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

Co-Designing an Easy-Read Adult Social Care Outcomes Measure for Older People: Approach to and Reflections on Involving People Living With Dementia and Their Supporters.

Health expectations : an international journal of public participation in health care and health policy·2026
Same author

Gold decorated bismuth sulfide nanorods for enhanced computed tomography imaging with in vitro and in vivo validation.

Discover nano·2026
Same author

Serum proteomic atlas reveals distinct molecular signatures of lupus nephritis activity, chronicity, and treatment response.

bioRxiv : the preprint server for biology·2026
Same author

An efficient approach to study ANA⁺ B cells in autoimmune diseases integrating flow cytometry with single-cell analysis.

Molecular medicine (Cambridge, Mass.)·2026
Same author

Six-Month Outcomes in Children With COVID-19 or Multisystem Inflammatory Syndrome in Children.

Hospital pediatrics·2026
Same author

Spatially Distinct Macrophage Subsets Drive Myofibroblast Heterogeneity and Maladaptive Fibrosis in Lupus Nephritis.

bioRxiv : the preprint server for biology·2026
Same journal

Pregnancy-induced tissue-resident memory-like T cells contribute to tumor control in breast cancer.

Nature immunology·2026
Same journal

Mechanosensing by T cells promotes a tissue-resident memory transcriptional program.

Nature immunology·2026
Same journal

Editorial Expression of Concern: Recognition of the nonclassical MHC class I molecule H2-M3 by the receptor Ly49A regulates the licensing and activation of NK cells.

Nature immunology·2026
Same journal

Inflammatory immune modulators of AML lung infiltration and respiratory failure.

Nature immunology·2026
Same journal

The neuroimmune system and cognition.

Nature immunology·2026
Same journal

Critical connections.

Nature immunology·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
14:45

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay

Published on: September 16, 2012

A model for harmonizing flow cytometry in clinical trials.

Holden T Maecker1, J Philip McCoy,

  • 1Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, California, USA. maecker@stanford.edu

Nature Immunology
|October 21, 2010
PubMed
Summary
This summary is machine-generated.

Flow cytometry is complex for clinical trials. Using a central laboratory can simplify sample handling, instrument setup, and data analysis for immunological monitoring.

More Related Videos

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells
06:22

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
10:20

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

Related Experiment Videos

Last Updated: Jun 7, 2026

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
14:45

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay

Published on: September 16, 2012

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells
06:22

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
10:20

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

Area of Science:

  • Immunology
  • Clinical Trials
  • Laboratory Medicine

Background:

  • Flow cytometry is a powerful tool for monitoring immunological parameters.
  • Challenges in sample handling, instrument setup, and data analysis hinder its widespread clinical trial application.
  • Standardization across multiple sites is difficult to achieve.

Purpose of the Study:

  • To address the complexities of flow cytometry in clinical trials.
  • To evaluate the effectiveness of a central laboratory approach for immunological monitoring.
  • To propose a solution for consistent and reliable flow cytometry data in clinical research.

Main Methods:

  • A novel approach utilizing a central laboratory for flow cytometry analysis was implemented.
  • Standardized protocols for sample handling, instrument calibration, and data acquisition were developed.
  • Quality control measures were established to ensure data integrity and reproducibility.

Main Results:

  • The central laboratory model successfully mitigated issues related to sample handling.
  • Standardized instrument setup and data analysis procedures reduced variability.
  • The approach facilitated consistent and reliable monitoring of immunological parameters.

Conclusions:

  • A central laboratory model offers a viable solution to overcome flow cytometry complexities in clinical trials.
  • This approach enhances the feasibility and reliability of immunological monitoring in research settings.
  • Implementing standardized central laboratory workflows can improve the quality of clinical trial data.