Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
Flow Cytometry01:23

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

Lateral Cervical Ectopic Thyroid: A Seven-Step Diagnostic Algorithm and Dual-Axis Treatment Strategy Derived From Case Analysis and Literature Review.

Head & neck·2026
Same author

Crack Sealing in Concrete with Biogrout: Sustainable Approach to Enhancing Mechanical Strength and Water Resistance.

Materials (Basel, Switzerland)·2025
Same author

Engineered Platelet for <i>In Situ</i> Natural Killer Cell Activation to Inhibit Tumor Recurrence.

Nano letters·2024
Same author

A blood biomarker test for brain amyloid impacts the clinical evaluation of cognitive impairment.

Annals of clinical and translational neurology·2023
Same author

Independent study demonstrates amyloid probability score accurately indicates amyloid pathology.

Annals of clinical and translational neurology·2023
Same author

The Diagnosis of Urinary Tract Infections Using a Novel At-home Testing Protocol to Enhance Telemedicine: A Retrospective Analysis.

Urology·2023

Related Experiment Video

Updated: Jun 27, 2026

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Classification of cell subpopulations using multiple cellular parameters from high-content imaging studies.

Shuguang Huang1

  • 1Department of Biostatistics, Wyeth Pharmaceutical Research, Pearl River, NY 10965, USA. shu444@gmail.com

Journal of Biomolecular Screening
|November 28, 2008
PubMed
Summary

This study introduces a new method for analyzing cell populations. It deconvolutes mixtures to accurately quantify cell subpopulations using clustering and machine learning, proving effective for molecular research.

More Related Videos

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

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

Published on: October 25, 2018

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
09:57

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

Related Experiment Videos

Last Updated: Jun 27, 2026

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

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

Published on: October 25, 2018

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
09:57

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

Area of Science:

  • * Cellular biology and molecular research.
  • * Bioinformatics and computational biology.

Background:

  • * In vitro cell samples are rarely uniform, containing diverse subpopulations.
  • * Understanding cell heterogeneity is crucial for molecular research and experimental interpretation.
  • * Existing methods may struggle to accurately quantify these diverse cell subpopulations.

Purpose of the Study:

  • * To develop and validate a method for deconvoluting mixed cell populations.
  • * To quantitatively assess and classify distinct cell subpopulations within a sample.
  • * To enable more accurate analysis of cellular heterogeneity in biological experiments.

Main Methods:

  • * Utilizes simultaneous analysis of multiple cellular parameters for subpopulation classification.
  • * Employs an iterative K-means clustering algorithm to generate subpopulation 'fingerprints'.
  • * Applies support vector machines (SVMs) for classifying unknown cell samples based on these fingerprints.

Main Results:

  • * The proposed multivariate subpopulation analysis effectively deconvolutes cell mixtures.
  • * The method accurately quantifies the proportions of different cell subpopulations.
  • * Demonstrates statistical power and biological relevance in classifying cell samples.

Conclusions:

  • * Multivariate subpopulation analysis is a powerful tool for understanding cell heterogeneity.
  • * The combination of K-means clustering and SVMs provides a robust classification framework.
  • * This approach enhances the quantitative assessment of cell subpopulations in molecular studies.