Si-C is a method for inferring super-resolution intact genome structure from single-cell Hi-C data

Area of Science:

• Genomics
• Computational Biology
• Epigenetics

Background:

• Single-cell Hi-C data is sparse and noisy, posing challenges for reconstructing 3D genome structures.
• Accurate 3D genome organization is crucial for understanding gene regulation and cellular function.

Purpose of the Study:

• To develop a computational method for reconstructing high-resolution, intact 3D genome structures from sparse single-cell Hi-C data.
• To validate the method's ability to capture conserved and cell-specific chromatin organization features.

Main Methods:

• Developed Single-Cell Chromosome Conformation Calculator (Si-C) based on Bayesian inference.
• Applied Si-C to single-cell Hi-C data from haploid mouse ES cells at 100-kb and 10-kb resolutions.
• Utilized an average of 0.2 contact reads per divided bin for reliable structure reconstruction.

Main Results:

• Successfully reconstructed intact 3D genome structures at 100-kb and 10-kb resolutions.
• Inferred structures accurately reproduced known conserved chromatin organization features.
• Revealed cell-to-cell variations in domain structures and identified hyperfine structures like loops within domains.

Conclusions:

• Si-C enables efficient and reliable reconstruction of super-resolution 3D genome structures from sparse single-cell Hi-C data.
• The method facilitates visualization and investigation of genome-wide chromatin interactions at the single-cell level.
• Si-C demonstrates potential for advancing studies of chromatin organization and dynamics in individual cells.