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.

Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...

You might also read

Related Articles

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

Sort by

Same author

Retinoic acid drives cell fate specification, maturation and retinal regionality in human retinal organoids.

Nature communications·2026

Same author

Highly Accelerated Real-time Cine MRI Pulse Sequence for Cardiac Implantable Electronic Devices and Arrhythmias.

Radiology. Cardiothoracic imaging·2025

Same author

Evaluation of Wideband Pulse Sequences for Suppressing Image Artifacts in Children with Cardiac Implantable Electronic Devices.

Radiology. Cardiothoracic imaging·2025

Same author

Design, Syntheses, and Pharmacological Evaluations of Core Ring Expanded Fentanyl Analogues as Potential Counteracting Agents Against Fentanyl Induced Respiratory Depression.

Journal of medicinal chemistry·2025

Same author

BrainSTEM: A single-cell multiresolution fetal brain atlas reveals transcriptomic fidelity of human midbrain cultures.

Science advances·2025

Same author

Bridging the gap in cardiovascular magnetic resonance imaging artificial intelligence implementations: From ambitious goals to real-world progress using foundation models.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2025

Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

Published on: December 1, 2023

CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data.

Daniel Kim^1,2,3, Carissa Chen^1,2,3, Lijia Yu^1,2,4

¹Computational Systems Biology Unit, Children's Medical Research Institute, Faculty of Medicine and Health, University of Sydney, Westmead, NSW 2145, Australia.

Bioinformatics (Oxford, England)

|September 22, 2025

Summary

This summary is machine-generated.

CLUEY, a novel framework, uses biological knowledge to improve cell type clustering in single-cell omics. It guides optimal cluster numbers and enhances biological interpretation for scRNA-seq and multimodal data.

More Related Videos

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Related Experiment Videos

Last Updated: Jan 17, 2026

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

Author Spotlight: Investigating Cellular and Molecular Dynamics During Muscle Regeneration Using Cutting-Edge Single-Cell Technologies

Published on: December 1, 2023

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Clustering is crucial for single-cell omics data analysis, impacting downstream interpretations.
Current methods often yield variable cell type counts due to technical factors, leading to subjective annotation.
Discrepancies in clustering results hinder accurate biological interpretation.

Purpose of the Study:

To introduce CLUEY, a knowledge-guided framework for cell type detection and clustering in single-cell omics.
To address challenges in determining the optimal number of clusters and enhancing interpretability.
To provide a robust solution for analyzing unimodal and multimodal single-cell data.

Main Methods:

CLUEY integrates prior biological knowledge into the clustering process.
The framework guides the selection of the optimal number of clusters.
It is designed for both unimodal (scRNA-seq, scATAC-seq) and multimodal (CITE-seq, SHARE-seq) datasets.

Main Results:

CLUEY demonstrates effectiveness in producing biologically meaningful clustering outcomes.
The framework successfully integrates prior biological knowledge for improved analysis.
Application to diverse single-cell omics datasets validates its utility.

Conclusions:

CLUEY offers essential guidance for clustering single-cell omics data.
The knowledge-guided approach enhances the reliability of cell type identification.
CLUEY improves the biological interpretability of single-cell omics analyses.