Combining imaging and sequencing for a better 3D genome. Credit: Katie Vicari/Springer Nature

The plethora of methods for probing a genome's 3D architecture all share the same principle: jointly isolate and sequence genomic regions that are distant from one another in linear space but in close proximity in 3D. Methods differ in whether they probe interactions genome wide, such as Hi-C, or around specific loci, such as 3C and 4C. Some approaches enrich for genomic loci that interact with a certain protein, such as ChIA-PETs or the recent Hi-ChIP (Nat. Methods 13, 919–922, 2016); others work with sparse input material, such as the recent single-cell Hi-C (Nature, 502, 59–64, 2013; Nat. Methods 14, 263–266, 2017). In addition to these techniques, which rely on cross-linking interacting loci, the year 2017 also saw a ligation-free method, genome architecture mapping (Nature 543, 519–524, 2017).

In all these methods, sequence-based contact frequencies are converted into contact maps, which generate important insights into chromatin architecture. But a strictly molecular readout cannot visualize interactions, let alone follow chromatin dynamics in live cells.

The assumption that imaging methods, such as fluorescence in situ hybridization (FISH), can validate 3C methods is not always accurate. Contact frequency, determined by 3C methods, and spatial distance, viewed by FISH, measure different aspects of genome organization. Also, FISH views an order of magnitude fewer interactions than those probed by 3C, thus it misses rare events. Reconciling these data to obtain a unified chromosome model is an outstanding challenge (Nat. Methods 14, 673–678, 2017).

To go beyond static FISH images, CRISPR-based methods for viewing chromatin dynamics in living cells have recently emerged (e.g., Nat. Commun. 8, 14725, 2017). These methods have been largely restricted to repeat regions, but with continued progress they will become sensitive enough to follow multiple, unique, nonrepetitive loci.

Each approach adds essential information, and integrating all the data is likely to yield the best answers. Researchers want to simultaneously achieve a high-resolution view of regions interacting in real time and know the sequences of these regions. This is particularly important, as progress is being made to manipulate genome structure at will (e.g., Nat. Commun. 8, 15993, 2017). Combining molecular and imaging readouts will deepen our understanding of the effects of wild-type or altered genome architectures on biological processes.