Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Overview Of Cell Separation And Isolation

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes.

Nature genetics·2026

Same author

Developmentally dynamic, non-convergent transcriptomic profiles in CNV models for schizophrenia risk.

Molecular psychiatry·2026

Same author

Co-occurring clonal hematopoiesis exhibits strong selection and high leukemia risk.

Nature communications·2026

Same author

Multi-omics integration predicts the incidence of 17 diseases in the UK Biobank.

Nature communications·2026

Same author

<i>Trans</i>-eQTLs reveal the architecture of human gene regulatory networks.

medRxiv : the preprint server for health sciences·2026

Same author

CRISPR tiling deletion screens reveal functional enhancers and allelic compensation effects (ACE) on SIN3A transcription.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Oct 1, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data.

Bryce Rowland¹, Ruth Huh¹, Zoey Hou²

¹Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.

|March 8, 2022

Summary

We developed THUNDER, a new unsupervised method to accurately determine cell type proportions in bulk Hi-C data. This approach improves analysis of 3D genome organization by accounting for cellular heterogeneity.

More Related Videos

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Related Experiment Videos

Last Updated: Oct 1, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Bulk Hi-C data offers population-averaged 3D chromatin contact information.
Analyzing heterogeneous bulk samples requires controlling for varying cell type proportions.
Accurate cell type deconvolution is crucial for robust downstream Hi-C analyses.

Purpose of the Study:

To introduce THUNDER, a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data.
To evaluate THUNDER's accuracy and robustness against existing methods using simulations and real-world data.
To provide a tool for analyzing cell-type-specific chromatin interactions.

Main Methods:

Developed the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER).
Validated THUNDER using simulated data derived from single-cell Hi-C (scHi-C) datasets.
Applied THUNDER to Hi-C data from adult human cortex samples.

Main Results:

THUNDER demonstrated superior accuracy in estimating cell type proportions compared to reference-free methods (TOAST, NMF).
THUNDER showed greater robustness than reference-dependent methods like MuSiC.
Successfully applied THUNDER to identify cell-type-specific interactions in human cortex Hi-C data.

Conclusions:

THUNDER is an effective unsupervised tool for deconvolution of cell type composition in bulk Hi-C data.
The method facilitates more valid and powerful downstream analyses, such as differential chromatin organization studies.
THUNDER's estimated contact profiles offer a framework for exploring chromatin interactome cell-type-specificity.