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

A Case of Concurrent Autoimmune Thyroiditis and Adrenal Insufficiency Following Nivolumab Therapy for Advanced Renal Cell Carcinoma.

Cureus·2026

Same author

Postmortem Evidence of Lymphatic Vessels Located at the Boundary of Central and Peripheral Nervous Systems in the Cervical Spine.

Cellular and molecular neurobiology·2026

Same author

The 2025 WAO Guidelines for the classification, diagnosis, and treatment of hereditary angioedema, with consideration of worldwide disparities.

The World Allergy Organization journal·2026

Same author

Long-Term Prophylaxis for Hereditary Angioedema: Real-World Treatment Patterns in Selected US Allergy Clinics.

International archives of allergy and immunology·2026

Same author

Emergency Department Prescribing and Opioid-Related Harms Among Adolescents in Alberta: 2010-2020.

Pediatrics·2026

Same author

Benchmarking component choices for unpaired single cell RNA and epigenomic integration.

Genome biology·2026

Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026

Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026

Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026

Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026

Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026

Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 31, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

MIMIC: an optimization method to identify cell type-specific marker panel for cell sorting.

Meng Zou¹, Zhana Duren², Qiuyue Yuan³

¹Department of Mathematics, Huazhong University of Science and Technology, Beijing 100190, China.

Briefings in Bioinformatics

|June 28, 2021

Summary

This summary is machine-generated.

We developed MIMIC, a novel computational method to identify optimal cell type-specific marker panels from complex multi-omics data. MIMIC enhances cell type discrimination and biological interpretation by selecting informative surface markers and transcription factors.

Keywords:

TFs cell type-specific marker dimension reduction hierarchical topology surface markers

More Related Videos

Author Spotlight: Importance of Single Cell Sorting in Isolating Purified Populations of Mesenchymal Stem Cells

Author Spotlight: Importance of Single Cell Sorting in Isolating Purified Populations of Mesenchymal Stem Cells

Published on: November 10, 2023

Isolation and Activation of Murine Lymphocytes

Isolation and Activation of Murine Lymphocytes

Published on: October 30, 2016

Related Experiment Videos

Last Updated: Oct 31, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Author Spotlight: Importance of Single Cell Sorting in Isolating Purified Populations of Mesenchymal Stem Cells

Author Spotlight: Importance of Single Cell Sorting in Isolating Purified Populations of Mesenchymal Stem Cells

Published on: November 10, 2023

Isolation and Activation of Murine Lymphocytes

Isolation and Activation of Murine Lymphocytes

Published on: October 30, 2016

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Multi-omics data offer insights into cell type discrimination and heterogeneity.
Selecting optimal marker panels from high-dimensional data for numerous cell types is challenging.

Purpose of the Study:

To introduce MIMIC (Mixed Integer programming Model to Identify Cell type-specific marker panel), a method for selecting optimal cell type-specific marker panels.
To maintain cell type hierarchy and maximize marker specificity.

Main Methods:

Developed MIMIC, a mixed-integer programming model.
Benchmarked MIMIC on mouse ENCODE RNA-seq data (29 tissues, 43 surface markers, 1345 transcription factors).
Compared MIMIC with standard single-gene approaches and dimensionality reduction techniques (MDS, t-SNE).

Main Results:

MIMIC selects biologically meaningful markers and is robust across accuracy criteria.
MIMIC outperforms standard methods in accuracy and biological interpretation.
Combined surface markers (SMs) and transcription factors (TFs) yield superior specificity.
Application to 641 RNA-seq samples (231 cell types) revealed cell type association network modularity.
Demonstrated scalability by selecting enhancer markers.

Conclusions:

MIMIC provides an effective approach for identifying cell type-specific marker panels from multi-omics data.
The method enhances understanding of cellular heterogeneity and biological networks.
MIMIC is a scalable and robust tool for biomarker discovery.