts Medical School and one of the inventors of 3C. “You do get stuck when you only have one way of doing an experiment,” notes Erez Lieberman Aiden, professor at Baylor College of Medicine and Rice University, in Houston.

Pombo and colleagues¹ apply GAM to human embryonic stem cells, as these have been extensively studied with 3C methods. Analyzing nuclear profiles from 400 cells, they find that GAM achieves a resolution of 30 kb, and a co-segregation analysis is consistent with previous Hi-C results, showing, for example, the existence of two nuclear compartments and of topology-associated domains (defined by frequent interactions within, and few outside, the domain). “It is pleasing to see that the initial data are largely consistent with Hi-C data,” says Dekker.

To identify specific interactions between individual genomic loci, the authors develop a more sophisticated model that distinguishes random contacts from specific interactions. The model, called SLICE, calculates how often random contacts between loci are expected to occur as a function of genomic distance based on the experimental data. It then identifies loci that are detected in the same nuclear slice significantly more often than would be expected if their contacts were random. SLICE takes into account various confounding factors, such as the detection efficiency of a genomic locus and genomic resolution.

At the global level, Pombo and colleagues¹ find a significant enrichment of connections between enhancers and active genes (defined previously by RNA-seq), which is especially pronounced at transcription start and end sites. In general, it is more difficult to detect interactions between three or more loci. In this respect, it is interesting that GAM detects three-way interactions between genomic regions at a resolution of hundreds of kilobases, roughly the size of topology-associated domains.

A notable advantage of GAM is that it requires only small numbers of cells. And in principle it can be applied not only to dissociated cells, as shown by Pombo and colleagues¹, but also to cells in fixed or frozen tissues, without the need to isolate the cells or culture them. “This might be the killer app for the technology,” says Aiden. Genome structure in cells in their native environment has been largely unexplored. “Especially for rare or difficult-to-culture cell types, the method is of high interest,” explains Dekker. Importantly, the authors also show that GAM uncovers higher levels of genome association, such as radial distribution of chromosomes, sub-chromosomal regions, and areas of chromosomal compaction, which could only be deduced indirectly from 3C data.

Although GAM is relatively labor-intensive and requires specialized skills and equipment, its potential to probe the hidden recesses of the nucleus, either independently or in combination with 3C and optical methods, appears vast. As Aiden notes, “One has to keep in mind that this is only the first report. Future improvements, such as cutting thinner slices, more sophisticated slicing strategies, or improved statistical analysis methods will certainly make the method even more powerful.”