Micro-C

Micro-C uses micrococcal nuclease (MNase) to digest chromatin into mononucleosomes, achieving ~150-200 bp resolution compared to Hi-C's 1-10 kb restriction fragments. This enables detection of fine-scale chromatin loops and enhancer-promoter interactions that are lost in coarser assays. The uniform nucleosome-level fragmentation eliminates the sequence bias inherent in restriction enzyme-based methods, providing more even coverage across the genome.

Micro-C reveals tetranucleosome folding motifs and higher-order chromatin fiber structures that are invisible to Hi-C. The assay captures interactions between adjacent nucleosomes, allowing researchers to observe how the 30-nm chromatin fiber organizes into tri-nucleosome and tetra-nucleosome units. This provides mechanistic insight into how local nucleosome positioning influences long-range genome architecture.

Unlike Hi-C, which relies on restriction enzyme digestion at specific 4-6 bp motifs, Micro-C's MNase digestion is largely sequence-independent. This eliminates restriction site bias and provides uniform coverage across gene bodies, intergenic regions, and repetitive elements. The result is a more complete and unbiased contact map, particularly important for studying heterochromatic regions and gene-dense loci.

Micro-C dramatically improves the detection of chromatin loops and architectural stripes—the linear tracks of enriched contact signal extending from loop anchors. These stripes, indicative of active loop extrusion by cohesin, are often obscured in Hi-C data. Micro-C's higher resolution enables precise mapping of CTCF-anchored loops and identification of transient extrusion intermediates, providing a dynamic view of genome folding.

Micro-C resolves multi-way chromatin interactions at regulatory hubs where multiple enhancers and promoters cluster in 3D space. These transcriptional condensates or super-enhancer hubs often involve contacts spanning only a few nucleosomes. Micro-C captures these fine-scale interactions, enabling the mapping of cis-regulatory networks that control gene expression with unprecedented detail.

Micro-C contact maps serve as high-resolution training targets for sequence-to-contact prediction models like Akita and DeepHiC. The nucleosome-level detail provides richer supervision signals, enabling models to learn the grammar of genome folding directly from DNA sequence. Predicted Micro-C maps can be used to simulate the effects of structural variants and non-coding mutations on 3D genome organization.