Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data.

Area of Science:

• Genomics
• Computational Biology
• Structural Biology

Background:

• Chromosome conformation capture (3C) techniques provide population-averaged insights into genome organization.
• Single-cell Hi-C reveals significant cell-to-cell variability in 3D genome structures.
• Existing restraint-based modeling struggles with sparse and low-resolution single-cell contact data.

Purpose of the Study:

• To adapt the Bayesian Inferential Structure Determination (ISD) framework for inferring 3D chromosome structures from single-cell Hi-C data.
• To address challenges posed by data sparsity and low resolution in single-cell contact maps.
• To enable the computation of structural error bars and unbiased model parameter estimation.

Main Methods:

• Adaptation of the Bayesian Inferential Structure Determination (ISD) framework.
• Application of ISD to single-cell Hi-C contact data.
• Comparison of different chromatin fiber models and incorporation strategies for single-cell contacts.
• Extension of the approach to diploid chromosome data analysis.

Main Results:

• Successful inference of statistical ensembles of chromosome structures from single-cell data.
• Computation of structural error bars, reducing bias from parameter choices.
• Demonstration of the framework's ability to handle sparse and low-resolution contact data.
• Extension to analyze diploid genome organization in single cells.

Conclusions:

• The adapted ISD framework effectively models 3D genome organization from single-cell Hi-C data.
• This approach provides crucial insights into genome structural variability and accuracy.
• The method offers a robust tool for analyzing complex chromosomal structures in individual cells.