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

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

Alpha-synuclein at the crossroads of host-virus interactions: immunological roles beyond the nervous system.

Journal of virology·2026
Same author

Distinct in vivo dynamics of donor-derived stem cell memory CAR T cells post-allogeneic HSCT relapse.

Cell·2026
Same author

Immunophenotypic skewing of B cells toward IgD⁻CD27⁻IgG⁺ subtype and metabolic attenuation in colorectal cancer.

Scientific reports·2026
Same author

Guidelines for T cell nomenclature.

Nature reviews. Immunology·2025
Same author

Single-Cell Atlas of Cardiac Endothelial Cell Heterogeneity in Pressure Overload.

Circulation research·2025
Same author

Immunosuppressive contribution of tumour-infiltrating B cells in human intrahepatic cholangiocarcinoma and their role in chemoimmunotherapy outcome.

Gut·2025
Same journal

A Modular High-Parameter Flow Cytometry Framework: Pre-Analytical Optimization and Validation for Clinical Research.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Quantitative Detection of Entotic Cell-In-Cell Structures Using Deformable Segmentation and Deep Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Comparison of Tissue Preparations to Identify and Phenotype T Cells in Human Colorectal Tumor Tissue.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Refractive Index-Correlated Pseudocoloring for Adaptive Color Fusion in Holotomographic Cytology.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Ensembling Unets for Rare Chromosomal Aberration Detection in Metaphase Images, Uncertainty Quantification, and Ionizing Radiation Dose Estimation.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

OMIP-121: Immune Phenotyping of Canine Peripheral Leukocytes by Mass Cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 2026

Quality-Controlled Sputum Analysis by Flow Cytometry
07:22

Quality-Controlled Sputum Analysis by Flow Cytometry

Published on: August 9, 2021

Data analysis in flow cytometry: the future just started.

Enrico Lugli1, Mario Roederer, Andrea Cossarizza

  • 1Immuno Technology Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland 20892, USA. luglie@mail.nih.gov

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|June 29, 2010
PubMed
Summary
This summary is machine-generated.

Recent advances in flow cytometry (FC) hardware and reagents enable analysis of up to 20 parameters, generating complex data. This review covers bioinformatics tools for polychromatic flow cytometry to analyze cellular heterogeneity.

More Related Videos

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization
08:52

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Published on: August 16, 2015

Related Experiment Videos

Last Updated: Jun 11, 2026

Quality-Controlled Sputum Analysis by Flow Cytometry
07:22

Quality-Controlled Sputum Analysis by Flow Cytometry

Published on: August 9, 2021

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization
08:52

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Published on: August 16, 2015

Area of Science:

  • Immunology
  • Bioinformatics
  • Cell Biology

Background:

  • Flow cytometry (FC) technology has advanced significantly in the last decade, particularly in instrumentation and reagent development.
  • This progress allows for high-dimensional single-cell analysis, enabling the measurement of up to 20 parameters per cell.
  • The resulting complex datasets necessitate sophisticated analytical approaches.

Purpose of the Study:

  • To review recent developments in bioinformatics for polychromatic flow cytometry (PFC).
  • To discuss the application of supervised and unsupervised methods for analyzing complex FC data.
  • To propose future directions for analyzing cellular heterogeneity using PFC.

Main Methods:

  • Review of current literature on bioinformatics tools for PFC.
  • Analysis of supervised and unsupervised machine learning approaches applied to FC data.
  • Discussion of challenges and opportunities in PFC data analysis.

Main Results:

  • Significant progress in hardware and reagents has increased FC capabilities.
  • Complex datasets from PFC require advanced computational analysis.
  • Various bioinformatics tools, including supervised and unsupervised methods, are being developed.

Conclusions:

  • Bioinformatics plays a crucial role in interpreting complex PFC data.
  • Further development of analytical tools is needed to fully understand cellular heterogeneity.
  • The integration of advanced bioinformatics is essential for future FC applications.