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Updated: Jun 22, 2025

An Efficient Method for Quantitative, Single-cell Analysis of Chromatin Modification and Nuclear Architecture in Whole-mount Ovules in Arabidopsis

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Published on: June 19, 2014

FISHnet: Detecting chromatin domains in single-cell sequential Oligopaints imaging data.

Rohan Patel^1,2,3, Kenneth Pham^1,2,3, Harshini Chandrashekar^1,2,3

¹Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.

Biorxiv : the Preprint Server for Biology

|July 1, 2024

Summary

We developed FISHnet, a new algorithm for analyzing genome folding. This method identifies cell type-specific chromatin domains and boundaries from DNA FISH data, advancing our understanding of genome organization.

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Published on: April 11, 2014

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Higher-order genome folding is crucial for gene regulation.
Sequential Oligopaints DNA FISH enables single-allele resolution of genome architecture.
Existing algorithms are insufficient for analyzing complex 3D genome data from this technique.

Purpose of the Study:

To develop a novel computational method for analyzing sequential Oligopaints DNA FISH data.
To identify 3D genome features, specifically chromatin domains and boundaries.
To enable the study of cell type-specific genome folding patterns.

Main Methods:

Developed FISHnet, a graph theory-based algorithm.
Utilized optimization of network modularity to analyze pairwise distance matrices.
Applied the method to sequential Oligopaints DNA FISH data.

Main Results:

FISHnet successfully detects chromatin domains and boundaries in single-allele resolution.
The algorithm uncovers cell type-specific genome folding patterns.
Demonstrated the capability to identify distinct 3D genome features.

Conclusions:

FISHnet provides a robust method for analyzing higher-order genome folding using sequential Oligopaints DNA FISH.
This approach facilitates the investigation of cell type-specific genome organization.
Enables future research into the functional implications of single-cell folding variation.