SCW: building the whole-genome 3D structures based on extremely sparse single-cell Hi-C data.

Area of Science:

• Genomics
• Computational Biology
• Structural Biology

Background:

• Understanding cell-to-cell variability requires single-cell 3D genome structure analysis.
• Reconstructing whole-genome 3D structures from sparse single-cell Hi-C data presents significant computational challenges.

Purpose of the Study:

• To develop a robust computational method for high-resolution 3D genome structure reconstruction from extremely sparse single-cell Hi-C data.
• To evaluate the performance and accuracy of the developed method against existing tools and experimental data.

Main Methods:

• Development of the SCW (single-cell whole-genome) computational method.
• Reconstruction of 3D genome structures using extremely sparse single-cell Hi-C data (binary matrices).
• Validation through comparison with FISH, bulk Hi-C, gene expression data, and state-of-the-art tools (Nuc_dynamics, Hickit, Tensor-FLAMINGO).

Main Results:

• SCW successfully reconstructs high-resolution 3D genome structures from highly sparse single-cell Hi-C data.
• SCW demonstrates superior robustness, outperforming Nuc_dynamics on binary Hi-C matrices.
• Achieved a Pearson Correlation of 0.63 with FISH data, surpassing Hickit, and showed improved performance over Tensor-FLAMINGO, especially at 20 Kbp resolution.
• Maintained both intra- and inter-chromosomal contact patterns consistent with gene expression data.

Conclusions:

• SCW enables precise whole-genome 3D reconstruction from extremely sparse single-cell Hi-C data.
• SCW exhibits enhanced structural accuracy and robust maintenance of chromosomal contacts compared to existing methods.
• The method's versatility is confirmed across various cell types.