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Chromatin Immunoprecipitation- ChIP02:36

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The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions take up more dye, appearing darker, while the less-compact areas take up less dye and appear lighter. Based on the compaction level, chromatins are classified into two primary forms – euchromatin and heterochromatin.
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Related Experiment Video

Updated: Mar 10, 2026

Capturing Chromosome Conformation Across Length Scales
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scDIAGRAM: detecting chromatin compartments from individual single-cell Hi-C matrix without imputation or reference

Yongli Peng1, Yujing Deng2, Menghan Liu2

  • 1Beijing International Center for Mathematical Research (BICMR), Peking University, No.5 Yiheyuan Road, Haidian District, Beijing 100871, China.

Briefings in Bioinformatics
|March 8, 2026
PubMed
Summary

We developed scDIAGRAM, a novel method to accurately identify A/B compartments in single-cell Hi-C (scHi-C) data. This approach enhances understanding of 3D genome organization and gene regulation at the single-cell level.

Keywords:
A/B compartmentchromatin heterogeneitysingle-cell Hi-Cstatistical modeling

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Area of Science:

  • Genomics
  • Epigenetics
  • Computational Biology

Background:

  • Single-cell Hi-C (scHi-C) data reveals 3D genome organization but is sparse and noisy.
  • Accurate detection of A/B compartments is vital for understanding chromatin structure and gene regulation.
  • Existing methods face challenges with scHi-C data sparsity and noise.

Purpose of the Study:

  • To present scDIAGRAM, a data-driven computational method for annotating A/B compartments in single cells.
  • To overcome limitations of existing methods in analyzing sparse scHi-C data.
  • To enable robust analysis of chromatin compartments at single-cell resolution.

Main Methods:

  • scDIAGRAM utilizes direct statistical modeling and graph community detection on individual scHi-C matrices.
  • The method infers chromatin compartments without imputation or external reference features.
  • A/B labels are assigned using conventional genomic annotations.

Main Results:

  • scDIAGRAM demonstrated accuracy and robustness on simulated and real scHi-C datasets.
  • The method successfully identified A/B compartments in human cell lines.
  • Application to mouse and human datasets revealed compartmental shifts linked to transcriptional variation.

Conclusions:

  • scDIAGRAM provides a robust framework for analyzing A/B compartments in single-cell resolution.
  • This method offers new insights into the functional roles of chromatin compartments across diverse biological contexts.
  • scDIAGRAM advances the study of 3D genome organization and its impact on gene regulation.