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Separation of Sister Chromatids

Separation of Sister Chromatids

At the transition from prophase to metaphase, there is a reduction in cohesion along the chromosomal arms, resulting in the resolution of sister chromatids. However, residual cohesin connections remain to hold the sister chromatids together until the transition from metaphase to anaphase. The residual connection prevents any premature separation of sister chromatids, blocking the risks of aneuploidy within the daughter cells.
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Separation of Sister Chromatids

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At the transition from prophase to metaphase, there is a reduction in cohesion along the chromosomal arms, resulting in the resolution of sister chromatids. However, residual cohesin connections remain to hold the sister chromatids together until the transition from metaphase to anaphase. The residual connection prevents any premature separation of sister chromatids, blocking the risks of aneuploidy within the daughter cells.
At the onset of anaphase, separase, a proteolytic enzyme, is...

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Related Experiment Video

Updated: May 8, 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

scHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data.

Qing Xie¹, Wang Meng², Shili Lin^3,4

¹Interdisciplinary Ph.D. Program in Biostatistics, The Ohio State University, Columbus, OH, 43210, USA.

BMC Bioinformatics

|May 21, 2025

Summary

Single cell Hi-C (scHi-C) data is sparse due to structural zeros and dropouts. Our scHiCSRS method distinguishes these zeros, improving data quality for better cell clustering and analysis of cell-to-cell variability.

Keywords:

Dropouts Neighborhoods Sampling zeros Sparsity Structural zeros

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Single cell Hi-C (scHi-C) enables studying cell-to-cell variability.
scHi-C data is sparse due to structural and sampling zeros (dropouts), hindering downstream analysis.
Distinguishing zero types is crucial for accurate interpretation of scHi-C data.

Purpose of the Study:

To develop a method for differentiating structural zeros from dropouts in scHi-C data.
To improve the quality of scHi-C data for enhanced downstream analyses.
To facilitate more accurate cell clustering and understanding of cell-to-cell variation.

Main Methods:

Proposed scHiCSRS, a self-representation smoothing method.
Utilized a Gaussian mixture model to identify structural zeros.
Incorporated spatial dependencies and information from similar single cells.

Main Results:

scHiCSRS effectively identifies structural zeros with high sensitivity.
Accurate imputation of dropout values in sampling zeros was achieved.
scHiCSRS-improved data yielded more accurate cell clustering compared to existing methods.

Conclusions:

scHiCSRS is a valuable tool for identifying structural zeros and imputing dropouts in scHi-C data.
Improved scHi-C data enhances downstream analyses, particularly for cell subtype clustering.
This method aids in understanding cell-to-cell variation through improved data quality.