Physics-Based Modeling of Sparse Single-Cell Hi-C Uncovers Structural and Epigenetic Variability.

Area of Science:

• Genomics
• Computational Biology
• Biophysics

Background:

• Chromatin conformation capture technologies reveal genome's 3D organization and regulatory roles.
• Single-cell Hi-C (scHi-C) maps genome architecture at single-cell resolution, but data sparsity poses analytical challenges.

Purpose of the Study:

• To present a physics-based computational framework for reconstructing full 3D genome structures from sparse scHi-C data.
• To enable robust downstream analyses of genome architecture and epigenetic variations at single-cell resolution.

Main Methods:

• A physics-based framework combining polymer modeling and computational methods.
• Reconstruction of full 3D genome structures from sparse scHi-C data.
• Validation using artificial and experimental data, including comparison with independent Hi-C and polymer models.

Main Results:

• The framework successfully imputes missing contacts and recovers accurate 3D genome structures.
• Analysis of human HeLa-S3 scHi-C data identified distinct structural classes and variable single-cell topologically associated domains (TADs).
• Inferred 3D polymer models captured diverse epigenetic signatures, showing greater structural variability in active chromatin domains.

Conclusions:

• The study provides a mechanistic and interpretable framework for analyzing sparse scHi-C data.
• Leveraging polymer physics allows uncovering genome architecture and functional variability at single-cell resolution.
• The approach enhances understanding of genome folding dynamics and epigenetic regulation in single cells.