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Heterochromatin

Heterochromatin

The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions that take up more dye are called heterochromatin. Heterochromatin is further classified into two forms – constitutive heterochromatin and facultative heterochromatin.
Constitutive heterochromatin: It is a highly compact region of chromatin that is mostly concentrated in the centromere and telomere. Unlike euchromatin, the amino acid at 9th...

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Updated: May 11, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Deciphering single-cell 3D chromatin structure using scCTG.

Ran Jiang¹, Yue Xue¹, Yanyi Huang^1,2,3

¹Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.

The Journal of Chemical Physics

|December 24, 2024

Summary

A new algorithm, scCTG, improves imputation for sparse single-cell Hi-C data. This method enhances the accuracy of chromatin structure analysis, offering deeper insights into gene expression relationships at the single-cell level.

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Last Updated: May 11, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

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Published on: May 6, 2010

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

Area of Science:

Genomics
Molecular Biology
Computational Biology

Background:

Sequencing-based Hi-C technology maps chromatin 3D structure.
Single-cell Hi-C enables cell-to-cell variation studies but yields sparse data.
Imputation is crucial for sparse single-cell Hi-C data, but current methods have limitations.

Purpose of the Study:

To develop an improved imputation algorithm for single-cell Hi-C data.
To address limitations of existing methods, including accuracy, reproducibility, and computational speed.
To provide a robust tool for analyzing single-cell chromatin structure.

Main Methods:

Improved the CTG (Hi-C To Geometry) algorithm to create the single-cell CTG (scCTG) algorithm.
Combined convolution and diffusion processes to generate spatial distance matrices.
Applied scCTG to various single-cell chromatin structure data types.

Main Results:

scCTG demonstrates high computational efficiency and robustness.
The algorithm shows strong correlation with physical spatial distances.
scCTG effectively identifies single-cell compartments and insulation strength.

Conclusions:

scCTG offers a significant advancement in imputing sparse single-cell Hi-C data.
The algorithm provides deeper insights into chromatin structure-gene expression relationships at the single-cell level.
scCTG is a valuable tool for single-cell epigenomics research.