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

You might also read

Related Articles

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

Sort by

Same author

Mapping the Conformational Landscape of DNA Minicircles Through Atomic Force Microscopy and Shape Space Analysis.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Extracting Mechanistic Information from an Open Data Set for a Pharma-Relevant Suzuki-Miyaura Cross-Coupling Reaction.

Organic process research & development·2026

Same author

Ontogeny independent expression of LPCAT2 in granuloma macrophages during experimental visceral leishmaniasis.

Communications biology·2026

Same author

ChIP happens: from biochemical origins to the modern omics toolbox for understanding steroid hormone receptors.

The Biochemical journal·2026

Same author

Chemometric analysis of ethoxylated polymer products using extracted MALDI-TOF-MS peak distribution features.

PloS one·2025

Same author

Subtropical southern Africa fire emissions of nitrogen oxides and ammonia obtained with satellite observations and GEOS-Chem.

Environmental science: atmospheres·2025

Same journal

Sub1 contributes to heart failure with preserved ejection fraction driven by aging in mice.

Nature communications·2026

Same journal

The BRCA1-A complex restricts replication fork reversal-dependent DNA repair in ATM deficient cells.

Nature communications·2026

Same journal

Signaling downstream of tumor-stroma interaction regulates mucinous colorectal adenocarcinoma apicobasal polarity.

Nature communications·2026

Same journal

Click-polymerized polyenamine membranes for efficient lithium extraction.

Nature communications·2026

Same journal

Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance.

Nature communications·2026

Same journal

Proton shuttling at electrochemical interfaces under alkaline hydrogen evolution.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 4, 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

The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition.

Laura Wiggins^1,2, Alice Lord², Killian L Murphy³

¹York Biomedical Research Institute, University of York, York, UK.

Nature Communications

|April 3, 2023

Summary

This summary is machine-generated.

CellPhe is a new toolkit for analyzing cell behavior in videos. It automatically identifies and characterizes different cell phenotypes, improving the accuracy of biological research.

More Related Videos

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Published on: December 16, 2017

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

Published on: February 16, 2018

Related Experiment Videos

Last Updated: Aug 4, 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

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Published on: December 16, 2017

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

Published on: February 16, 2018

Area of Science:

Cell biology
Bioinformatics
Computational imaging

Background:

Phenotypic heterogeneity is common in cell populations.
Quantitative, temporal analysis of single-cell morphology and dynamics is increasingly needed.
Existing methods may lack automation and robustness.

Purpose of the Study:

To present CellPhe, a pattern recognition toolkit for unbiased cell phenotyping.
To enable automated characterization of cellular phenotypes from time-lapse videos.
To improve data quality for downstream single-cell analysis.

Main Methods:

CellPhe imports tracking data from various segmentation and tracking algorithms.
It automates cell boundary error detection and removal.
Features are extracted from cell time series, with custom selection for discrimination.
Ensemble classification and clustering algorithms are used for phenotyping and subset characterization.

Main Results:

The toolkit provides automated, unbiased cell phenotyping.
It enhances data quality by removing segmentation and tracking errors.
Validated adaptability across different cell types and experimental conditions.
Demonstrated accurate prediction of cellular phenotype and characterization of heterogeneous subsets.

Conclusions:

CellPhe offers a robust solution for analyzing single-cell dynamics and heterogeneity.
The toolkit supports diverse imaging modalities and analysis requirements.
It facilitates more accurate and efficient biological research involving cell populations.