A large international study published in Nature has presented an integrated analysis of the human “4D nucleome." The study describes how the three-dimensional organization of the genome changes across space, time, and cell states, and how these features relate to gene regulation and DNA replication. The work was produced by the NIH-funded 4D Nucleome Project and focuses on two commonly used human cell types: embryonic stem cells and immortalized fibroblasts.
The investigators combined multiple genome architecture assays – including Hi-C, Micro-C, SPRITE, GAM, ChIA-PET, PLAC-seq, TSA-seq, and DamID – to map chromatin loops, topologically associating domains (TADs), nuclear compartments, and proximity to nuclear structures such as speckles, nucleoli, and the nuclear lamina. More than 140,000 chromatin loops were identified per cell type, highlighting the scale and complexity of long-range genomic interactions.
A key contribution of the study is a systematic comparison of these assays. The authors show that different methods capture different aspects of genome organization. For example, Hi-C and SPRITE are better suited to detecting large-scale chromatin compartments, while Micro-C provides higher resolution for chromatin loops. Targeted approaches, such as ChIA-PET and PLAC-seq, preferentially identify interactions linked to transcription. These findings provide practical guidance for laboratories selecting assays to study structural genome changes.
The integrated data were also used to define spatial genome states that reflect how genomic regions are positioned within the nucleus. These states correlated with gene expression, histone modifications, and replication timing, offering a framework for interpreting how noncoding variants may affect gene regulation through changes in genome architecture.
The study also demonstrates computational models that predict genome folding directly from DNA sequence. These models were used to estimate how sequence variants could alter chromatin structure and long-range interactions, a capability that may be relevant for future diagnostic interpretation of genetic variants.
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